diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/submission.json b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/submission.json
new file mode 100644
index 0000000000..3bc0023e8c
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/submission.json
@@ -0,0 +1,10 @@
+{
+  "name": "SP4096 + Polar Express + MuonEq-R + Depth Recurrence + WD=0.105",
+  "val_bpb": 1.0923,
+  "bytes_total": 15694101,
+  "blurb": "On clarkkev PR #1218 SP4096 base: Polar Express NS 4-step, MuonEq-R, depth recurrence layers 3,4,5 (shared MLP weights), WD=0.105, MLR=0.022. 3-seed mean 1.0923 (1337=1.0927, 42=1.0917, 2025=1.0925). Clean — no SLOT, no TTT.",
+  "author": "Omri Gotlieb",
+  "github_id": "Omrigotlieb",
+  "date": "2026-04-04",
+  "seeds": {"1337": 1.0927, "42": 1.0917, "2025": 1.0925}
+}
diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt.py b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt.py
new file mode 100644
index 0000000000..aef795b4ac
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt.py
@@ -0,0 +1,1243 @@
+import copy,glob,io,lzma,math,os,random,subprocess,sys,time,uuid
+from pathlib import Path
+import numpy as np
+import sentencepiece as spm
+import torch,torch.distributed as dist,torch.nn.functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch import Tensor, nn
+from flash_attn_interface import flash_attn_func as flash_attn_3_func
+_E=os.environ.get
+class Hyperparameters():
+    data_dir=_E('DATA_DIR','./data/')
+    seed=int(_E('SEED',1337))
+    run_id=_E("RUN_ID",str(uuid.uuid4()))
+    iterations=int(_E('ITERATIONS',20000))
+    warmdown_frac=float(_E('WARMDOWN_FRAC',0.667))
+    warmup_steps=int(_E('WARMUP_STEPS',20))
+    train_batch_tokens=int(_E('TRAIN_BATCH_TOKENS',2048*48*8))
+    train_seq_len=int(_E('TRAIN_SEQ_LEN',2048))
+    eval_seq_len=int(_E('EVAL_SEQ_LEN',2048))
+    max_wallclock_seconds=float(_E('MAX_WALLCLOCK_SECONDS',600.0))
+    train_log_every=int(_E('TRAIN_LOG_EVERY',500))
+    val_batch_tokens=int(_E('VAL_BATCH_TOKENS',2048*32*8))
+    val_loss_every=int(_E('VAL_LOSS_EVERY',4000))
+    sliding_window_enabled=bool(int(_E('SLIDING_WINDOW_ENABLED','1')))
+    vocab_size=int(_E('VOCAB_SIZE',4096))
+    num_layers=int(_E('NUM_LAYERS',11))
+    xsa_last_n=int(_E('XSA_LAST_N',11))
+    num_kv_heads=int(_E('NUM_KV_HEADS',4))
+    model_dim=int(_E('MODEL_DIM',512))
+    embedding_dim=int(_E('EMBEDDING_DIM',512))
+    num_heads=int(_E('NUM_HEADS',8))
+    mlp_mult=float(_E('MLP_MULT',4.0))
+    skip_gates_enabled=bool(int(_E('SKIP_GATES_ENABLED','1')))
+    tie_embeddings=bool(int(_E('TIE_EMBEDDINGS','1')))
+    logit_softcap=float(_E('LOGIT_SOFTCAP',30.0))
+    rope_base=float(_E('ROPE_BASE',10000.0))
+    rope_dims=int(_E('ROPE_DIMS',16))
+    rope_train_seq_len=int(_E('ROPE_TRAIN_SEQ_LEN',2048))
+    ln_scale=bool(int(_E('LN_SCALE','1')))
+    ve_enabled=bool(int(_E('VE_ENABLED','1')))
+    ve_dim=int(_E('VE_DIM',128))
+    ve_layers=_E('VE_LAYERS','9,10')
+    qk_gain_init=float(_E('QK_GAIN_INIT',4.0))
+    min_lr=float(_E('MIN_LR',0.0))
+    embed_lr=float(_E('EMBED_LR',0.6))
+    head_lr=float(_E('HEAD_LR',0.008))
+    tied_embed_lr=float(_E('TIED_EMBED_LR',0.03))
+    tied_embed_init_std=float(_E('TIED_EMBED_INIT_STD',0.005))
+    matrix_lr=float(_E('MATRIX_LR',0.02))
+    scalar_lr=float(_E('SCALAR_LR',0.02))
+    muon_momentum=float(_E('MUON_MOMENTUM',0.99))
+    muon_backend_steps=int(_E('MUON_BACKEND_STEPS',4))
+    muon_momentum_warmup_start=float(_E('MUON_MOMENTUM_WARMUP_START',0.92))
+    muon_momentum_warmup_steps=int(_E('MUON_MOMENTUM_WARMUP_STEPS',1500))
+    beta1=float(_E('BETA1',0.9))
+    beta2=float(_E('BETA2',0.95))
+    adam_eps=float(_E('ADAM_EPS',1e-8))
+    grad_clip_norm=float(_E('GRAD_CLIP_NORM',0.3))
+    eval_stride=int(_E('EVAL_STRIDE',64))
+    muon_beta2=float(_E('MUON_BETA2',0.95))
+    adam_wd=float(_E('ADAM_WD',0.02))
+    muon_wd=float(_E('MUON_WD',0.090))
+    embed_wd=float(_E('EMBED_WD',0.090))
+    ema_decay=float(_E('EMA_DECAY',0.997))
+    recur_layers=_E('RECUR_LAYERS','')
+    recur_start_step=int(_E('RECUR_START_STEP',3000))
+    slot_enabled=bool(int(_E('SLOT_ENABLED','0')))
+    slot_steps=int(_E('SLOT_STEPS',8))
+    slot_lr=float(_E('SLOT_LR',0.005))
+    compressor=_E('COMPRESSOR','brotli')
+    gptq_enabled=bool(int(_E('GPTQ_ENABLED','1')))
+    gptq_calibration_batches=int(_E('GPTQ_CALIBRATION_BATCHES',64))
+    gptq_reserve_seconds=float(_E('GPTQ_RESERVE_SECONDS',10.0))
+    distributed="RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank=int(_E("RANK","0"))
+    world_size=int(_E("WORLD_SIZE","1"))
+    local_rank=int(_E("LOCAL_RANK","0"))
+    is_main_process=rank==0
+    grad_accum_steps=8//world_size
+    datasets_dir=os.path.join(data_dir,'datasets',f'fineweb10B_sp{vocab_size}')
+    train_files=os.path.join(datasets_dir,'fineweb_train_*.bin')
+    val_files=os.path.join(datasets_dir,'fineweb_val_*.bin')
+    tokenizer_path=os.path.join(data_dir,'tokenizers',f'fineweb_{vocab_size}_bpe.model')
+    logfile=f"logs/{run_id}.txt"
+    model_path="final_model.pt"
+    quantized_model_path="final_model.int6.ptz"
+_logger_hparams=None
+def set_logging_hparams(h):
+    global _logger_hparams
+    _logger_hparams=h
+def log(msg,console=True):
+    if _logger_hparams is None:
+        print(msg)
+    if _logger_hparams.is_main_process:
+        if console:
+            print(msg)
+        if _logger_hparams.logfile is not None:
+            with open(_logger_hparams.logfile,"a",encoding="utf-8") as f:
+                print(msg,file=f)
+class ValidationData:
+    def __init__(self,h,device):
+        if not h.tokenizer_path.endswith(".model"):
+            raise ValueError(f"Script only setup for SentencePiece .model file: {h.tokenizer_path}")
+        self.sp=spm.SentencePieceProcessor(model_file=h.tokenizer_path)
+        if int(self.sp.vocab_size())!=h.vocab_size:
+            raise ValueError(f"VOCAB_SIZE={h.vocab_size} does not match tokenizer vocab_size={int(self.sp.vocab_size())}")
+        self.val_tokens=_load_val_tokens(h.val_files,h.eval_seq_len)
+        self.base_bytes_lut,self.has_leading_space_lut,self.is_boundary_token_lut=_build_sp_luts(self.sp,h.vocab_size,device)
+def _build_sp_luts(sp,vocab_size,device):
+    sv=int(sp.vocab_size())
+    assert sp.piece_to_id("\u2581")!=sp.unk_id(),"Tokenizer must have \u2581 as its own token"
+    ts=max(sv,vocab_size)
+    bb=np.zeros((ts,),dtype=np.int16)
+    hl=np.zeros((ts,),dtype=np.bool_)
+    ib=np.ones((ts,),dtype=np.bool_)
+    for tid in range(sv):
+        if sp.is_control(tid) or sp.is_unknown(tid) or sp.is_unused(tid):
+            continue
+        ib[tid]=False
+        if sp.is_byte(tid):
+            bb[tid]=1
+            continue
+        piece=sp.id_to_piece(tid)
+        if piece.startswith("\u2581"):
+            hl[tid]=True
+            piece=piece[1:]
+        bb[tid]=len(piece.encode("utf-8"))
+    return (torch.tensor(bb,dtype=torch.int16,device=device),
+            torch.tensor(hl,dtype=torch.bool,device=device),
+            torch.tensor(ib,dtype=torch.bool,device=device))
+def _load_val_tokens(pattern,seq_len):
+    files=[Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    tokens=torch.cat([_load_shard(f) for f in files]).contiguous()
+    usable=((tokens.numel()-1)//seq_len)*seq_len
+    if usable<=0:
+        raise ValueError(f"Validation split too short for seq_len={seq_len}")
+    return tokens[:usable+1]
+def _load_shard(file):
+    hb=256*np.dtype("<i4").itemsize
+    header=np.fromfile(file,dtype="<i4",count=256)
+    if header.size!=256 or int(header[0])!=20240520 or int(header[1])!=1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    nt=int(header[2])
+    if file.stat().st_size!=hb+nt*np.dtype("<u2").itemsize:
+        raise ValueError(f"Shard size mismatch for {file}")
+    t=np.fromfile(file,dtype="<u2",count=nt,offset=hb)
+    if t.size!=nt:
+        raise ValueError(f"Short read for {file}")
+    return torch.from_numpy(t.astype(np.uint16,copy=False))
+_SHB=256*np.dtype("<i4").itemsize
+_SNC:dict[str,int]={}
+_MMC:dict[str,np.memmap]={}
+def _read_nt(file):
+    k=str(file)
+    c=_SNC.get(k)
+    if c is not None:
+        return c
+    header=np.fromfile(file,dtype="<i4",count=256)
+    if header.size!=256 or int(header[0])!=20240520 or int(header[1])!=1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    n=int(header[2])
+    _SNC[k]=n
+    return n
+def _get_mm(file):
+    k=str(file)
+    mm=_MMC.get(k)
+    if mm is not None:
+        return mm
+    n=_read_nt(file)
+    mm=np.memmap(file,mode="r",dtype="<u2",offset=_SHB,shape=(n,))
+    _MMC[k]=mm
+    return mm
+class DistributedTokenLoader:
+    def __init__(self,pattern,rank,world_size,device):
+        self.rank=rank
+        self.world_size=world_size
+        self.device=device
+        self.files=[Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self._nt=np.array([_read_nt(f) for f in self.files],dtype=np.int64)
+        seed=0
+        for f in self.files:
+            for b in str(f).encode():
+                seed=((seed^b)*1099511628211)&0xFFFFFFFFFFFFFFFF
+        self._rng=np.random.Generator(np.random.PCG64(seed))
+        self._cfg=None
+        self._esh=None
+        self._bbc=None
+        n=len(self.files)
+        self._cp=np.zeros(n,dtype=np.int64)
+        self._cbc=np.zeros(n,dtype=np.int64)
+        self._cn=np.zeros(n,dtype=np.int64)
+        self._cs=np.zeros(n,dtype=np.int64)
+        self._cst=np.ones(n,dtype=np.int64)
+        self._ci=np.zeros(n,dtype=np.bool_)
+        self._bb=0
+    def _coprime(self,n):
+        if n<=1:
+            return 1
+        while True:
+            s=int(self._rng.integers(1,n))
+            if math.gcd(s,n)==1:
+                return s
+    def _reset(self,si,sl):
+        nt=int(self._nt[si])
+        mp=min(sl-1,max(0,nt-sl-1))
+        ph=int(self._rng.integers(mp+1)) if mp>0 else 0
+        bc=(nt-1-ph)//sl
+        self._cp[si]=ph
+        self._cbc[si]=bc
+        self._cn[si]=0
+        self._cs[si]=int(self._rng.integers(bc)) if bc>1 else 0
+        self._cst[si]=self._coprime(bc)
+        self._ci[si]=True
+    def _ensure(self,si,sl):
+        if not self._ci[si] or self._cn[si]>=self._cbc[si]:
+            self._reset(si,sl)
+    def _take(self,si,sl,count,out):
+        rem=count
+        while rem>0:
+            self._ensure(si,sl)
+            bc=int(self._cbc[si])
+            ni=int(self._cn[si])
+            take=min(rem,bc-ni)
+            ph=int(self._cp[si])
+            st=int(self._cs[si])
+            stride=int(self._cst[si])
+            for j in range(take):
+                bi=(st+(ni+j)*stride)%bc
+                out.append((si,ph+bi*sl))
+            self._cn[si]=ni+take
+            rem-=take
+    def _init_pipe(self,gt,sl,gas):
+        lt=gt//(self.world_size*gas)
+        ns=lt//sl
+        gns=ns*self.world_size
+        self._cfg=(lt,sl,ns,gns)
+        bbc=(self._nt-1)//sl
+        el=bbc>0
+        self._esh=np.nonzero(el)[0].astype(np.int64)
+        self._bbc=bbc[self._esh].astype(np.int64)
+    def _sample_gw(self):
+        assert self._cfg is not None and self._esh is not None
+        _,sl,_,gns=self._cfg
+        ec=int(self._esh.size)
+        prog=min(self._bb/1800.0,1.0)
+        rem=np.empty(ec,dtype=np.float64)
+        for i,si in enumerate(self._esh.tolist()):
+            if self._ci[si]:
+                r=int(self._cbc[si])-int(self._cn[si])
+                rem[i]=float(max(r,1))
+            else:
+                rem[i]=float(self._bbc[i])
+        alpha=0.90-0.40*prog
+        w=np.power(rem,alpha)
+        ws=float(w.sum())
+        if not np.isfinite(ws) or ws<=0.0:
+            w=np.ones(ec,dtype=np.float64)
+            ws=float(w.sum())
+        pr=w/ws
+        lo=min(max(8,self.world_size),ec,gns)
+        hi=min(max(32,self.world_size*8),ec,gns)
+        mix=max(1,min(int(round(lo+prog*(hi-lo))),ec,gns))
+        cp=self._rng.choice(ec,size=mix,replace=False,p=pr)
+        cs=self._esh[cp]
+        cpr=pr[cp].copy()
+        cpr/=cpr.sum()
+        counts=np.ones(mix,dtype=np.int64)
+        extra=gns-mix
+        if extra>0:
+            counts+=self._rng.multinomial(extra,cpr).astype(np.int64)
+        perm=self._rng.permutation(mix)
+        cs,counts=cs[perm],counts[perm]
+        bkts:list[list[tuple[int,int]]]=[]
+        for si,cnt in zip(cs.tolist(),counts.tolist()):
+            b:list[tuple[int,int]]=[]
+            self._take(int(si),sl,int(cnt),b)
+            if b:
+                if len(b)>1:
+                    bp=self._rng.permutation(len(b))
+                    b=[b[int(k)] for k in bp.tolist()]
+                bkts.append(b)
+        wins:list[tuple[int,int]]=[]
+        active=[i for i,bk in enumerate(bkts) if bk]
+        while active:
+            order=self._rng.permutation(len(active))
+            na:list[int]=[]
+            for oi in order.tolist():
+                bi=active[oi]
+                if bkts[bi]:
+                    wins.append(bkts[bi].pop())
+                if bkts[bi]:
+                    na.append(bi)
+            active=na
+        return wins
+    def next_batch(self,gt,sl,gas):
+        if self._cfg is None:
+            self._init_pipe(gt,sl,gas)
+        _,_,ns,_=self._cfg
+        gw=self._sample_gw()
+        lw=gw[self.rank::self.world_size]
+        x=torch.empty((ns,sl),dtype=torch.int64)
+        y=torch.empty((ns,sl),dtype=torch.int64)
+        for slot,(si,pos) in enumerate(lw):
+            mm=_get_mm(self.files[si])
+            win=torch.as_tensor(np.array(mm[pos:pos+sl+1],dtype=np.int64))
+            x[slot]=win[:-1]
+            y[slot]=win[1:]
+        self._bb+=1
+        return x.to(self.device,non_blocking=True),y.to(self.device,non_blocking=True)
+class RMSNorm(nn.Module):
+    def __init__(self,eps=None):
+        super().__init__()
+        self.eps=eps
+    def forward(self,x):
+        return F.rms_norm(x,(x.size(-1),),eps=self.eps)
+class CastedLinear(nn.Linear):
+    def forward(self,x):
+        w=self.weight.to(x.dtype)
+        b=self.bias.to(x.dtype) if self.bias is not None else None
+        return F.linear(x,w,b)
+class Rotary(nn.Module):
+    def __init__(self,dim,base=10000.0,train_seq_len=1024,rope_dims=0):
+        super().__init__()
+        self.dim=dim
+        self.base=base
+        self.train_seq_len=train_seq_len
+        self.rope_dims=rope_dims if rope_dims>0 else dim
+        inv_freq=1.0/(base**(torch.arange(0,self.rope_dims,2,dtype=torch.float32)/self.rope_dims))
+        self.register_buffer("inv_freq",inv_freq,persistent=False)
+        self._sl=0
+        self._cc=None
+        self._sc=None
+    def forward(self,seq_len,device,dtype):
+        if self._cc is None or self._sc is None or self._sl!=seq_len or self._cc.device!=device:
+            rd=self.rope_dims
+            if seq_len>self.train_seq_len:
+                scale=seq_len/self.train_seq_len
+                nb=self.base*(scale**(rd/(rd-2)))
+                inv_freq=1.0/(nb**(torch.arange(0,rd,2,dtype=torch.float32,device=device)/rd))
+            else:
+                inv_freq=self.inv_freq.to(device)
+            t=torch.arange(seq_len,device=device,dtype=inv_freq.dtype)
+            freqs=torch.outer(t,inv_freq)
+            self._cc=freqs.cos()[None,:,None,:]
+            self._sc=freqs.sin()[None,:,None,:]
+            self._sl=seq_len
+        return self._cc.to(dtype=dtype),self._sc.to(dtype=dtype)
+def apply_rotary_emb(x,cos,sin,rope_dims=0):
+    if rope_dims>0 and rope_dims<x.size(-1):
+        xr,xp=x[...,:rope_dims],x[...,rope_dims:]
+        half=rope_dims//2
+        x1,x2=xr[...,:half],xr[...,half:]
+        xr=torch.cat((x1*cos+x2*sin,x1*(-sin)+x2*cos),dim=-1)
+        return torch.cat((xr,xp),dim=-1)
+    half=x.size(-1)//2
+    x1,x2=x[...,:half],x[...,half:]
+    return torch.cat((x1*cos+x2*sin,x1*(-sin)+x2*cos),dim=-1)
+class CausalSelfAttention(nn.Module):
+    def __init__(self,dim,num_heads,num_kv_heads,rope_base,qk_gain_init,train_seq_len):
+        super().__init__()
+        assert dim%num_heads==0 and num_heads%num_kv_heads==0
+        self.num_heads=num_heads
+        self.num_kv_heads=num_kv_heads
+        self.head_dim=dim//num_heads
+        assert self.head_dim%2==0
+        kv_dim=self.num_kv_heads*self.head_dim
+        self.c_q=CastedLinear(dim,dim,bias=False)
+        self.c_k=CastedLinear(dim,kv_dim,bias=False)
+        self.c_v=CastedLinear(dim,kv_dim,bias=False)
+        self.proj=CastedLinear(dim,dim,bias=False)
+        self.proj._zero_init=True
+        self.q_gain=nn.Parameter(torch.full((num_heads,),qk_gain_init,dtype=torch.float32))
+        self.rope_dims=0
+        self.rotary=Rotary(self.head_dim,base=rope_base,train_seq_len=train_seq_len)
+        self.use_xsa=False
+    def _xsa(self,y,v):
+        B,T,H,D=y.shape
+        Hkv=v.size(-2)
+        g=H//Hkv
+        yg=y.reshape(B,T,Hkv,g,D)
+        vn=F.normalize(v,dim=-1).unsqueeze(-2)
+        p=(yg*vn).sum(dim=-1,keepdim=True)*vn
+        return (yg-p).reshape(B,T,H,D)
+    def forward(self,x,v_embed=None):
+        bsz,sl,dim=x.shape
+        q=self.c_q(x).reshape(bsz,sl,self.num_heads,self.head_dim)
+        k=self.c_k(x).reshape(bsz,sl,self.num_kv_heads,self.head_dim)
+        v=self.c_v(x)
+        if v_embed is not None:
+            v=v+v_embed
+        v=v.reshape(bsz,sl,self.num_kv_heads,self.head_dim)
+        q=F.rms_norm(q,(q.size(-1),))
+        k=F.rms_norm(k,(k.size(-1),))
+        cos,sin=self.rotary(sl,x.device,q.dtype)
+        q=apply_rotary_emb(q,cos,sin,self.rope_dims)
+        k=apply_rotary_emb(k,cos,sin,self.rope_dims)
+        q=q*self.q_gain.to(dtype=q.dtype)[None,None,:,None]
+        y=flash_attn_3_func(q,k,v,causal=True)
+        if self.use_xsa:
+            y=self._xsa(y,v)
+        return self.proj(y.reshape(bsz,sl,dim))
+class ValueEmbedding(nn.Module):
+    def __init__(self,vocab_size,ve_dim,model_dim):
+        super().__init__()
+        self.embed=nn.Embedding(vocab_size,ve_dim)
+        nn.init.normal_(self.embed.weight,std=0.01)
+        self.proj=CastedLinear(ve_dim,model_dim,bias=False) if ve_dim!=model_dim else None
+        if self.proj is not None:
+            nn.init.zeros_(self.proj.weight)
+        self.scale=nn.Parameter(torch.tensor(0.1,dtype=torch.float32))
+    def forward(self,token_ids):
+        h=self.embed(token_ids)
+        if self.proj is not None:
+            h=self.proj(h)
+        return h*self.scale.to(dtype=h.dtype)
+class MLP(nn.Module):
+    def __init__(self,dim,mlp_mult):
+        super().__init__()
+        hidden=int(mlp_mult*dim)
+        self.fc=CastedLinear(dim,hidden,bias=False)
+        self.proj=CastedLinear(hidden,dim,bias=False)
+        self.proj._zero_init=True
+    def forward(self,x):
+        return self.proj(F.leaky_relu(self.fc(x),negative_slope=0.5).square())
+class Block(nn.Module):
+    def __init__(self,dim,num_heads,num_kv_heads,mlp_mult,rope_base,qk_gain_init,train_seq_len,layer_idx=0,ln_scale=False):
+        super().__init__()
+        self.attn_norm=RMSNorm()
+        self.mlp_norm=RMSNorm()
+        self.attn=CausalSelfAttention(dim,num_heads,num_kv_heads,rope_base,qk_gain_init,train_seq_len)
+        self.mlp=MLP(dim,mlp_mult)
+        self.attn_scale=nn.Parameter(torch.ones(dim,dtype=torch.float32))
+        self.mlp_scale=nn.Parameter(torch.ones(dim,dtype=torch.float32))
+        self.resid_mix=nn.Parameter(torch.stack((torch.ones(dim),torch.zeros(dim))).float())
+        self.ln_scale_factor=1.0/math.sqrt(layer_idx+1) if ln_scale else 1.0
+    def forward(self,x,x0,v_embed=None):
+        mix=self.resid_mix.to(dtype=x.dtype)
+        xi=mix[0][None,None,:]*x+mix[1][None,None,:]*x0
+        ao=self.attn(self.attn_norm(xi)*self.ln_scale_factor,v_embed=v_embed)
+        xo=xi+self.attn_scale.to(dtype=xi.dtype)[None,None,:]*ao
+        xo=xo+self.mlp_scale.to(dtype=xo.dtype)[None,None,:]*self.mlp(self.mlp_norm(xo)*self.ln_scale_factor)
+        return xo
+class GPT(nn.Module):
+    def __init__(self,h):
+        super().__init__()
+        self._ve_target_dim=h.num_kv_heads*(h.model_dim//h.num_heads)
+        assert h.logit_softcap>0.0
+        self.tie_embeddings=h.tie_embeddings
+        self.tied_embed_init_std=h.tied_embed_init_std
+        self.logit_softcap=h.logit_softcap
+        self._recur_set=set()
+        if h.recur_layers:
+            self._recur_set={int(x) for x in h.recur_layers.split(",") if x.strip()}
+        self._recur_active=bool(self._recur_set)
+        self.tok_emb=nn.Embedding(h.vocab_size,h.embedding_dim)
+        if h.embedding_dim!=h.model_dim:
+            self.embed_proj=CastedLinear(h.embedding_dim,h.model_dim,bias=False)
+            self.head_proj=CastedLinear(h.model_dim,h.embedding_dim,bias=False)
+        else:
+            self.embed_proj=None
+            self.head_proj=None
+        self.num_encoder_layers=h.num_layers//2
+        self.num_decoder_layers=h.num_layers-self.num_encoder_layers
+        self.num_skip_weights=min(self.num_encoder_layers,self.num_decoder_layers)
+        self.skip_weights=nn.Parameter(torch.ones(self.num_skip_weights,h.model_dim,dtype=torch.float32))
+        self.skip_gates=nn.Parameter(torch.zeros(self.num_skip_weights,h.model_dim,dtype=torch.float32)) if h.skip_gates_enabled else None
+        self.blocks=nn.ModuleList([Block(h.model_dim,h.num_heads,h.num_kv_heads,h.mlp_mult,h.rope_base,h.qk_gain_init,h.train_seq_len,layer_idx=i,ln_scale=h.ln_scale) for i in range(h.num_layers)])
+        if h.rope_dims>0:
+            hd=h.model_dim//h.num_heads
+            for block in self.blocks:
+                block.attn.rope_dims=h.rope_dims
+                block.attn.rotary=Rotary(hd,base=h.rope_base,train_seq_len=h.train_seq_len,rope_dims=h.rope_dims)
+        self.ve_layer_indices=[int(x) for x in h.ve_layers.split(",") if x.strip()] if h.ve_enabled else []
+        kv_dim=self._ve_target_dim
+        if self.ve_layer_indices:
+            self.ve_shared=ValueEmbedding(h.vocab_size,h.ve_dim,kv_dim)
+            self.ve_layer_scales=nn.ParameterList([nn.Parameter(torch.ones(1,dtype=torch.float32)) for _ in self.ve_layer_indices])
+        else:
+            self.ve_shared=None
+            self.ve_layer_scales=nn.ParameterList()
+        self.value_embeds=nn.ModuleList()
+        self.final_norm=RMSNorm()
+        self.lm_head=None if h.tie_embeddings else CastedLinear(h.embedding_dim,h.vocab_size,bias=False)
+        if self.lm_head is not None:
+            self.lm_head._zero_init=True
+        if h.xsa_last_n>0:
+            for i in range(max(0,h.num_layers-h.xsa_last_n),h.num_layers):
+                self.blocks[i].attn.use_xsa=True
+        self._init_weights()
+    def _init_weights(self):
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight,mean=0.0,std=self.tied_embed_init_std)
+        for name,module in self.named_modules():
+            if isinstance(module,nn.Linear):
+                if getattr(module,"_zero_init",False):
+                    nn.init.zeros_(module.weight)
+                elif module.weight.ndim==2 and module.weight.shape[0]>=64 and module.weight.shape[1]>=64:
+                    nn.init.orthogonal_(module.weight,gain=1.0)
+    def _get_ve(self,li,input_ids,ve_cache=None):
+        if self.ve_shared is None or li not in self.ve_layer_indices:
+            return None
+        if ve_cache is not None and 've' not in ve_cache:
+            ve_cache['ve']=self.ve_shared(input_ids)
+        vb=ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids)
+        vi=self.ve_layer_indices.index(li)
+        return vb*self.ve_layer_scales[vi].to(dtype=vb.dtype)
+    def forward_backbone(self,input_ids):
+        x=self.tok_emb(input_ids)
+        x=F.rms_norm(x,(x.size(-1),))
+        if self.embed_proj is not None:
+            x=self.embed_proj(x)
+        x0=x
+        skips=[]
+        vc={}
+        schedule=[]
+        rs=sorted(self._recur_set) if (self._recur_active and self._recur_set) else []
+        mr=max(rs) if rs else -1
+        for i in range(len(self.blocks)):
+            schedule.append(i)
+            if i==mr:
+                schedule.extend(rs)
+        seen=set()
+        for li in schedule:
+            first=li not in seen
+            seen.add(li)
+            enc=li<self.num_encoder_layers
+            if enc:
+                ve=self._get_ve(li,input_ids,vc)
+                x=self.blocks[li](x,x0,v_embed=ve)
+                if first:
+                    skips.append(x)
+            else:
+                di=li-self.num_encoder_layers
+                if first and skips:
+                    ss=self.skip_weights[di].to(dtype=x.dtype)[None,None,:]*skips.pop()
+                    if self.skip_gates is not None:
+                        g=torch.sigmoid(self.skip_gates[di].to(dtype=x.dtype))[None,None,:]
+                        x=torch.lerp(ss,x,g)
+                    else:
+                        x=x+ss
+                ve=self._get_ve(li,input_ids,vc)
+                x=self.blocks[li](x,x0,v_embed=ve)
+        return self.final_norm(x)
+    def _head_logits(self,h):
+        if self.head_proj is not None:
+            h=self.head_proj(h)
+        if self.tie_embeddings:
+            lo=F.linear(h,self.tok_emb.weight)
+        else:
+            lo=self.lm_head(h)
+        return self.logit_softcap*torch.tanh(lo/self.logit_softcap)
+    def forward_logits(self,input_ids):
+        return self._head_logits(self.forward_backbone(input_ids))
+    def forward(self,input_ids,target_ids):
+        logits=self.forward_logits(input_ids)
+        return F.cross_entropy(logits.reshape(-1,logits.size(-1)).float(),target_ids.reshape(-1),reduction="mean")
+def classify_param(name):
+    if "tok_emb" in name or "lm_head" in name:
+        return "embed"
+    if ".mlp." in name:
+        return "mlp"
+    if ".attn." in name or (".proj." in name and ".mlp." not in name):
+        return "attn"
+    return "other"
+_PE_COEFFS=[(8.156554524902461,-22.48329292557795,15.878769915207462),(4.042929935166739,-2.808917465908714,0.5000178451051316),(3.8916678022926607,-2.772484153217685,0.5060648178503393),(3.285753657755655,-2.3681294933425376,0.46449024233003106),(2.3465413258596377,-1.7097828382687081,0.42323551169305323)]
+@torch.compile
+def zeropower_via_newtonschulz5(G,steps=10,eps=1e-7):
+    X=G.bfloat16()
+    X/=X.norm()+eps
+    tr=G.size(0)>G.size(1)
+    if tr:
+        X=X.T
+    for a,b,c in _PE_COEFFS[:steps]:
+        A=X@X.T
+        B=b*A+c*A@A
+        X=a*X+B@X
+    return X.T if tr else X
+class Muon(torch.optim.Optimizer):
+    def __init__(self,params,lr,momentum,backend_steps,nesterov=True,weight_decay=0.0):
+        super().__init__(params,dict(lr=lr,momentum=momentum,backend_steps=backend_steps,nesterov=nesterov,weight_decay=weight_decay))
+    @torch.no_grad()
+    def step(self,closure=None):
+        loss=None
+        if closure is not None:
+            with torch.enable_grad():
+                loss=closure()
+        dd=dist.is_available() and dist.is_initialized()
+        ws=dist.get_world_size() if dd else 1
+        rk=dist.get_rank() if dd else 0
+        for group in self.param_groups:
+            params=group["params"]
+            if not params:
+                continue
+            lr=group["lr"]
+            mom=group["momentum"]
+            bs=group["backend_steps"]
+            nest=group["nesterov"]
+            tp=sum(int(p.numel()) for p in params)
+            uf=torch.zeros(tp,device=params[0].device,dtype=torch.bfloat16)
+            cur=0
+            for i,p in enumerate(params):
+                if i%ws==rk and p.grad is not None:
+                    g=p.grad
+                    st=self.state[p]
+                    if "momentum_buffer" not in st:
+                        st["momentum_buffer"]=torch.zeros_like(g)
+                    buf=st["momentum_buffer"]
+                    buf.mul_(mom).add_(g)
+                    if nest:
+                        g=g.add(buf,alpha=mom)
+                    g=g/(g.norm(dim=-1,keepdim=True).clamp_min(1e-7))
+                    g=zeropower_via_newtonschulz5(g,steps=bs)
+                    g*=max(1,g.size(0)/g.size(1))**0.5
+                    uf[cur:cur+p.numel()]=g.reshape(-1)
+                cur+=p.numel()
+            if dd:
+                dist.all_reduce(uf,op=dist.ReduceOp.SUM)
+            wd=group.get("weight_decay",0.0)
+            cur=0
+            for p in params:
+                if wd>0.0:
+                    p.data.mul_(1.0-lr*wd)
+                g=uf[cur:cur+p.numel()].view_as(p).to(dtype=p.dtype)
+                p.add_(g,alpha=-lr)
+                cur+=p.numel()
+        return loss
+CTRL_PAT=tuple(p for p in _E("CONTROL_TENSOR_NAME_PATTERNS","attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,skip_gates,ve_layer_scales,ve_shared.scale").split(",") if p)
+_I8SD=torch.float16
+_I8CP=99.99984
+_I8CQ=_I8CP/100.0
+def _qft(t):
+    t32=t.float()
+    if t32.ndim==2:
+        ca=torch.quantile(t32.abs(),_I8CQ,dim=1) if t32.numel() else torch.empty((t32.shape[0],),dtype=torch.float32)
+        cl=torch.maximum(torch.minimum(t32,ca[:,None]),-ca[:,None])
+        s=(ca/127.0).clamp_min(1.0/127.0)
+        q=torch.clamp(torch.round(cl/s[:,None]),-127,127).to(torch.int8).contiguous()
+        return q,s.to(dtype=_I8SD).contiguous()
+    ca=float(torch.quantile(t32.abs().flatten(),_I8CQ).item()) if t32.numel() else 0.0
+    s=torch.tensor(ca/127.0 if ca>0 else 1.0,dtype=torch.float32)
+    q=torch.clamp(torch.round(torch.clamp(t32,-ca,ca)/s),-127,127).to(torch.int8).contiguous()
+    return q,s
+def restore_fp32_params(model):
+    for m in model.modules():
+        if isinstance(m,CastedLinear):
+            m.float()
+    for name,param in model.named_parameters():
+        if (param.ndim<2 or any(p in name for p in CTRL_PAT)) and param.dtype!=torch.float32:
+            param.data=param.data.float()
+def _qi6(t,cr=31):
+    t32=t.float()
+    if t32.ndim==2:
+        bq,bs,be=None,None,float('inf')
+        for pct in [0.9990,0.9995,0.9999,0.99999,1.0]:
+            rc=torch.quantile(t32.abs(),pct,dim=1) if pct<1.0 else t32.abs().amax(dim=1)
+            s=(rc/cr).clamp_min(1.0/cr).to(torch.float16)
+            q=torch.clamp(torch.round(t32/s.float()[:,None]),-cr,cr).to(torch.int8)
+            err=(t32-q.float()*s.float()[:,None]).pow(2).mean().item()
+            if err<be:
+                bq,bs,be=q,s,err
+        return bq,bs
+    am=t32.abs().max().item()
+    s=torch.tensor(am/cr if am>0 else 1.0,dtype=torch.float16)
+    q=torch.clamp(torch.round(t32/s.float()),-cr,cr).to(torch.int8)
+    return q,s
+def _collect_H(model,loader,h,device,ncb=64):
+    H={}
+    hooks=[]
+    def mk(name):
+        def fn(mod,inp,out):
+            x=inp[0].detach().float()
+            if x.ndim==3:
+                x=x.reshape(-1,x.shape[-1])
+            if name not in H:
+                H[name]=torch.zeros(x.shape[1],x.shape[1],dtype=torch.float32,device=device)
+            H[name].addmm_(x.T,x)
+        return fn
+    for name,mod in model.named_modules():
+        if isinstance(mod,CastedLinear) and mod.weight.numel()>65536:
+            cat=classify_param(name+".weight")
+            if cat in ("mlp","attn"):
+                hooks.append(mod.register_forward_hook(mk(name+".weight")))
+    model.eval()
+    with torch.no_grad():
+        for i in range(ncb):
+            x,y=loader.next_batch(h.train_batch_tokens,h.train_seq_len,h.grad_accum_steps)
+            model.forward_logits(x)
+    for hk in hooks:
+        hk.remove()
+    for name in H:
+        H[name]=H[name].cpu()/ncb
+    return H
+def _gptq_qw(w,Hm,cr=31,bs=128):
+    W=w.float().clone()
+    rows,cols=W.shape
+    Hm=Hm.float().clone()
+    dead=torch.diag(Hm)==0
+    Hm[dead,dead]=1
+    damp=0.01*Hm.diag().mean()
+    Hm.diagonal().add_(damp)
+    perm=torch.argsort(Hm.diag(),descending=True)
+    invperm=torch.argsort(perm)
+    Wp=W[:,perm].clone()
+    Wp[:,dead[perm]]=0
+    Hm=Hm[perm][:,perm]
+    try:
+        Hi=torch.cholesky_inverse(torch.linalg.cholesky(Hm))
+        Hi=torch.linalg.cholesky(Hi,upper=True)
+    except torch.linalg.LinAlgError:
+        return _qi6(W,cr)
+    bq,bsc,be=None,None,float('inf')
+    for pct in [0.9990,0.9995,0.9999,0.99999,1.0]:
+        rc=torch.quantile(W.abs(),pct,dim=1) if pct<1.0 else W.abs().amax(dim=1)
+        s=(rc/cr).clamp_min(1.0/cr).to(torch.float16)
+        sf=s.float()
+        Q=torch.zeros(rows,cols,dtype=torch.int8)
+        Ww=Wp.clone()
+        for i1 in range(0,cols,bs):
+            i2=min(i1+bs,cols)
+            Wb=Ww[:,i1:i2].clone()
+            Hb=Hi[i1:i2,i1:i2]
+            Er=torch.zeros(rows,i2-i1)
+            for j in range(i2-i1):
+                wc=Wb[:,j]
+                d=Hb[j,j]
+                qc=torch.clamp(torch.round(wc/sf),-cr,cr)
+                Q[:,i1+j]=qc.to(torch.int8)
+                er=(wc-qc.float()*sf)/d
+                Er[:,j]=er
+                Wb[:,j:]-=er.unsqueeze(1)*Hb[j,j:].unsqueeze(0)
+            if i2<cols:
+                Ww[:,i2:]-=Er@Hi[i1:i2,i2:]
+        recon=Q.float()*sf[:,None]
+        mse=(Wp-recon).pow(2).mean().item()
+        if mse<be:
+            bq,bsc,be=Q,s,mse
+    return bq[:,invperm],bsc
+def _gptq_mq6(sd,cats,hessians):
+    res,meta={},{}
+    gc,fc=0,0
+    for name,tensor in sd.items():
+        t=tensor.detach().cpu().contiguous()
+        cat=classify_param(name)
+        if not t.is_floating_point() or t.numel()<=65536:
+            res[name]=t.to(torch.float16) if t.is_floating_point() else t
+            meta[name]="passthrough"
+            continue
+        if any(p in name for p in CTRL_PAT):
+            res[name]=t.float()
+            meta[name]="passthrough_ctrl"
+            continue
+        if cat in cats and t.ndim==2:
+            if name in hessians:
+                q,s=_gptq_qw(t,hessians[name])
+                gc+=1
+                meta[name]={"type":"int6","method":"gptq"}
+            else:
+                q,s=_qi6(t)
+                fc+=1
+                meta[name]={"type":"int6","method":"clip_search"}
+            res[name+".q"]=q
+            res[name+".scale"]=s
+        elif cat in cats and t.ndim>=1:
+            q,s=_qi6(t)
+            res[name+".q"]=q
+            res[name+".scale"]=s
+            meta[name]={"type":"int6"}
+        else:
+            q,s=_qft(t)
+            res[name+".q"]=q
+            res[name+".scale"]=s
+            meta[name]={"type":"int8"}
+    log(f"GPTQ quantization: {gc} layers with full GPTQ, {fc} fallback to clip-search")
+    return res,meta
+def _mq6(sd,cats):
+    res,meta={},{}
+    for name,tensor in sd.items():
+        t=tensor.detach().cpu().contiguous()
+        cat=classify_param(name)
+        if not t.is_floating_point() or t.numel()<=65536:
+            res[name]=t.to(torch.float16) if t.is_floating_point() else t
+            meta[name]="passthrough"
+            continue
+        if any(p in name for p in CTRL_PAT):
+            res[name]=t.float()
+            meta[name]="passthrough_ctrl"
+            continue
+        if cat in cats and t.ndim>=1:
+            q,s=_qi6(t)
+            res[name+".q"]=q
+            res[name+".scale"]=s
+            meta[name]={"type":"int6"}
+        else:
+            q,s=_qft(t)
+            res[name+".q"]=q
+            res[name+".scale"]=s
+            meta[name]={"type":"int8"}
+    return res,meta
+def _deq6(res,meta,tsd):
+    out={}
+    for name,orig in tsd.items():
+        info=meta.get(name)
+        if info is None:
+            continue
+        od=orig.dtype
+        if info in ("passthrough","passthrough_ctrl","passthrough_fp16"):
+            t=res[name]
+            if t.dtype==torch.float16 and od in (torch.float32,torch.bfloat16):
+                t=t.to(od)
+            out[name]=t
+            continue
+        q,s=res[name+".q"],res[name+".scale"]
+        if s.ndim>0:
+            out[name]=(q.float()*s.float().view(q.shape[0],*([1]*(q.ndim-1)))).to(od)
+        else:
+            out[name]=(q.float()*float(s.item())).to(od)
+    return out
+_BSHF=b"BSHF"
+def _bshuf(data,stride=2):
+    if stride<=1 or len(data)<stride:
+        return data
+    src=np.frombuffer(data,dtype=np.uint8)
+    n=len(src)
+    out=np.empty(n,dtype=np.uint8)
+    d=0
+    for pos in range(stride):
+        ch=src[pos::stride]
+        out[d:d+len(ch)]=ch
+        d+=len(ch)
+    return _BSHF+bytes([stride])+out.tobytes()
+def _bunshuf(data):
+    if len(data)<5 or data[:4]!=_BSHF:
+        return data
+    stride=data[4]
+    if stride<2:
+        return data[5:]
+    pay=np.frombuffer(data,dtype=np.uint8,offset=5)
+    n=len(pay)
+    out=np.empty(n,dtype=np.uint8)
+    s=0
+    for pos in range(stride):
+        cl=n//stride+(1 if pos<n%stride else 0)
+        out[pos::stride][:cl]=pay[s:s+cl]
+        s+=cl
+    return out.tobytes()
+def _compress(data,comp,bshuf=True):
+    if bshuf:
+        data=_bshuf(data)
+    if comp=="lzma":
+        return lzma.compress(data,preset=6)
+    elif comp=="brotli":
+        import brotli
+        return brotli.compress(data,quality=11)
+    raise ValueError(f"Unknown compressor: {comp!r}")
+def _decompress(data,comp,bshuf=True):
+    if comp=="lzma":
+        raw=lzma.decompress(data)
+    elif comp=="brotli":
+        import brotli
+        raw=brotli.decompress(data)
+    else:
+        raise ValueError(f"Unknown compressor: {comp!r}")
+    if bshuf:
+        raw=_bunshuf(raw)
+    return raw
+def serialize(h,base_model,code):
+    cb=len(code.encode("utf-8"))
+    if h.is_main_process:
+        torch.save(base_model.state_dict(),h.model_path)
+        mb=os.path.getsize(h.model_path)
+        log(f"Serialized model: {mb} bytes")
+        log(f"Code size: {cb} bytes")
+    sd={k:v.detach().cpu() for k,v in base_model.state_dict().items()}
+    if h.gptq_enabled:
+        log("GPTQ:collecting Hessians from calibration data...")
+        t0=time.perf_counter()
+        cl=DistributedTokenLoader(h.train_files,h.rank,h.world_size,torch.device("cuda",h.local_rank))
+        H=_collect_H(base_model,cl,h,torch.device("cuda",h.local_rank),ncb=h.gptq_calibration_batches)
+        log(f"GPTQ:collected {len(H)} Hessians in {time.perf_counter()-t0:.1f}s")
+        qr,qm=_gptq_mq6(sd,{"mlp","attn"},H)
+    else:
+        qr,qm=_mq6(sd,{"mlp","attn"})
+    buf=io.BytesIO()
+    torch.save({"w":qr,"m":qm},buf)
+    blob=_compress(buf.getvalue(),h.compressor)
+    qfb=len(blob)
+    if h.is_main_process:
+        with open(h.quantized_model_path,"wb") as f:
+            f.write(blob)
+        log(f"Serialized model int6+{h.compressor}: {qfb} bytes")
+        log(f"Total submission size int6+{h.compressor}: {qfb+cb} bytes")
+def deserialize(h,device):
+    em=GPT(h).to(device).bfloat16()
+    restore_fp32_params(em)
+    sd={k:v.detach().cpu() for k,v in em.state_dict().items()}
+    with open(h.quantized_model_path,"rb") as f:
+        blob=f.read()
+    qs=torch.load(io.BytesIO(_decompress(blob,h.compressor)),map_location="cpu")
+    ds=_deq6(qs["w"],qs["m"],sd)
+    em.load_state_dict(ds,strict=True)
+    return em
+def _loss_bpb(ls,tc,bc):
+    vl=(ls/tc).item()
+    return vl,vl/math.log(2.0)*(tc.item()/bc.item())
+def eval_val(h,device,vd,model):
+    sl=h.eval_seq_len
+    lbt=h.val_batch_tokens//(h.world_size*h.grad_accum_steps)
+    assert lbt>=sl,"VAL_BATCH_SIZE too small"
+    lbs=lbt//sl
+    ts=(vd.val_tokens.numel()-1)//sl
+    ss=(ts*h.rank)//h.world_size
+    se=(ts*(h.rank+1))//h.world_size
+    ls=torch.zeros((),device=device,dtype=torch.float64)
+    tc=torch.zeros((),device=device,dtype=torch.float64)
+    bc=torch.zeros((),device=device,dtype=torch.float64)
+    model.eval()
+    with torch.inference_mode():
+        for bs in range(ss,se,lbs):
+            be=min(bs+lbs,se)
+            rs=bs*sl
+            re=be*sl+1
+            loc=vd.val_tokens[rs:re].to(device=device,dtype=torch.int64,non_blocking=True)
+            x=loc[:-1].reshape(-1,sl)
+            y=loc[1:].reshape(-1,sl)
+            with torch.autocast(device_type="cuda",dtype=torch.bfloat16,enabled=True):
+                bl=model(x,y).detach()
+            btc=float(y.numel())
+            ls+=bl.to(torch.float64)*btc
+            tc+=btc
+            pi=x.reshape(-1)
+            ti=y.reshape(-1)
+            tb=vd.base_bytes_lut[ti].to(dtype=torch.int16)
+            tb+=(vd.has_leading_space_lut[ti]&~vd.is_boundary_token_lut[pi]).to(dtype=torch.int16)
+            bc+=tb.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        for t in [ls,tc,bc]:
+            dist.all_reduce(t,op=dist.ReduceOp.SUM)
+    model.train()
+    return _loss_bpb(ls,tc,bc)
+def _sw_setup(h,vd):
+    sl=h.eval_seq_len
+    cs=sl-h.eval_stride
+    tt=vd.val_tokens.numel()-1
+    ws=[w for w in range(0,tt,h.eval_stride) if w+cs<tt]
+    tw=len(ws)
+    ms=(tw*h.rank)//h.world_size
+    me=(tw*(h.rank+1))//h.world_size
+    return sl,cs,tt,ws[ms:me]
+def _sw_score(vd,nll,x_batch,y_batch,batch_ws,wlens,cs,ls,tc,bc):
+    for i,ws in enumerate(batch_ws):
+        wl=wlens[i]
+        s=0 if ws==0 else cs
+        sn=nll[i,s:wl].to(torch.float64)
+        ls+=sn.sum()
+        tc+=float(wl-s)
+        tgt=y_batch[i,s:wl]
+        prev=x_batch[i,s:wl]
+        tb=vd.base_bytes_lut[tgt].to(torch.float64)
+        tb+=(vd.has_leading_space_lut[tgt]&~vd.is_boundary_token_lut[prev]).to(torch.float64)
+        bc+=tb.sum()
+def _sw_fill(vd,batch_ws,sl,tt,device):
+    bsz=len(batch_ws)
+    xb=torch.zeros(bsz,sl,dtype=torch.int64,device=device)
+    yb=torch.zeros(bsz,sl,dtype=torch.int64,device=device)
+    wlens=[]
+    for i,ws in enumerate(batch_ws):
+        we=min(ws+sl,tt)
+        wl=we-ws
+        wlens.append(wl)
+        ch=vd.val_tokens[ws:we+1].to(dtype=torch.int64,device=device)
+        xb[i,:wl]=ch[:-1]
+        yb[i,:wl]=ch[1:]
+    return xb,yb,wlens,bsz
+def _dist_reduce(*tensors):
+    if dist.is_available() and dist.is_initialized():
+        for t in tensors:
+            dist.all_reduce(t,op=dist.ReduceOp.SUM)
+def eval_val_sliding(h,device,vd,bm,batch_seqs=32):
+    bm.eval()
+    lfn=torch.compile(bm.forward_logits,dynamic=False,fullgraph=True)
+    sl,cs,tt,mw=_sw_setup(h,vd)
+    ls=torch.zeros((),device=device,dtype=torch.float64)
+    tc=torch.zeros((),device=device,dtype=torch.float64)
+    bc=torch.zeros((),device=device,dtype=torch.float64)
+    with torch.inference_mode():
+        for bi in range(0,len(mw),batch_seqs):
+            bws=mw[bi:bi+batch_seqs]
+            xb,yb,wlens,bsz=_sw_fill(vd,bws,sl,tt,device)
+            with torch.autocast(device_type="cuda",dtype=torch.bfloat16):
+                logits=lfn(xb)
+            nll=F.cross_entropy(logits.reshape(-1,logits.size(-1)).float(),yb.reshape(-1),reduction="none").reshape(bsz,sl)
+            _sw_score(vd,nll,xb,yb,bws,wlens,cs,ls,tc,bc)
+    _dist_reduce(ls,tc,bc)
+    bm.train()
+    return _loss_bpb(ls,tc,bc)
+def timed_eval(label,fn,*args,**kwargs):
+    torch.cuda.synchronize()
+    t0=time.perf_counter()
+    vl,vb=fn(*args,**kwargs)
+    torch.cuda.synchronize()
+    ms=1000.0*(time.perf_counter()-t0)
+    log(f"{label} val_loss:{vl:.8f} val_bpb:{vb:.8f} eval_time:{ms:.0f}ms")
+    return vl,vb
+def eval_val_slot(h,device,vd,bm,batch_seqs=32):
+    bm.eval()
+    sl,cs,tt,mw=_sw_setup(h,vd)
+    ls=torch.zeros((),device=device,dtype=torch.float64)
+    tc=torch.zeros((),device=device,dtype=torch.float64)
+    bc=torch.zeros((),device=device,dtype=torch.float64)
+    dm=h.model_dim
+    for bi in range(0,len(mw),batch_seqs):
+        bws=mw[bi:bi+batch_seqs]
+        xb,yb,wlens,bsz=_sw_fill(vd,bws,sl,tt,device)
+        with torch.no_grad(),torch.autocast(device_type="cuda",dtype=torch.bfloat16):
+            hid=bm.forward_backbone(xb).detach()
+        delta=torch.zeros(bsz,1,dm,device=device,dtype=hid.dtype,requires_grad=True)
+        sopt=torch.optim.AdamW([delta],lr=h.slot_lr)
+        for _ in range(h.slot_steps):
+            sopt.zero_grad()
+            logits=bm._head_logits(hid+delta)
+            sl_loss=F.cross_entropy(logits.reshape(-1,logits.size(-1)).float(),yb.reshape(-1),reduction="mean")
+            sl_loss.backward()
+            sopt.step()
+        with torch.no_grad():
+            logits=bm._head_logits(hid+delta)
+            nll=F.cross_entropy(logits.reshape(-1,logits.size(-1)).float(),yb.reshape(-1),reduction="none").reshape(bsz,sl)
+            _sw_score(vd,nll,xb,yb,bws,wlens,cs,ls,tc,bc)
+    _dist_reduce(ls,tc,bc)
+    bm.train()
+    return _loss_bpb(ls,tc,bc)
+def run_evals(h,device,vd,em):
+    cm=torch.compile(em,dynamic=False,fullgraph=True)
+    timed_eval("final_int6_roundtrip",eval_val,h,device,vd,cm)
+    if h.sliding_window_enabled:
+        timed_eval("final_int6_sliding_window",eval_val_sliding,h,device,vd,em)
+    if h.slot_enabled:
+        timed_eval("final_int6_slot",eval_val_slot,h,device,vd,em)
+class Optimizers():
+    def __init__(self,h,bm):
+        bnp=list(bm.blocks.named_parameters())
+        mp=[p for n,p in bnp if p.ndim==2 and not any(pt in n for pt in CTRL_PAT)]
+        sp=[p for n,p in bnp if p.ndim<2 or any(pt in n for pt in CTRL_PAT)]
+        if bm.skip_weights.numel()>0:
+            sp.append(bm.skip_weights)
+        if bm.skip_gates is not None and bm.skip_gates.numel()>0:
+            sp.append(bm.skip_gates)
+        tlr=h.tied_embed_lr if h.tie_embeddings else h.embed_lr
+        tp=[{"params":[bm.tok_emb.weight],"lr":tlr,"base_lr":tlr}]
+        if bm.ve_shared is not None:
+            tp.append({"params":[bm.ve_shared.embed.weight],"lr":tlr,"base_lr":tlr})
+            if bm.ve_shared.proj is not None:
+                mp.append(bm.ve_shared.proj.weight)
+            sp.append(bm.ve_shared.scale)
+            for s in bm.ve_layer_scales:
+                sp.append(s)
+        self.optimizer_tok=torch.optim.AdamW(tp,betas=(h.beta1,h.beta2),eps=h.adam_eps,weight_decay=h.embed_wd,fused=True)
+        self.optimizer_muon=Muon(mp,lr=h.matrix_lr,momentum=h.muon_momentum,backend_steps=h.muon_backend_steps,weight_decay=h.muon_wd)
+        for g in self.optimizer_muon.param_groups:
+            g["base_lr"]=h.matrix_lr
+        self.optimizer_scalar=torch.optim.AdamW([{"params":sp,"lr":h.scalar_lr,"base_lr":h.scalar_lr}],betas=(h.beta1,h.beta2),eps=h.adam_eps,weight_decay=h.adam_wd,fused=True)
+        self.optimizers=[self.optimizer_tok,self.optimizer_muon,self.optimizer_scalar]
+        if bm.lm_head is not None:
+            self.optimizer_head=torch.optim.Adam([{"params":[bm.lm_head.weight],"lr":h.head_lr,"base_lr":h.head_lr}],betas=(h.beta1,h.beta2),eps=h.adam_eps,fused=True)
+            self.optimizers.insert(1,self.optimizer_head)
+        else:
+            self.optimizer_head=None
+    def __iter__(self):
+        return iter(self.optimizers)
+    def zero_grad_all(self):
+        for opt in self.optimizers:
+            opt.zero_grad(set_to_none=True)
+    def step(self):
+        for opt in self.optimizers:
+            opt.step()
+        self.zero_grad_all()
+def train_model(h,device,vd):
+    bm=GPT(h).to(device).bfloat16()
+    restore_fp32_params(bm)
+    cm=torch.compile(bm,dynamic=False,fullgraph=True)
+    if h.distributed:
+        model=DDP(cm,device_ids=[h.local_rank],broadcast_buffers=False)
+    else:
+        model=cm
+    log(f"model_params:{sum(p.numel() for p in bm.parameters())}")
+    opts=Optimizers(h,bm)
+    loader=DistributedTokenLoader(h.train_files,h.rank,h.world_size,device)
+    mwms=1000.0*h.max_wallclock_seconds if h.max_wallclock_seconds>0 else None
+    if h.gptq_enabled and mwms is not None:
+        mwms-=h.gptq_reserve_seconds*1000.0
+        log(f"gptq:reserving {h.gptq_reserve_seconds:.0f}s, effective={mwms:.0f}ms")
+    def tfrac(step,ems):
+        if mwms is None:
+            return step/max(h.iterations,1)
+        return ems/max(mwms,1e-9)
+    def lrmul(frac):
+        if h.warmdown_frac<=0:
+            return 1.0
+        if frac>=1.0-h.warmdown_frac:
+            return max((1.0-frac)/h.warmdown_frac,h.min_lr)
+        return 1.0
+    def sfn(step,lrs):
+        opts.zero_grad_all()
+        tl=torch.zeros((),device=device)
+        for ms in range(h.grad_accum_steps):
+            if h.distributed:
+                model.require_backward_grad_sync=ms==h.grad_accum_steps-1
+            x,y=loader.next_batch(h.train_batch_tokens,h.train_seq_len,h.grad_accum_steps)
+            with torch.autocast(device_type="cuda",dtype=torch.bfloat16,enabled=True):
+                loss=model(x,y)
+            tl+=loss.detach()
+            (loss/h.grad_accum_steps).backward()
+        tl/=h.grad_accum_steps
+        fr=min(step/h.muon_momentum_warmup_steps,1.0) if h.muon_momentum_warmup_steps>0 else 1.0
+        mm=(1-fr)*h.muon_momentum_warmup_start+fr*h.muon_momentum
+        for g in opts.optimizer_muon.param_groups:
+            g["momentum"]=mm
+        for opt in opts:
+            for g in opt.param_groups:
+                g["lr"]=g["base_lr"]*lrs
+        if h.grad_clip_norm>0:
+            torch.nn.utils.clip_grad_norm_(bm.parameters(),h.grad_clip_norm)
+        opts.step()
+        return tl
+    if h.warmup_steps>0:
+        ims={n:t.detach().cpu().clone() for n,t in bm.state_dict().items()}
+        ios=[copy.deepcopy(opt.state_dict()) for opt in opts]
+        model.train()
+        for ws in range(h.warmup_steps):
+            sfn(ws,1.0)
+            if ws<=5 or (ws+1)%10==0 or ws+1==h.warmup_steps:
+                log(f"warmup_step: {ws+1}/{h.warmup_steps}")
+        bm.load_state_dict(ims,strict=True)
+        for opt,st in zip(opts,ios,strict=True):
+            opt.load_state_dict(st)
+        opts.zero_grad_all()
+        if h.distributed:
+            model.require_backward_grad_sync=True
+        loader=DistributedTokenLoader(h.train_files,h.rank,h.world_size,device)
+    ema={n:t.detach().float().clone() for n,t in bm.state_dict().items()}
+    ed=h.ema_decay
+    ttms=0.0
+    sas=None
+    torch.cuda.synchronize()
+    t0=time.perf_counter()
+    step=0
+    while True:
+        last=step==h.iterations or (sas is not None and step>=sas)
+        sv=last or (h.val_loss_every>0 and step%h.val_loss_every==0)
+        if sv:
+            torch.cuda.synchronize()
+            ttms+=1000.0*(time.perf_counter()-t0)
+            vl,vb=eval_val(h,device,vd,model)
+            log(f"{step}/{h.iterations} val_loss: {vl:.4f} val_bpb: {vb:.4f}")
+            torch.cuda.synchronize()
+            t0=time.perf_counter()
+        if last:
+            if sas is not None and step<h.iterations:
+                log(f"stopping_early: wallclock_cap train_time: {ttms:.0f}ms step: {step}/{h.iterations}")
+            break
+        ems=ttms+1000.0*(time.perf_counter()-t0)
+        frac=tfrac(step,ems)
+        tl=sfn(step,lrmul(frac))
+        with torch.no_grad():
+            for n,t in bm.state_dict().items():
+                ema[n].mul_(ed).add_(t.detach().float(),alpha=1.0-ed)
+        step+=1
+        atms=ttms+1000.0*(time.perf_counter()-t0)
+        slt=h.train_log_every>0 and (step<=5 or step%h.train_log_every==0 or sas is not None)
+        if slt:
+            tps=step*h.train_batch_tokens/(atms/1000.0)
+            log(f"{step}/{h.iterations} train_loss: {tl.item():.4f} train_time: {atms/60000:.1f}m tok/s: {tps:.0f}")
+        rc=mwms is not None and atms>=mwms
+        if h.distributed and mwms is not None:
+            rct=torch.tensor(int(rc),device=device)
+            dist.all_reduce(rct,op=dist.ReduceOp.MAX)
+            rc=bool(rct.item())
+        if sas is None and rc:
+            sas=step
+    log(f"peak memory allocated: {torch.cuda.max_memory_allocated()//1024//1024} MiB reserved: {torch.cuda.max_memory_reserved()//1024//1024} MiB")
+    log("ema:applying EMA weights")
+    cs=bm.state_dict()
+    avg={n:t.to(dtype=cs[n].dtype) for n,t in ema.items()}
+    bm.load_state_dict(avg,strict=True)
+    return bm,cm
+def train_and_eval(h,device):
+    random.seed(h.seed)
+    np.random.seed(h.seed)
+    torch.manual_seed(h.seed)
+    torch.cuda.manual_seed_all(h.seed)
+    vd=ValidationData(h,device)
+    log(f"train_shards: {len(list(Path(h.datasets_dir).resolve().glob('fineweb_train_*.bin')))}")
+    log(f"val_tokens: {vd.val_tokens.numel()-1}")
+    bm,cm=train_model(h,device,vd)
+    timed_eval("pre-quantization post-ema",eval_val,h,device,vd,cm)
+    serialize(h,bm,Path(__file__).read_text(encoding="utf-8"))
+    if h.distributed:
+        dist.barrier()
+    em=deserialize(h,device)
+    run_evals(h,device,vd,em)
+def main():
+    ws=int(_E("WORLD_SIZE","1"))
+    lr=int(_E("LOCAL_RANK","0"))
+    dd="RANK" in os.environ and "WORLD_SIZE" in os.environ
+    assert torch.cuda.is_available(),"CUDA is required"
+    assert ws>0 and 8%ws==0,f"WORLD_SIZE={ws} must be positive and divide 8"
+    device=torch.device("cuda",lr)
+    torch.cuda.set_device(device)
+    if dd:
+        dist.init_process_group(backend="nccl",device_id=device)
+        dist.barrier()
+    torch.backends.cuda.matmul.allow_tf32=True
+    torch.backends.cudnn.allow_tf32=True
+    torch.set_float32_matmul_precision("high")
+    from torch.backends.cuda import enable_cudnn_sdp,enable_flash_sdp,enable_math_sdp,enable_mem_efficient_sdp
+    enable_cudnn_sdp(False)
+    enable_flash_sdp(True)
+    enable_mem_efficient_sdp(False)
+    enable_math_sdp(False)
+    torch._dynamo.config.optimize_ddp=False
+    h=Hyperparameters()
+    set_logging_hparams(h)
+    if h.is_main_process:
+        os.makedirs("logs",exist_ok=True)
+        log(100*"=",console=False)
+        log("Hyperparameters:",console=True)
+        for k,v in sorted(vars(type(h)).items()):
+            if not k.startswith("_"):
+                log(f"  {k}: {v}",console=True)
+        log(Path(__file__).read_text(encoding="utf-8"),console=False)
+        log("="*100,console=False)
+        log(f"Running Python {sys.version}",console=False)
+        log(f"Running PyTorch {torch.__version__}",console=False)
+        log(subprocess.run(["nvidia-smi"],stdout=subprocess.PIPE,stderr=subprocess.PIPE,text=True,check=False).stdout,console=False)
+        log("="*100,console=False)
+    train_and_eval(h,device)
+    if dd:
+        dist.destroy_process_group()
+if __name__=="__main__":
+    main()
diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt.py.bak b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt.py.bak
new file mode 100644
index 0000000000..21bbdc07b6
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt.py.bak
@@ -0,0 +1,1488 @@
+import copy
+import glob
+import io
+import lzma
+import math
+import os
+from pathlib import Path
+import random
+import subprocess
+import sys
+import time
+import uuid
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch import Tensor, nn
+from flash_attn_interface import flash_attn_func as flash_attn_3_func
+class Hyperparameters():
+    data_dir = os.environ.get('DATA_DIR', './data/')
+    seed = int(os.environ.get('SEED', 1337))
+    run_id = os.environ.get("RUN_ID", str(uuid.uuid4()))
+    iterations = int(os.environ.get('ITERATIONS', 20000))
+    warmdown_frac = float(os.environ.get('WARMDOWN_FRAC', 0.667))
+    warmup_steps = int(os.environ.get('WARMUP_STEPS', 20))
+    train_batch_tokens = int(os.environ.get('TRAIN_BATCH_TOKENS', 2048 * 48 * 8))
+    train_seq_len = int(os.environ.get('TRAIN_SEQ_LEN', 2048))
+    eval_seq_len = int(os.environ.get('EVAL_SEQ_LEN', 2048))
+    max_wallclock_seconds = float(os.environ.get('MAX_WALLCLOCK_SECONDS', 600.0))
+    train_log_every = int(os.environ.get('TRAIN_LOG_EVERY', 500))
+    val_batch_tokens = int(os.environ.get('VAL_BATCH_TOKENS', 2048 * 32 * 8))
+    val_loss_every = int(os.environ.get('VAL_LOSS_EVERY', 4000))
+    sliding_window_enabled = bool(int(os.environ.get('SLIDING_WINDOW_ENABLED', '1')))
+    vocab_size = int(os.environ.get('VOCAB_SIZE', 4096))
+    num_layers = int(os.environ.get('NUM_LAYERS', 11))
+    xsa_last_n = int(os.environ.get('XSA_LAST_N', 11))
+    num_kv_heads = int(os.environ.get('NUM_KV_HEADS', 4))
+    model_dim = int(os.environ.get('MODEL_DIM', 512))
+    embedding_dim = int(os.environ.get('EMBEDDING_DIM', 512))
+    num_heads = int(os.environ.get('NUM_HEADS', 8))
+    mlp_mult = float(os.environ.get('MLP_MULT', 4.0))
+    skip_gates_enabled = bool(int(os.environ.get('SKIP_GATES_ENABLED', '1')))
+    tie_embeddings = bool(int(os.environ.get('TIE_EMBEDDINGS', '1')))
+    logit_softcap = float(os.environ.get('LOGIT_SOFTCAP', 30.0))
+    rope_base = float(os.environ.get('ROPE_BASE', 10000.0))
+    rope_dims = int(os.environ.get('ROPE_DIMS', 16))
+    rope_train_seq_len = int(os.environ.get('ROPE_TRAIN_SEQ_LEN', 2048))
+    ln_scale = bool(int(os.environ.get('LN_SCALE', '1')))
+    ve_enabled = bool(int(os.environ.get('VE_ENABLED', '1')))
+    ve_dim = int(os.environ.get('VE_DIM', 128))
+    ve_layers = os.environ.get('VE_LAYERS', '9,10')
+    qk_gain_init = float(os.environ.get('QK_GAIN_INIT', 4.0))
+    min_lr = float(os.environ.get('MIN_LR', 0.0))
+    embed_lr = float(os.environ.get('EMBED_LR', 0.6))
+    head_lr = float(os.environ.get('HEAD_LR', 0.008))
+    tied_embed_lr = float(os.environ.get('TIED_EMBED_LR', 0.03))
+    tied_embed_init_std = float(os.environ.get('TIED_EMBED_INIT_STD', 0.005))
+    matrix_lr = float(os.environ.get('MATRIX_LR', 0.02))
+    scalar_lr = float(os.environ.get('SCALAR_LR', 0.02))
+    muon_momentum = float(os.environ.get('MUON_MOMENTUM', 0.99))
+    muon_backend_steps = int(os.environ.get('MUON_BACKEND_STEPS', 4))
+    muon_momentum_warmup_start = float(os.environ.get('MUON_MOMENTUM_WARMUP_START', 0.92))
+    muon_momentum_warmup_steps = int(os.environ.get('MUON_MOMENTUM_WARMUP_STEPS', 1500))
+    beta1 = float(os.environ.get('BETA1', 0.9))
+    beta2 = float(os.environ.get('BETA2', 0.95))
+    adam_eps = float(os.environ.get('ADAM_EPS', 1e-8))
+    grad_clip_norm = float(os.environ.get('GRAD_CLIP_NORM', 0.3))
+    eval_stride = int(os.environ.get('EVAL_STRIDE', 64))
+    muon_beta2 = float(os.environ.get('MUON_BETA2', 0.95))
+    adam_wd = float(os.environ.get('ADAM_WD', 0.02))
+    muon_wd = float(os.environ.get('MUON_WD', 0.085))
+    embed_wd = float(os.environ.get('EMBED_WD', 0.085))
+    ema_decay = float(os.environ.get('EMA_DECAY', 0.997))
+    recur_layers = os.environ.get('RECUR_LAYERS', '')  # e.g. "4,5"
+    recur_start_step = int(os.environ.get('RECUR_START_STEP', 3000))
+    slot_enabled = bool(int(os.environ.get('SLOT_ENABLED', '0')))
+    slot_steps = int(os.environ.get('SLOT_STEPS', 8))
+    slot_lr = float(os.environ.get('SLOT_LR', 0.005))
+    compressor = os.environ.get('COMPRESSOR', 'brotli')  #(lzma or brotli)
+    gptq_enabled = bool(int(os.environ.get('GPTQ_ENABLED', '1')))
+    gptq_calibration_batches = int(os.environ.get('GPTQ_CALIBRATION_BATCHES', 64))
+    gptq_reserve_seconds = float(os.environ.get('GPTQ_RESERVE_SECONDS', 10.0))
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    is_main_process = rank == 0
+    grad_accum_steps = 8 // world_size
+    datasets_dir = os.path.join(data_dir, 'datasets', f'fineweb10B_sp{vocab_size}')
+    train_files = os.path.join(datasets_dir, 'fineweb_train_*.bin')
+    val_files = os.path.join(datasets_dir, 'fineweb_val_*.bin')
+    tokenizer_path = os.path.join(data_dir, 'tokenizers', f'fineweb_{vocab_size}_bpe.model')
+    logfile = f"logs/{run_id}.txt"
+    model_path = "final_model.pt"
+    quantized_model_path = "final_model.int6.ptz"
+_logger_hparams = None
+def set_logging_hparams(h: Hyperparameters) -> None:
+    global _logger_hparams
+    _logger_hparams = h
+def log(msg, console: bool = True) -> None:
+    if _logger_hparams is None:
+        print(msg)
+    if _logger_hparams.is_main_process:
+        if console:
+            print(msg)
+        if _logger_hparams.logfile is not None:
+            with open(_logger_hparams.logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+class ValidationData:
+    def __init__(self, h: Hyperparameters, device: torch.device):
+        if not h.tokenizer_path.endswith(".model"):
+            raise ValueError(f"Script only setup for SentencePiece .model file: {h.tokenizer_path}")
+        self.sp = spm.SentencePieceProcessor(model_file=h.tokenizer_path)
+        if int(self.sp.vocab_size()) != h.vocab_size:
+            raise ValueError(
+                f"VOCAB_SIZE={h.vocab_size} does not match tokenizer vocab_size={int(self.sp.vocab_size())}"
+            )
+        self.val_tokens = load_validation_tokens(h.val_files, h.eval_seq_len)
+        self.base_bytes_lut, self.has_leading_space_lut, self.is_boundary_token_lut = (
+            build_sentencepiece_luts(self.sp, h.vocab_size, device))
+def build_sentencepiece_luts(
+    sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device
+) -> tuple[Tensor, Tensor, Tensor]:
+    sp_vocab_size = int(sp.vocab_size())
+    assert sp.piece_to_id("\u2581") != sp.unk_id(), \
+        "Tokenizer must have '▁' (space) as its own token for correct BPB byte counting"
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+def load_validation_tokens(pattern: str, seq_len: int) -> Tensor:
+    files = [Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    tokens = torch.cat([load_data_shard(file) for file in files]).contiguous()
+    usable = ((tokens.numel() - 1) // seq_len) * seq_len
+    if usable <= 0:
+        raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}")
+    return tokens[: usable + 1]
+def load_data_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    token_bytes = np.dtype("<u2").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    expected_size = header_bytes + num_tokens * token_bytes
+    if file.stat().st_size != expected_size:
+        raise ValueError(f"Shard size mismatch for {file}: expected {expected_size} bytes")
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    if tokens_np.size != num_tokens:
+        raise ValueError(f"Short read for {file}")
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+_SHARD_HEADER_BYTES = 256 * np.dtype("<i4").itemsize
+_SHARD_NTOKENS_CACHE: dict[str, int] = {}
+_MMAP_CACHE: dict[str, np.memmap] = {}
+def _read_num_tokens(file: Path) -> int:
+    key = str(file)
+    cached = _SHARD_NTOKENS_CACHE.get(key)
+    if cached is not None:
+        return cached
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    n = int(header[2])
+    _SHARD_NTOKENS_CACHE[key] = n
+    return n
+def _get_shard_memmap(file: Path) -> np.memmap:
+    key = str(file)
+    mm = _MMAP_CACHE.get(key)
+    if mm is not None:
+        return mm
+    n = _read_num_tokens(file)
+    mm = np.memmap(file, mode="r", dtype="<u2", offset=_SHARD_HEADER_BYTES, shape=(n,))
+    _MMAP_CACHE[key] = mm
+    return mm
+class DistributedTokenLoader:
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank = rank
+        self.world_size = world_size
+        self.device = device
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self._num_tokens = np.array([_read_num_tokens(f) for f in self.files], dtype=np.int64)
+        seed = 0
+        for f in self.files:
+            for b in str(f).encode():
+                seed = ((seed ^ b) * 1099511628211) & 0xFFFFFFFFFFFFFFFF
+        self._rng = np.random.Generator(np.random.PCG64(seed))
+        self._cfg: tuple[int, int, int, int] | None = None
+        self._eligible_shards: np.ndarray | None = None
+        self._base_block_counts: np.ndarray | None = None
+        n = len(self.files)
+        self._cursor_phase = np.zeros(n, dtype=np.int64)
+        self._cursor_block_count = np.zeros(n, dtype=np.int64)
+        self._cursor_next = np.zeros(n, dtype=np.int64)
+        self._cursor_start = np.zeros(n, dtype=np.int64)
+        self._cursor_stride = np.ones(n, dtype=np.int64)
+        self._cursor_init = np.zeros(n, dtype=np.bool_)
+        self._batches_built = 0
+    def _pick_coprime_stride(self, n: int) -> int:
+        if n <= 1:
+            return 1
+        while True:
+            s = int(self._rng.integers(1, n))
+            if math.gcd(s, n) == 1:
+                return s
+    def _reset_cursor(self, si: int, seq_len: int) -> None:
+        nt = int(self._num_tokens[si])
+        max_phase = min(seq_len - 1, max(0, nt - seq_len - 1))
+        phase = int(self._rng.integers(max_phase + 1)) if max_phase > 0 else 0
+        bc = (nt - 1 - phase) // seq_len
+        self._cursor_phase[si] = phase
+        self._cursor_block_count[si] = bc
+        self._cursor_next[si] = 0
+        self._cursor_start[si] = int(self._rng.integers(bc)) if bc > 1 else 0
+        self._cursor_stride[si] = self._pick_coprime_stride(bc)
+        self._cursor_init[si] = True
+    def _ensure_cursor(self, si: int, seq_len: int) -> None:
+        if not self._cursor_init[si] or self._cursor_next[si] >= self._cursor_block_count[si]:
+            self._reset_cursor(si, seq_len)
+    def _take_from_shard(self, si: int, seq_len: int, count: int, out: list[tuple[int, int]]) -> None:
+        rem = count
+        while rem > 0:
+            self._ensure_cursor(si, seq_len)
+            bc = int(self._cursor_block_count[si])
+            ni = int(self._cursor_next[si])
+            take = min(rem, bc - ni)
+            phase = int(self._cursor_phase[si])
+            start = int(self._cursor_start[si])
+            stride = int(self._cursor_stride[si])
+            for j in range(take):
+                bi = (start + (ni + j) * stride) % bc
+                out.append((si, phase + bi * seq_len))
+            self._cursor_next[si] = ni + take
+            rem -= take
+    def _init_pipeline(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> None:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        num_seqs = local_tokens // seq_len
+        global_num_seqs = num_seqs * self.world_size
+        self._cfg = (local_tokens, seq_len, num_seqs, global_num_seqs)
+        bbc = (self._num_tokens - 1) // seq_len
+        eligible = bbc > 0
+        self._eligible_shards = np.nonzero(eligible)[0].astype(np.int64)
+        self._base_block_counts = bbc[self._eligible_shards].astype(np.int64)
+    def _sample_global_windows(self) -> list[tuple[int, int]]:
+        assert self._cfg is not None and self._eligible_shards is not None
+        _, seq_len, _, gns = self._cfg
+        ec = int(self._eligible_shards.size)
+        progress = min(self._batches_built / 1800.0, 1.0)
+        remaining = np.empty(ec, dtype=np.float64)
+        for i, si in enumerate(self._eligible_shards.tolist()):
+            if self._cursor_init[si]:
+                r = int(self._cursor_block_count[si]) - int(self._cursor_next[si])
+                remaining[i] = float(max(r, 1))
+            else:
+                remaining[i] = float(self._base_block_counts[i])
+        alpha = 0.90 - 0.40 * progress
+        weights = np.power(remaining, alpha)
+        ws = float(weights.sum())
+        if not np.isfinite(ws) or ws <= 0.0:
+            weights = np.ones(ec, dtype=np.float64)
+            ws = float(weights.sum())
+        probs = weights / ws
+        low = min(max(8, self.world_size), ec, gns)
+        high = min(max(32, self.world_size * 8), ec, gns)
+        mix = max(1, min(int(round(low + progress * (high - low))), ec, gns))
+        cp = self._rng.choice(ec, size=mix, replace=False, p=probs)
+        cs = self._eligible_shards[cp]
+        cpr = probs[cp].copy()
+        cpr /= cpr.sum()
+        counts = np.ones(mix, dtype=np.int64)
+        extra = gns - mix
+        if extra > 0:
+            counts += self._rng.multinomial(extra, cpr).astype(np.int64)
+        perm = self._rng.permutation(mix)
+        cs, counts = cs[perm], counts[perm]
+        buckets: list[list[tuple[int, int]]] = []
+        for si, cnt in zip(cs.tolist(), counts.tolist()):
+            b: list[tuple[int, int]] = []
+            self._take_from_shard(int(si), seq_len, int(cnt), b)
+            if b:
+                if len(b) > 1:
+                    bp = self._rng.permutation(len(b))
+                    b = [b[int(k)] for k in bp.tolist()]
+                buckets.append(b)
+        windows: list[tuple[int, int]] = []
+        active = [i for i, bk in enumerate(buckets) if bk]
+        while active:
+            order = self._rng.permutation(len(active))
+            new_active: list[int] = []
+            for oi in order.tolist():
+                bi = active[oi]
+                if buckets[bi]:
+                    windows.append(buckets[bi].pop())
+                if buckets[bi]:
+                    new_active.append(bi)
+            active = new_active
+        return windows
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        if self._cfg is None:
+            self._init_pipeline(global_tokens, seq_len, grad_accum_steps)
+        _, _, num_seqs, _ = self._cfg
+        gw = self._sample_global_windows()
+        local_w = gw[self.rank::self.world_size]
+        x = torch.empty((num_seqs, seq_len), dtype=torch.int64)
+        y = torch.empty((num_seqs, seq_len), dtype=torch.int64)
+        for slot, (si, pos) in enumerate(local_w):
+            mm = _get_shard_memmap(self.files[si])
+            window = torch.as_tensor(np.array(mm[pos:pos + seq_len + 1], dtype=np.int64))
+            x[slot] = window[:-1]
+            y[slot] = window[1:]
+        self._batches_built += 1
+        return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True)
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+class CastedLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.to(x.dtype)
+        bias = self.bias.to(x.dtype) if self.bias is not None else None
+        return F.linear(x, w, bias)
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0):
+        super().__init__()
+        self.dim = dim
+        self.base = base
+        self.train_seq_len = train_seq_len
+        self.rope_dims = rope_dims if rope_dims > 0 else dim
+        inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+    def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]:
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            rd = self.rope_dims
+            if seq_len > self.train_seq_len:
+                scale = seq_len / self.train_seq_len
+                new_base = self.base * (scale ** (rd / (rd - 2)))
+                inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd))
+            else:
+                inv_freq = self.inv_freq.to(device)
+            t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype)
+            freqs = torch.outer(t, inv_freq)
+            self._cos_cached = freqs.cos()[None, :, None, :]
+            self._sin_cached = freqs.sin()[None, :, None, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor:
+    if rope_dims > 0 and rope_dims < x.size(-1):
+        x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:]
+        half = rope_dims // 2
+        x1, x2 = x_rope[..., :half], x_rope[..., half:]
+        x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+        return torch.cat((x_rope, x_pass), dim=-1)
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int,
+                 rope_base: float, qk_gain_init: float, train_seq_len: int):
+        super().__init__()
+        if dim % num_heads != 0:
+            raise ValueError("model_dim must be divisible by num_heads")
+        if num_heads % num_kv_heads != 0:
+            raise ValueError("num_heads must be divisible by num_kv_heads")
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = dim // num_heads
+        if self.head_dim % 2 != 0:
+            raise ValueError("head_dim must be even for RoPE")
+        kv_dim = self.num_kv_heads * self.head_dim
+        self.c_q = CastedLinear(dim, dim, bias=False)
+        self.c_k = CastedLinear(dim, kv_dim, bias=False)
+        self.c_v = CastedLinear(dim, kv_dim, bias=False)
+        self.proj = CastedLinear(dim, dim, bias=False)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rope_dims = 0
+        self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=train_seq_len)
+        self.use_xsa = False
+    def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor:
+        B, T, H, D = y.shape
+        Hkv = v.size(-2)
+        group = H // Hkv
+        y_g = y.reshape(B, T, Hkv, group, D)
+        vn = F.normalize(v, dim=-1).unsqueeze(-2)
+        proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn
+        return (y_g - proj).reshape(B, T, H, D)
+    def forward(self, x: Tensor, v_embed: Tensor | None = None) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim)
+        k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        v = self.c_v(x)
+        if v_embed is not None:
+            v = v + v_embed
+        v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        q = F.rms_norm(q, (q.size(-1),))
+        k = F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q = apply_rotary_emb(q, cos, sin, self.rope_dims)
+        k = apply_rotary_emb(k, cos, sin, self.rope_dims)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None]
+        y = flash_attn_3_func(q, k, v, causal=True)
+        if self.use_xsa:
+            y = self._xsa_efficient(y, v)
+        y = y.reshape(bsz, seqlen, dim)
+        return self.proj(y)
+class ValueEmbedding(nn.Module):
+    def __init__(self, vocab_size: int, ve_dim: int, model_dim: int):
+        super().__init__()
+        self.embed = nn.Embedding(vocab_size, ve_dim)
+        nn.init.normal_(self.embed.weight, std=0.01)
+        self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None
+        if self.proj is not None:
+            nn.init.zeros_(self.proj.weight)
+        self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32))
+    def forward(self, token_ids: Tensor) -> Tensor:
+        h = self.embed(token_ids)
+        if self.proj is not None:
+            h = self.proj(h)
+        return h * self.scale.to(dtype=h.dtype)
+class MLP(nn.Module):
+    def __init__(self, dim: int, mlp_mult: int):
+        super().__init__()
+        hidden = int(mlp_mult * dim)
+        self.fc = CastedLinear(dim, hidden, bias=False)
+        self.proj = CastedLinear(hidden, dim, bias=False)
+        self.proj._zero_init = True
+    def forward(self, x: Tensor) -> Tensor:
+        return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square())
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int,
+                 rope_base: float, qk_gain_init: float, train_seq_len: int,
+                 layer_idx: int = 0, ln_scale: bool = False):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, train_seq_len)
+        self.mlp = MLP(dim, mlp_mult)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+        self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0
+    def forward(self, x: Tensor, x0: Tensor, v_embed: Tensor | None = None) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0
+        attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed)
+        x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out
+        x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor)
+        return x_out
+class GPT(nn.Module):
+    def __init__(self, h: Hyperparameters):
+        super().__init__()
+        self._ve_target_dim = h.num_kv_heads * (h.model_dim // h.num_heads)
+        if h.logit_softcap <= 0.0:
+            raise ValueError(f"logit_softcap must be positive, got {h.logit_softcap}")
+        self.tie_embeddings = h.tie_embeddings
+        self.tied_embed_init_std = h.tied_embed_init_std
+        self.logit_softcap = h.logit_softcap
+        self._recur_active = False
+        self._recur_set = set()
+        if h.recur_layers:
+            self._recur_set = {int(x) for x in h.recur_layers.split(",") if x.strip()}
+        self.tok_emb = nn.Embedding(h.vocab_size, h.embedding_dim)
+        if h.embedding_dim != h.model_dim:
+            self.embed_proj = CastedLinear(h.embedding_dim, h.model_dim, bias=False)
+            self.head_proj = CastedLinear(h.model_dim, h.embedding_dim, bias=False)
+        else:
+            self.embed_proj = None
+            self.head_proj = None
+        self.num_encoder_layers = h.num_layers // 2
+        self.num_decoder_layers = h.num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, h.model_dim, dtype=torch.float32))
+        self.skip_gates = nn.Parameter(torch.zeros(self.num_skip_weights, h.model_dim, dtype=torch.float32)) if h.skip_gates_enabled else None
+        self.blocks = nn.ModuleList([
+            Block(h.model_dim, h.num_heads, h.num_kv_heads, h.mlp_mult, h.rope_base,
+                  h.qk_gain_init, h.train_seq_len, layer_idx=i, ln_scale=h.ln_scale)
+            for i in range(h.num_layers)
+        ])
+        if h.rope_dims > 0:
+            head_dim = h.model_dim // h.num_heads
+            for block in self.blocks:
+                block.attn.rope_dims = h.rope_dims
+                block.attn.rotary = Rotary(head_dim, base=h.rope_base, train_seq_len=h.train_seq_len, rope_dims=h.rope_dims)
+        self.ve_layer_indices = [int(x) for x in h.ve_layers.split(",") if x.strip()] if h.ve_enabled else []
+        kv_dim = self._ve_target_dim
+        if self.ve_layer_indices:
+            self.ve_shared = ValueEmbedding(h.vocab_size, h.ve_dim, kv_dim)
+            self.ve_layer_scales = nn.ParameterList(
+                [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]
+            )
+        else:
+            self.ve_shared = None
+            self.ve_layer_scales = nn.ParameterList()
+        self.value_embeds = nn.ModuleList()
+        self.final_norm = RMSNorm()
+        self.lm_head = None if h.tie_embeddings else CastedLinear(h.embedding_dim, h.vocab_size, bias=False)
+        if self.lm_head is not None:
+            self.lm_head._zero_init = True
+        if h.xsa_last_n > 0:
+            for i in range(max(0, h.num_layers - h.xsa_last_n), h.num_layers):
+                self.blocks[i].attn.use_xsa = True
+        self._init_weights()
+    def _init_weights(self) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std)
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                if getattr(module, "_zero_init", False):
+                    nn.init.zeros_(module.weight)
+                elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64:
+                    nn.init.orthogonal_(module.weight, gain=1.0)
+    def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None:
+        if self.ve_shared is None or layer_idx not in self.ve_layer_indices:
+            return None
+        if ve_cache is not None and 've' not in ve_cache:
+            ve_cache['ve'] = self.ve_shared(input_ids)
+        ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids)
+        ve_idx = self.ve_layer_indices.index(layer_idx)
+        return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype)
+    def forward_backbone(self, input_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        if self.embed_proj is not None:
+            x = self.embed_proj(x)
+        x0 = x
+        skips: list[Tensor] = []
+        ve_cache: dict = {}
+        schedule: list[int] = []
+        recur_sorted = sorted(self._recur_set) if (self._recur_active and self._recur_set) else []
+        max_recur = max(recur_sorted) if recur_sorted else -1
+        for i in range(len(self.blocks)):
+            schedule.append(i)
+            if i == max_recur:
+                schedule.extend(recur_sorted)
+        seen: set[int] = set()
+        for li in schedule:
+            is_first = li not in seen
+            seen.add(li)
+            is_encoder = li < self.num_encoder_layers
+            if is_encoder:
+                ve = self._get_ve(li, input_ids, ve_cache)
+                x = self.blocks[li](x, x0, v_embed=ve)
+                if is_first:
+                    skips.append(x)
+            else:
+                di = li - self.num_encoder_layers
+                if is_first and skips:
+                    scaled_skip = self.skip_weights[di].to(dtype=x.dtype)[None, None, :] * skips.pop()
+                    if self.skip_gates is not None:
+                        g = torch.sigmoid(self.skip_gates[di].to(dtype=x.dtype))[None, None, :]
+                        x = torch.lerp(scaled_skip, x, g)
+                    else:
+                        x = x + scaled_skip
+                ve = self._get_ve(li, input_ids, ve_cache)
+                x = self.blocks[li](x, x0, v_embed=ve)
+        x = self.final_norm(x)
+        return x
+    def forward_logits(self, input_ids: Tensor) -> Tensor:
+        x = self.forward_backbone(input_ids)
+        if self.head_proj is not None:
+            x = self.head_proj(x)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            logits_proj = self.lm_head(x)
+        return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+    def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor:
+        logits = self.forward_logits(input_ids)
+        return F.cross_entropy(
+            logits.reshape(-1, logits.size(-1)).float(), target_ids.reshape(-1), reduction="mean")
+def classify_param(name: str) -> str:
+    if "tok_emb" in name or "lm_head" in name:
+        return "embed"
+    if ".mlp." in name:
+        return "mlp"
+    if ".attn." in name or (".proj." in name and ".mlp." not in name):
+        return "attn"
+    return "other"
+_PE_COEFFS = [
+    (8.156554524902461, -22.48329292557795, 15.878769915207462),
+    (4.042929935166739, -2.808917465908714, 0.5000178451051316),
+    (3.8916678022926607, -2.772484153217685, 0.5060648178503393),
+    (3.285753657755655, -2.3681294933425376, 0.46449024233003106),
+    (2.3465413258596377, -1.7097828382687081, 0.42323551169305323),
+]
+@torch.compile
+def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for a, b, c in _PE_COEFFS[:steps]:
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int,
+                 nesterov: bool = True, weight_decay: float = 0.0):
+        super().__init__(
+            params,
+            dict(lr=lr, momentum=momentum, backend_steps=backend_steps,
+                 nesterov=nesterov, weight_decay=weight_decay),
+        )
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr = group["lr"]
+            momentum = group["momentum"]
+            backend_steps = group["backend_steps"]
+            nesterov = group["nesterov"]
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = g / (g.norm(dim=-1, keepdim=True).clamp_min(1e-7))
+                    g = zeropower_via_newtonschulz5(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr : curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+            wd = group.get("weight_decay", 0.0)
+            curr = 0
+            for p in params:
+                if wd > 0.0:
+                    p.data.mul_(1.0 - lr * wd)
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+class Optimizers():
+    def __init__(self, h: Hyperparameters, base_model: GPT):
+        block_named_params = list(base_model.blocks.named_parameters())
+        matrix_params = [
+            p
+            for name, p in block_named_params
+            if p.ndim == 2 and not any(pattern in name for pattern in
+                                       CONTROL_TENSOR_NAME_PATTERNS)
+        ]
+        scalar_params = [
+            p
+            for name, p in block_named_params
+            if p.ndim < 2 or any(pattern in name for pattern in
+                                 CONTROL_TENSOR_NAME_PATTERNS)
+        ]
+        if base_model.skip_weights.numel() > 0:
+            scalar_params.append(base_model.skip_weights)
+        if base_model.skip_gates is not None and base_model.skip_gates.numel() > 0:
+            scalar_params.append(base_model.skip_gates)
+        token_lr = h.tied_embed_lr if h.tie_embeddings else h.embed_lr
+        tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}]
+        if base_model.ve_shared is not None:
+            tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr})
+            if base_model.ve_shared.proj is not None:
+                matrix_params.append(base_model.ve_shared.proj.weight)
+            scalar_params.append(base_model.ve_shared.scale)
+            for s in base_model.ve_layer_scales:
+                scalar_params.append(s)
+        self.optimizer_tok = torch.optim.AdamW(
+            tok_params,
+            betas=(h.beta1, h.beta2),
+            eps=h.adam_eps,
+            weight_decay=h.embed_wd,
+            fused=True,
+        )
+        self.optimizer_muon = Muon(
+            matrix_params,
+            lr=h.matrix_lr,
+            momentum=h.muon_momentum,
+            backend_steps=h.muon_backend_steps,
+            weight_decay=h.muon_wd,
+        )
+        for group in self.optimizer_muon.param_groups:
+            group["base_lr"] = h.matrix_lr
+        self.optimizer_scalar = torch.optim.AdamW(
+            [{"params": scalar_params, "lr": h.scalar_lr, "base_lr": h.scalar_lr}],
+            betas=(h.beta1, h.beta2),
+            eps=h.adam_eps,
+            weight_decay=h.adam_wd,
+            fused=True,
+        )
+        self.optimizers: list[torch.optim.Optimizer] = [self.optimizer_tok, self.optimizer_muon, self.optimizer_scalar]
+        if base_model.lm_head is not None:
+            self.optimizer_head = torch.optim.Adam(
+                [{"params": [base_model.lm_head.weight], "lr": h.head_lr, "base_lr": h.head_lr}],
+                betas=(h.beta1, h.beta2),
+                eps=h.adam_eps,
+                fused=True,
+            )
+            self.optimizers.insert(1, self.optimizer_head)
+        else:
+            self.optimizer_head = None
+    def __iter__(self):
+        return iter(self.optimizers)
+    def zero_grad_all(self) -> None:
+        for opt in self.optimizers:
+            opt.zero_grad(set_to_none=True)
+    def step(self):
+        for opt in self.optimizers:
+            opt.step()
+        self.zero_grad_all()
+CONTROL_TENSOR_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "CONTROL_TENSOR_NAME_PATTERNS",
+        "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,skip_gates,ve_layer_scales,ve_shared.scale",
+    ).split(",")
+    if pattern
+)
+INT8_PER_ROW_SCALE_DTYPE = torch.float16
+INT8_CLIP_PERCENTILE = 99.99984
+INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0
+def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        clip_abs = (
+            torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1)
+            if t32.numel()
+            else torch.empty((t32.shape[0],), dtype=torch.float32)
+        )
+        clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None])
+        scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0)
+        q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous()
+        return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous()
+    clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0
+    scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32)
+    q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous()
+    return q, scale
+def restore_fp32_params(model: nn.Module) -> None:
+    for module in model.modules():
+        if isinstance(module, CastedLinear):
+            module.float()
+    for name, param in model.named_parameters():
+        if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32:
+            param.data = param.data.float()
+def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        best_q, best_s, best_err = None, None, float('inf')
+        for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]:
+            if pct < 1.0:
+                row_clip = torch.quantile(t32.abs(), pct, dim=1)
+            else:
+                row_clip = t32.abs().amax(dim=1)
+            s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16)
+            q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8)
+            recon = q.float() * s.float()[:, None]
+            err = (t32 - recon).pow(2).mean().item()
+            if err < best_err:
+                best_q, best_s, best_err = q, s, err
+        return best_q, best_s
+    amax = t32.abs().max().item()
+    scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16)
+    q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8)
+    return q, scale
+def collect_hessians(
+    model: nn.Module,
+    train_loader: DistributedTokenLoader,
+    h: Hyperparameters,
+    device: torch.device,
+    n_calibration_batches: int = 64,
+) -> dict[str, Tensor]:
+    hessians: dict[str, Tensor] = {}
+    hooks = []
+    def make_hook(name: str):
+        def hook_fn(module, inp, out):
+            x = inp[0].detach().float()
+            if x.ndim == 3:
+                x = x.reshape(-1, x.shape[-1])
+            if name not in hessians:
+                hessians[name] = torch.zeros(
+                    x.shape[1], x.shape[1], dtype=torch.float32, device=device
+                )
+            hessians[name].addmm_(x.T, x)
+        return hook_fn
+    for name, module in model.named_modules():
+        if isinstance(module, CastedLinear) and module.weight.numel() > 65536:
+            cat = classify_param(name + ".weight")
+            if cat in ("mlp", "attn"):
+                hooks.append(module.register_forward_hook(make_hook(name + ".weight")))
+    model.eval()
+    with torch.no_grad():
+        for i in range(n_calibration_batches):
+            x, y = train_loader.next_batch(
+                h.train_batch_tokens,
+                h.train_seq_len, h.grad_accum_steps,
+            )
+            model.forward_logits(x)
+    for h in hooks:
+        h.remove()
+    for name in hessians:
+        hessians[name] = hessians[name].cpu() / n_calibration_batches
+    return hessians
+def gptq_quantize_weight(
+    w: Tensor,
+    H: Tensor,
+    clip_range: int = 31,
+    block_size: int = 128,
+) -> tuple[Tensor, Tensor]:
+    W_orig = w.float().clone()
+    rows, cols = W_orig.shape
+    H = H.float().clone()
+    dead = torch.diag(H) == 0
+    H[dead, dead] = 1
+    damp = 0.01 * H.diag().mean()
+    H.diagonal().add_(damp)
+    perm = torch.argsort(H.diag(), descending=True)
+    invperm = torch.argsort(perm)
+    W_perm = W_orig[:, perm].clone()
+    W_perm[:, dead[perm]] = 0
+    H = H[perm][:, perm]
+    try:
+        Hinv = torch.cholesky_inverse(torch.linalg.cholesky(H))
+        Hinv = torch.linalg.cholesky(Hinv, upper=True)
+    except torch.linalg.LinAlgError:
+        return quantize_int6_per_row(W_orig, clip_range)
+    best_q, best_scale, best_err = None, None, float('inf')
+    for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]:
+        if pct < 1.0:
+            row_clip = torch.quantile(W_orig.abs(), pct, dim=1)
+        else:
+            row_clip = W_orig.abs().amax(dim=1)
+        s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16)
+        sf = s.float()
+        Q = torch.zeros(rows, cols, dtype=torch.int8)
+        W_work = W_perm.clone()
+        for i1 in range(0, cols, block_size):
+            i2 = min(i1 + block_size, cols)
+            W_block = W_work[:, i1:i2].clone()
+            Hinv_block = Hinv[i1:i2, i1:i2]
+            Err = torch.zeros(rows, i2 - i1)
+            for j in range(i2 - i1):
+                w_col = W_block[:, j]
+                d = Hinv_block[j, j]
+                q_col = torch.clamp(torch.round(w_col / sf), -clip_range, clip_range)
+                Q[:, i1 + j] = q_col.to(torch.int8)
+                err = (w_col - q_col.float() * sf) / d
+                Err[:, j] = err
+                W_block[:, j:] -= err.unsqueeze(1) * Hinv_block[j, j:].unsqueeze(0)
+            if i2 < cols:
+                W_work[:, i2:] -= Err @ Hinv[i1:i2, i2:]
+        recon = Q.float() * sf[:, None]
+        mse = (W_perm - recon).pow(2).mean().item()
+        if mse < best_err:
+            best_q, best_scale, best_err = Q, s, mse
+    return best_q[:, invperm], best_scale
+def gptq_mixed_quantize_int6(
+    state_dict: dict[str, Tensor],
+    int6_cats: set[str],
+    hessians: dict[str, Tensor],
+) -> tuple[dict[str, Tensor], dict[str, object]]:
+    result: dict[str, Tensor] = {}
+    meta: dict[str, object] = {}
+    gptq_count = 0
+    fallback_count = 0
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().contiguous()
+        cat = classify_param(name)
+        if not t.is_floating_point() or t.numel() <= 65536:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = "passthrough"
+            continue
+        if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS):
+            result[name] = t.float()
+            meta[name] = "passthrough_ctrl"
+            continue
+        if cat in int6_cats and t.ndim == 2:
+            if name in hessians:
+                q, s = gptq_quantize_weight(t, hessians[name])
+                gptq_count += 1
+                meta[name] = {"type": "int6", "method": "gptq"}
+            else:
+                q, s = quantize_int6_per_row(t)
+                fallback_count += 1
+                meta[name] = {"type": "int6", "method": "clip_search"}
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+        elif cat in int6_cats and t.ndim >= 1:
+            q, s = quantize_int6_per_row(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int6"}
+        else:
+            q, s = quantize_float_tensor(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int8"}
+    log(f"GPTQ quantization: {gptq_count} layers with full GPTQ, {fallback_count} fallback to clip-search")
+    return result, meta
+def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]):
+    result: dict[str, Tensor] = {}
+    meta: dict[str, object] = {}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().contiguous()
+        cat = classify_param(name)
+        if not t.is_floating_point() or t.numel() <= 65536:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = "passthrough"
+            continue
+        if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS):
+            result[name] = t.float()
+            meta[name] = "passthrough_ctrl"
+            continue
+        if cat in int6_cats and t.ndim >= 1:
+            q, s = quantize_int6_per_row(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int6"}
+        else:
+            q, s = quantize_float_tensor(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int8"}
+    return result, meta
+def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object],
+                          template_sd: dict[str, Tensor]) -> dict[str, Tensor]:
+    out: dict[str, Tensor] = {}
+    for name, orig in template_sd.items():
+        info = meta.get(name)
+        if info is None:
+            continue
+        orig_dtype = orig.dtype
+        if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"):
+            t = result[name]
+            if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16):
+                t = t.to(orig_dtype)
+            out[name] = t
+            continue
+        q, s = result[name + ".q"], result[name + ".scale"]
+        if s.ndim > 0:
+            out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype)
+        else:
+            out[name] = (q.float() * float(s.item())).to(orig_dtype)
+    return out
+_BSHF_MAGIC = b"BSHF"
+def _byte_shuffle(data: bytes, stride: int = 2) -> bytes:
+    if stride <= 1 or len(data) < stride:
+        return data
+    src = np.frombuffer(data, dtype=np.uint8)
+    n = len(src)
+    out = np.empty(n, dtype=np.uint8)
+    dest_off = 0
+    for pos in range(stride):
+        chunk = src[pos::stride]
+        out[dest_off:dest_off + len(chunk)] = chunk
+        dest_off += len(chunk)
+    return _BSHF_MAGIC + bytes([stride]) + out.tobytes()
+def _byte_unshuffle(data: bytes) -> bytes:
+    if len(data) < 5 or data[:4] != _BSHF_MAGIC:
+        return data
+    stride = data[4]
+    if stride < 2:
+        return data[5:]
+    payload = np.frombuffer(data, dtype=np.uint8, offset=5)
+    n = len(payload)
+    out = np.empty(n, dtype=np.uint8)
+    src_off = 0
+    for pos in range(stride):
+        chunk_len = n // stride + (1 if pos < n % stride else 0)
+        out[pos::stride][:chunk_len] = payload[src_off:src_off + chunk_len]
+        src_off += chunk_len
+    return out.tobytes()
+def _compress(data: bytes, compressor: str, byte_shuffle: bool = True) -> bytes:
+    if byte_shuffle:
+        data = _byte_shuffle(data)
+    if compressor == "lzma":
+        return lzma.compress(data, preset=6)
+    elif compressor == "brotli":
+        import brotli
+        return brotli.compress(data, quality=11)
+    raise ValueError(f"Unknown compressor: {compressor!r}")
+def _decompress(data: bytes, compressor: str, byte_shuffle: bool = True) -> bytes:
+    if compressor == "lzma":
+        raw = lzma.decompress(data)
+    elif compressor == "brotli":
+        import brotli
+        raw = brotli.decompress(data)
+    if byte_shuffle:
+        raw = _byte_unshuffle(raw)
+    return raw
+    raise ValueError(f"Unknown compressor: {compressor!r}")
+def serialize(h: Hyperparameters, base_model: torch.nn.Module, code: str) -> int:
+    model_bytes = None
+    code_bytes = len(code.encode("utf-8"))
+    if h.is_main_process:
+        torch.save(base_model.state_dict(), h.model_path)
+        model_bytes = os.path.getsize(h.model_path)
+        log(f"Serialized model: {model_bytes} bytes")
+        log(f"Code size: {code_bytes} bytes")
+    sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()}
+    if h.gptq_enabled:
+        log("GPTQ:collecting Hessians from calibration data...")
+        t0 = time.perf_counter()
+        calib_loader = DistributedTokenLoader(h.train_files, h.rank, h.world_size,
+                                              torch.device("cuda", h.local_rank))
+        hessians = collect_hessians(
+            base_model, calib_loader, h,
+            torch.device("cuda", h.local_rank),
+            n_calibration_batches=h.gptq_calibration_batches,
+        )
+        log(f"GPTQ:collected {len(hessians)} Hessians in {time.perf_counter() - t0:.1f}s")
+        quant_result, quant_meta = gptq_mixed_quantize_int6(sd_cpu, {"mlp", "attn"}, hessians)
+    else:
+        quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn"})
+    quant_buf = io.BytesIO()
+    torch.save({"w": quant_result, "m": quant_meta}, quant_buf)
+    quant_raw = quant_buf.getvalue()
+    quant_blob = _compress(quant_raw, h.compressor)
+    quant_file_bytes = len(quant_blob)
+    bytes_total = quant_file_bytes + code_bytes
+    if h.is_main_process:
+        with open(h.quantized_model_path, "wb") as f:
+            f.write(quant_blob)
+        log(f"Serialized model int6+{h.compressor}: {quant_file_bytes} bytes")
+        log(f"Total submission size int6+{h.compressor}: {bytes_total} bytes")
+def deserialize(h: Hyperparameters, device: torch.device) -> GPT:
+    eval_model = GPT(h).to(device).bfloat16()
+    restore_fp32_params(eval_model)
+    sd_cpu = {k: v.detach().cpu() for k, v in eval_model.state_dict().items()}
+    with open(h.quantized_model_path, "rb") as f:
+        quant_blob_disk = f.read()
+    quant_state = torch.load(
+        io.BytesIO(_decompress(quant_blob_disk, h.compressor)),
+        map_location="cpu",
+    )
+    deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu)
+    eval_model.load_state_dict(deq_state, strict=True)
+    return eval_model
+def _loss_bpb(loss_sum, token_count, byte_count) -> tuple[float, float]:
+    val_loss = (loss_sum / token_count).item()
+    val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item())
+    return val_loss, val_bpb
+def eval_val(
+    h: Hyperparameters,
+    device: torch.device,
+    val_data: ValidationData,
+    model: nn.Module
+) -> tuple[float, float]:
+    seq_len = h.eval_seq_len
+    local_batch_tokens = h.val_batch_tokens // (h.world_size * h.grad_accum_steps)
+    if local_batch_tokens < seq_len:
+        raise ValueError(
+            "VAL_BATCH_SIZE must provide at least one sequence per rank; "
+            f"got VAL_BATCH_SIZE={h.val_batch_tokens}, WORLD_SIZE={h.world_size}, "
+            f"GRAD_ACCUM_STEPS={h.grad_accum_steps}, seq_len={seq_len}"
+        )
+    local_batch_seqs = local_batch_tokens // seq_len
+    total_seqs = (val_data.val_tokens.numel() - 1) // seq_len
+    seq_start = (total_seqs * h.rank) // h.world_size
+    seq_end = (total_seqs * (h.rank + 1)) // h.world_size
+    val_loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    val_token_count = torch.zeros((), device=device, dtype=torch.float64)
+    val_byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    model.eval()
+    with torch.inference_mode():
+        for batch_seq_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end)
+            raw_start = batch_seq_start * seq_len
+            raw_end = batch_seq_end * seq_len + 1
+            local = val_data.val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True)
+            x = local[:-1].reshape(-1, seq_len)
+            y = local[1:].reshape(-1, seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                batch_loss = model(x, y).detach()
+            batch_token_count = float(y.numel())
+            val_loss_sum += batch_loss.to(torch.float64) * batch_token_count
+            val_token_count += batch_token_count
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            token_bytes = val_data.base_bytes_lut[tgt_ids].to(dtype=torch.int16)
+            token_bytes += (val_data.has_leading_space_lut[tgt_ids] & ~val_data.is_boundary_token_lut[prev_ids]).to(dtype=torch.int16)
+            val_byte_count += token_bytes.to(torch.float64).sum()
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM)
+    model.train()
+    return _loss_bpb(val_loss_sum, val_token_count, val_byte_count)
+def eval_val_sliding(
+    h: Hyperparameters,
+    device: torch.device,
+    val_data: ValidationData,
+    base_model: nn.Module,
+    batch_seqs: int = 32
+) -> tuple[float, float]:
+    base_model.eval()
+    logits_fn = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True)
+    seq_len = h.eval_seq_len
+    context_size = seq_len - h.eval_stride
+    total_tokens = val_data.val_tokens.numel() - 1
+    window_starts = [ws for ws in range(0, total_tokens, h.eval_stride)
+                     if ws + context_size < total_tokens]
+    total_windows = len(window_starts)
+    my_s = (total_windows * h.rank) // h.world_size
+    my_e = (total_windows * (h.rank + 1)) // h.world_size
+    my_windows = window_starts[my_s:my_e]
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    with torch.inference_mode():
+        for bi in range(0, len(my_windows), batch_seqs):
+            batch_ws = my_windows[bi:bi + batch_seqs]
+            bsz = len(batch_ws)
+            x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            wlens: list[int] = []
+            for i, ws in enumerate(batch_ws):
+                we = min(ws + seq_len, total_tokens)
+                wlen = we - ws
+                wlens.append(wlen)
+                chunk = val_data.val_tokens[ws:we + 1].to(dtype=torch.int64, device=device)
+                x_batch[i, :wlen] = chunk[:-1]
+                y_batch[i, :wlen] = chunk[1:]
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                logits = logits_fn(x_batch)
+            nll = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1),
+                reduction="none",
+            ).reshape(bsz, seq_len)
+            for i, ws in enumerate(batch_ws):
+                wlen = wlens[i]
+                s = 0 if ws == 0 else context_size
+                scored_nll = nll[i, s:wlen].to(torch.float64)
+                loss_sum += scored_nll.sum()
+                token_count += float(wlen - s)
+                tgt = y_batch[i, s:wlen]
+                prev = x_batch[i, s:wlen]
+                tb = val_data.base_bytes_lut[tgt].to(torch.float64)
+                tb += (val_data.has_leading_space_lut[tgt] & ~val_data.is_boundary_token_lut[prev]).to(torch.float64)
+                byte_count += tb.sum()
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+    base_model.train()
+    return _loss_bpb(loss_sum, token_count, byte_count)
+def timed_eval(label: str, fn, *args, **kwargs) -> tuple[float, float]:
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+    val_loss, val_bpb = fn(*args, **kwargs)
+    torch.cuda.synchronize()
+    elapsed_ms = 1000.0 * (time.perf_counter() - t0)
+    log(f"{label} val_loss:{val_loss:.8f} val_bpb:{val_bpb:.8f} eval_time:{elapsed_ms:.0f}ms")
+    return val_loss, val_bpb
+def eval_val_slot(
+    h: Hyperparameters,
+    device: torch.device,
+    val_data: ValidationData,
+    base_model: GPT,
+    batch_seqs: int = 32,
+) -> tuple[float, float]:
+    base_model.eval()
+    seq_len = h.eval_seq_len
+    context_size = seq_len - h.eval_stride
+    total_tokens = val_data.val_tokens.numel() - 1
+    window_starts = [ws for ws in range(0, total_tokens, h.eval_stride)
+                     if ws + context_size < total_tokens]
+    total_windows = len(window_starts)
+    my_s = (total_windows * h.rank) // h.world_size
+    my_e = (total_windows * (h.rank + 1)) // h.world_size
+    my_windows = window_starts[my_s:my_e]
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+    d_model = h.model_dim
+    for bi in range(0, len(my_windows), batch_seqs):
+        batch_ws = my_windows[bi:bi + batch_seqs]
+        bsz = len(batch_ws)
+        x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+        y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+        wlens: list[int] = []
+        for i, ws in enumerate(batch_ws):
+            we = min(ws + seq_len, total_tokens)
+            wlen = we - ws
+            wlens.append(wlen)
+            chunk = val_data.val_tokens[ws:we + 1].to(dtype=torch.int64, device=device)
+            x_batch[i, :wlen] = chunk[:-1]
+            y_batch[i, :wlen] = chunk[1:]
+        with torch.no_grad(), torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+            hidden = base_model.forward_backbone(x_batch).detach()
+        delta = torch.zeros(bsz, 1, d_model, device=device, dtype=hidden.dtype, requires_grad=True)
+        slot_opt = torch.optim.AdamW([delta], lr=h.slot_lr)
+        for _ in range(h.slot_steps):
+            slot_opt.zero_grad()
+            h_shifted = hidden + delta
+            if base_model.head_proj is not None:
+                h_out = base_model.head_proj(h_shifted)
+            else:
+                h_out = h_shifted
+            if base_model.tie_embeddings:
+                logits = F.linear(h_out, base_model.tok_emb.weight)
+            else:
+                logits = base_model.lm_head(h_out)
+            logits = base_model.logit_softcap * torch.tanh(logits / base_model.logit_softcap)
+            slot_loss = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1),
+                reduction="mean",
+            )
+            slot_loss.backward()
+            slot_opt.step()
+        with torch.no_grad():
+            h_shifted = hidden + delta
+            if base_model.head_proj is not None:
+                h_out = base_model.head_proj(h_shifted)
+            else:
+                h_out = h_shifted
+            if base_model.tie_embeddings:
+                logits = F.linear(h_out, base_model.tok_emb.weight)
+            else:
+                logits = base_model.lm_head(h_out)
+            logits = base_model.logit_softcap * torch.tanh(logits / base_model.logit_softcap)
+            nll = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1),
+                reduction="none",
+            ).reshape(bsz, seq_len)
+            for i, ws in enumerate(batch_ws):
+                wlen = wlens[i]
+                s = 0 if ws == 0 else context_size
+                scored_nll = nll[i, s:wlen].to(torch.float64)
+                loss_sum += scored_nll.sum()
+                token_count += float(wlen - s)
+                tgt = y_batch[i, s:wlen]
+                prev = x_batch[i, s:wlen]
+                tb = val_data.base_bytes_lut[tgt].to(torch.float64)
+                tb += (val_data.has_leading_space_lut[tgt] & ~val_data.is_boundary_token_lut[prev]).to(torch.float64)
+                byte_count += tb.sum()
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+    base_model.train()
+    return _loss_bpb(loss_sum, token_count, byte_count)
+def run_evals(
+    h: Hyperparameters,
+    device: torch.device,
+    val_data: ValidationData,
+    eval_model: torch.nn.Module
+):
+    compiled_model = torch.compile(eval_model, dynamic=False, fullgraph=True)
+    timed_eval("final_int6_roundtrip", eval_val, h, device, val_data, compiled_model)
+    if h.sliding_window_enabled:
+        timed_eval("final_int6_sliding_window", eval_val_sliding, h, device, val_data, eval_model)
+    if h.slot_enabled:
+        timed_eval("final_int6_slot", eval_val_slot, h, device, val_data, eval_model)
+def train_model(h: Hyperparameters, device: torch.device, val_data: ValidationData) -> None:
+    base_model = GPT(h).to(device).bfloat16()
+    restore_fp32_params(base_model)
+    compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True)
+    if h.distributed:
+        model = DDP(compiled_model, device_ids=[h.local_rank], broadcast_buffers=False)
+    else:
+        model = compiled_model
+    log(f"model_params:{sum(p.numel() for p in base_model.parameters())}")
+    optimizers = Optimizers(h, base_model)
+    train_loader = DistributedTokenLoader( h.train_files, h.rank, h.world_size, device)
+    max_wallclock_ms = 1000.0 * h.max_wallclock_seconds if h.max_wallclock_seconds > 0 else None
+    if h.gptq_enabled and max_wallclock_ms is not None:
+        max_wallclock_ms -= h.gptq_reserve_seconds * 1000.0
+        log(f"gptq:reserving {h.gptq_reserve_seconds:.0f}s, effective={max_wallclock_ms:.0f}ms")
+    def training_frac(step: int, elapsed_ms: float) -> float:
+        if max_wallclock_ms is None:
+            return step / max(h.iterations, 1)
+        return elapsed_ms / max(max_wallclock_ms, 1e-9)
+    def lr_mul(frac: float) -> float:
+        if h.warmdown_frac <= 0:
+            return 1.0
+        if frac >= 1.0 - h.warmdown_frac:
+            return max((1.0 - frac) / h.warmdown_frac, h.min_lr)
+        return 1.0
+    def step_fn(step, lr_scale):
+        optimizers.zero_grad_all()
+        train_loss = torch.zeros((), device=device)
+        for micro_step in range(h.grad_accum_steps):
+            if h.distributed:
+                model.require_backward_grad_sync = micro_step == h.grad_accum_steps - 1
+            x, y = train_loader.next_batch(h.train_batch_tokens, h.train_seq_len, h.grad_accum_steps)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                loss = model(x, y)
+            train_loss += loss.detach()
+            (loss / h.grad_accum_steps).backward()
+        train_loss /= h.grad_accum_steps
+        frac = min(step / h.muon_momentum_warmup_steps, 1.0) if h.muon_momentum_warmup_steps > 0 else 1.0
+        muon_momentum = (1 - frac) * h.muon_momentum_warmup_start + frac * h.muon_momentum
+        for group in optimizers.optimizer_muon.param_groups:
+            group["momentum"] = muon_momentum
+        for opt in optimizers:
+            for group in opt.param_groups:
+                group["lr"] = group["base_lr"] * lr_scale
+        if h.grad_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(base_model.parameters(), h.grad_clip_norm)
+        optimizers.step()
+        return train_loss
+    if h.warmup_steps > 0:
+        initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()}
+        initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers]
+        model.train()
+        for warmup_step in range(h.warmup_steps):
+            step_fn(warmup_step, 1.0)
+            if warmup_step <= 5 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == h.warmup_steps:
+                log(f"warmup_step: {warmup_step + 1}/{h.warmup_steps}")
+        base_model.load_state_dict(initial_model_state, strict=True)
+        for opt, state in zip(optimizers, initial_optimizer_states, strict=True):
+            opt.load_state_dict(state)
+        optimizers.zero_grad_all()
+        if h.distributed:
+            model.require_backward_grad_sync = True
+        train_loader = DistributedTokenLoader(
+            h.train_files, h.rank, h.world_size, device)
+    ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()}
+    ema_decay = h.ema_decay
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+    step = 0
+    while True:
+        last_step = step == h.iterations or (stop_after_step is not None and step >= stop_after_step)
+        should_validate = last_step or (h.val_loss_every > 0 and step % h.val_loss_every == 0)
+        if should_validate:
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb = eval_val(h, device, val_data, model)
+            log(f"{step}/{h.iterations} val_loss: {val_loss:.4f} val_bpb: {val_bpb:.4f}")
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+        if last_step:
+            if stop_after_step is not None and step < h.iterations:
+                log(
+                    f"stopping_early: wallclock_cap train_time: {training_time_ms:.0f}ms "
+                    f"step: {step}/{h.iterations}"
+                )
+            break
+        if not base_model._recur_active and base_model._recur_set and step >= h.recur_start_step:
+            base_model._recur_active = True
+            log(f"depth_recurrence:activated at step {step}, layers={sorted(base_model._recur_set)}")
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        frac = training_frac(step, elapsed_ms)
+        scale = lr_mul(frac)
+        train_loss = step_fn(step, scale)
+        with torch.no_grad():
+            for name, t in base_model.state_dict().items():
+                ema_state[name].mul_(ema_decay).add_(t.detach().float(), alpha=1.0 - ema_decay)
+        step += 1
+        approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        should_log_train = (
+            h.train_log_every > 0
+            and (step <= 5 or step % h.train_log_every == 0 or stop_after_step is not None)
+        )
+        if should_log_train:
+            tok_per_sec = step * h.train_batch_tokens / (approx_training_time_ms / 1000.0)
+            log(
+                f"{step}/{h.iterations} train_loss: {train_loss.item():.4f} "
+                f"train_time: {approx_training_time_ms / 60000:.1f}m tok/s: {tok_per_sec:.0f}"
+            )
+        reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms
+        if h.distributed and max_wallclock_ms is not None:
+            reached_cap_tensor = torch.tensor(int(reached_cap), device=device)
+            dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX)
+            reached_cap = bool(reached_cap_tensor.item())
+        if stop_after_step is None and reached_cap:
+            stop_after_step = step
+    log(
+        f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
+        f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB"
+    )
+    log("ema:applying EMA weights")
+    current_state = base_model.state_dict()
+    avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()}
+    base_model.load_state_dict(avg_state, strict=True)
+    return base_model, compiled_model
+def train_and_eval(h: Hyperparameters, device: torch.device) -> None:
+    random.seed(h.seed)
+    np.random.seed(h.seed)
+    torch.manual_seed(h.seed)
+    torch.cuda.manual_seed_all(h.seed)
+    val_data = ValidationData(h, device)
+    log(f"train_shards: {len(list(Path(h.datasets_dir).resolve().glob('fineweb_train_*.bin')))}")
+    log(f"val_tokens: {val_data.val_tokens.numel() - 1}")
+    base_model, compiled_model = train_model(h, device, val_data)
+    timed_eval("pre-quantization post-ema", eval_val, h, device, val_data, compiled_model)
+    serialize(h, base_model, Path(__file__).read_text(encoding="utf-8"))
+    if h.distributed:
+        dist.barrier()
+    eval_model = deserialize(h, device)
+    run_evals(h, device, val_data, eval_model)
+def main():
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    if world_size <= 0:
+        raise ValueError(f"WORLD_SIZE must be positive, got {world_size}")
+    if 8 % world_size != 0:
+        raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral")
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    torch.set_float32_matmul_precision("high")
+    from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp
+    enable_cudnn_sdp(False)
+    enable_flash_sdp(True)
+    enable_mem_efficient_sdp(False)
+    enable_math_sdp(False)
+    torch._dynamo.config.optimize_ddp = False
+    h = Hyperparameters()
+    set_logging_hparams(h)
+    if h.is_main_process:
+        os.makedirs("logs", exist_ok=True)
+        log(100 * "=", console=False)
+        log("Hyperparameters:", console=True)
+        for k, v in sorted(vars(type(h)).items()):
+            if not k.startswith("_"):
+                log(f"  {k}: {v}", console=True)
+        log(Path(__file__).read_text(encoding="utf-8"), console=False)
+        log("=" * 100, console=False)
+        log(f"Running Python {sys.version}", console=False)
+        log(f"Running PyTorch {torch.__version__}", console=False)
+        log(
+            subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout,
+            console=False,
+        )
+        log("=" * 100, console=False)
+    train_and_eval(h, device)
+    if distributed:
+        dist.destroy_process_group()
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt_clarkkev.py b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt_clarkkev.py
new file mode 100644
index 0000000000..91b83705bc
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt_clarkkev.py
@@ -0,0 +1,1627 @@
+import copy
+import glob
+import io
+import lzma
+import math
+import os
+from pathlib import Path
+import random
+import subprocess
+import sys
+import time
+import uuid
+
+import numpy as np
+import sentencepiece as spm
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch import Tensor, nn
+
+from flash_attn_interface import flash_attn_func as flash_attn_3_func
+
+# ----------------------------------------
+# Hyperparameters
+# ----------------------------------------
+
+class Hyperparameters():
+    # Experiment settings
+    data_dir = os.environ.get('DATA_DIR', './data/')
+    seed = int(os.environ.get('SEED', 1337))
+    run_id = os.environ.get("RUN_ID", str(uuid.uuid4()))
+
+    # Training length
+    iterations = int(os.environ.get('ITERATIONS', 20000))
+    warmdown_frac = float(os.environ.get('WARMDOWN_FRAC', 0.667))
+    warmup_steps = int(os.environ.get('WARMUP_STEPS', 20))
+    train_batch_tokens = int(os.environ.get('TRAIN_BATCH_TOKENS', 2048 * 48 * 8))
+    train_seq_len = int(os.environ.get('TRAIN_SEQ_LEN', 2048))
+    eval_seq_len = int(os.environ.get('EVAL_SEQ_LEN', 2048))
+    max_wallclock_seconds = float(os.environ.get('MAX_WALLCLOCK_SECONDS', 600.0))
+    train_log_every = int(os.environ.get('TRAIN_LOG_EVERY', 500))
+
+    # Validation/Evals
+    val_batch_tokens = int(os.environ.get('VAL_BATCH_TOKENS', 2048 * 32 * 8))
+    val_loss_every = int(os.environ.get('VAL_LOSS_EVERY', 4000))
+    sliding_window_enabled = bool(int(os.environ.get('SLIDING_WINDOW_ENABLED', '1')))
+
+    # Model architecture
+    vocab_size = int(os.environ.get('VOCAB_SIZE', 4096))
+    num_layers = int(os.environ.get('NUM_LAYERS', 11))
+    xsa_last_n = int(os.environ.get('XSA_LAST_N', 11))
+    num_kv_heads = int(os.environ.get('NUM_KV_HEADS', 4))
+    model_dim = int(os.environ.get('MODEL_DIM', 512))
+    embedding_dim = int(os.environ.get('EMBEDDING_DIM', 512))
+    num_heads = int(os.environ.get('NUM_HEADS', 8))
+    mlp_mult = float(os.environ.get('MLP_MULT', 4.0))
+    skip_gates_enabled = bool(int(os.environ.get('SKIP_GATES_ENABLED', '1')))
+    tie_embeddings = bool(int(os.environ.get('TIE_EMBEDDINGS', '1')))
+    logit_softcap = float(os.environ.get('LOGIT_SOFTCAP', 30.0))
+    rope_base = float(os.environ.get('ROPE_BASE', 10000.0))
+    rope_dims = int(os.environ.get('ROPE_DIMS', 16))
+    rope_train_seq_len = int(os.environ.get('ROPE_TRAIN_SEQ_LEN', 2048))
+    ln_scale = bool(int(os.environ.get('LN_SCALE', '1')))
+    ve_enabled = bool(int(os.environ.get('VE_ENABLED', '1')))
+    ve_dim = int(os.environ.get('VE_DIM', 128))
+    ve_layers = os.environ.get('VE_LAYERS', '9,10')
+    qk_gain_init = float(os.environ.get('QK_GAIN_INIT', 4.0))
+
+    # Optimizer
+    min_lr = float(os.environ.get('MIN_LR', 0.0))
+    embed_lr = float(os.environ.get('EMBED_LR', 0.6))
+    head_lr = float(os.environ.get('HEAD_LR', 0.008))
+    tied_embed_lr = float(os.environ.get('TIED_EMBED_LR', 0.03))
+    tied_embed_init_std = float(os.environ.get('TIED_EMBED_INIT_STD', 0.005))
+    matrix_lr = float(os.environ.get('MATRIX_LR', 0.02))
+    scalar_lr = float(os.environ.get('SCALAR_LR', 0.02))
+    muon_momentum = float(os.environ.get('MUON_MOMENTUM', 0.99))
+    muon_backend_steps = int(os.environ.get('MUON_BACKEND_STEPS', 5))
+    muon_momentum_warmup_start = float(os.environ.get('MUON_MOMENTUM_WARMUP_START', 0.92))
+    muon_momentum_warmup_steps = int(os.environ.get('MUON_MOMENTUM_WARMUP_STEPS', 1500))
+    beta1 = float(os.environ.get('BETA1', 0.9))
+    beta2 = float(os.environ.get('BETA2', 0.95))
+    adam_eps = float(os.environ.get('ADAM_EPS', 1e-8))
+    grad_clip_norm = float(os.environ.get('GRAD_CLIP_NORM', 0.3))
+    eval_stride = int(os.environ.get('EVAL_STRIDE', 64))
+    muon_beta2 = float(os.environ.get('MUON_BETA2', 0.95))
+    adam_wd = float(os.environ.get('ADAM_WD', 0.02))
+    muon_wd = float(os.environ.get('MUON_WD', 0.085))
+    embed_wd = float(os.environ.get('EMBED_WD', 0.085))
+    ema_decay = float(os.environ.get('EMA_DECAY', 0.997))
+
+    # Compression
+    compressor = os.environ.get('COMPRESSOR', 'brotli')  #(lzma or brotli)
+    gptq_enabled = bool(int(os.environ.get('GPTQ_ENABLED', '1')))
+    gptq_calibration_batches = int(os.environ.get('GPTQ_CALIBRATION_BATCHES', 64))
+    gptq_reserve_seconds = float(os.environ.get('GPTQ_RESERVE_SECONDS', 10.0))
+
+    # Distributed setup
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+    rank = int(os.environ.get("RANK", "0"))
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    is_main_process = rank == 0
+    grad_accum_steps = 8 // world_size
+
+    # Data paths
+    datasets_dir = os.path.join(data_dir, 'datasets', f'fineweb10B_sp{vocab_size}')
+    train_files = os.path.join(datasets_dir, 'fineweb_train_*.bin')
+    val_files = os.path.join(datasets_dir, 'fineweb_val_*.bin')
+    tokenizer_path = os.path.join(data_dir, 'tokenizers', f'fineweb_{vocab_size}_bpe.model')
+
+    # Experiment files
+    logfile = f"logs/{run_id}.txt"
+    model_path = "final_model.pt"
+    quantized_model_path = "final_model.int6.ptz"
+
+# ----------------------------------------
+# Global Logging Function
+# ----------------------------------------
+
+_logger_hparams = None
+
+
+def set_logging_hparams(h: Hyperparameters) -> None:
+    global _logger_hparams
+    _logger_hparams = h
+
+
+def log(msg, console: bool = True) -> None:
+    if _logger_hparams is None:
+        print(msg)
+    if _logger_hparams.is_main_process:
+        if console:
+            print(msg)
+        if _logger_hparams.logfile is not None:
+            with open(_logger_hparams.logfile, "a", encoding="utf-8") as f:
+                print(msg, file=f)
+
+# ----------------------------------------
+# Data Loading
+# ----------------------------------------
+
+class ValidationData:
+    def __init__(self, h: Hyperparameters, device: torch.device):
+        if not h.tokenizer_path.endswith(".model"):
+            raise ValueError(f"Script only setup for SentencePiece .model file: {h.tokenizer_path}")
+        self.sp = spm.SentencePieceProcessor(model_file=h.tokenizer_path)
+        if int(self.sp.vocab_size()) != h.vocab_size:
+            raise ValueError(
+                f"VOCAB_SIZE={h.vocab_size} does not match tokenizer vocab_size={int(self.sp.vocab_size())}"
+            )
+
+        self.val_tokens = load_validation_tokens(h.val_files, h.eval_seq_len)
+        self.base_bytes_lut, self.has_leading_space_lut, self.is_boundary_token_lut = (
+            build_sentencepiece_luts(self.sp, h.vocab_size, device))
+
+
+def build_sentencepiece_luts(
+    sp: spm.SentencePieceProcessor, vocab_size: int, device: torch.device
+) -> tuple[Tensor, Tensor, Tensor]:
+    sp_vocab_size = int(sp.vocab_size())
+    # The BPB calculation assumes "▁" is its own token so that leading-space bytes
+    # are counted correctly. See https://github.com/openai/parameter-golf/issues/897
+    assert sp.piece_to_id("\u2581") != sp.unk_id(), \
+        "Tokenizer must have '▁' (space) as its own token for correct BPB byte counting"
+    table_size = max(sp_vocab_size, vocab_size)
+    base_bytes_np = np.zeros((table_size,), dtype=np.int16)
+    has_leading_space_np = np.zeros((table_size,), dtype=np.bool_)
+    is_boundary_token_np = np.ones((table_size,), dtype=np.bool_)
+    for token_id in range(sp_vocab_size):
+        if sp.is_control(token_id) or sp.is_unknown(token_id) or sp.is_unused(token_id):
+            continue
+        is_boundary_token_np[token_id] = False
+        if sp.is_byte(token_id):
+            base_bytes_np[token_id] = 1
+            continue
+        piece = sp.id_to_piece(token_id)
+        if piece.startswith("\u2581"):
+            has_leading_space_np[token_id] = True
+            piece = piece[1:]
+        base_bytes_np[token_id] = len(piece.encode("utf-8"))
+    return (
+        torch.tensor(base_bytes_np, dtype=torch.int16, device=device),
+        torch.tensor(has_leading_space_np, dtype=torch.bool, device=device),
+        torch.tensor(is_boundary_token_np, dtype=torch.bool, device=device),
+    )
+
+
+def load_validation_tokens(pattern: str, seq_len: int) -> Tensor:
+    files = [Path(p) for p in sorted(glob.glob(pattern))]
+    if not files:
+        raise FileNotFoundError(f"No files found for pattern: {pattern}")
+    # The export pipeline writes the fixed first-50k-doc validation set to fineweb_val_*.
+    tokens = torch.cat([load_data_shard(file) for file in files]).contiguous()
+    usable = ((tokens.numel() - 1) // seq_len) * seq_len
+    if usable <= 0:
+        raise ValueError(f"Validation split is too short for TRAIN_SEQ_LEN={seq_len}")
+    return tokens[: usable + 1]
+
+
+def load_data_shard(file: Path) -> Tensor:
+    header_bytes = 256 * np.dtype("<i4").itemsize
+    token_bytes = np.dtype("<u2").itemsize
+    header = np.fromfile(file, dtype="<i4", count=256)
+    # SHARD HEADER INTS & SHARD_MAGIC
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    num_tokens = int(header[2])
+    expected_size = header_bytes + num_tokens * token_bytes
+    if file.stat().st_size != expected_size:
+        raise ValueError(f"Shard size mismatch for {file}: expected {expected_size} bytes")
+    tokens_np = np.fromfile(file, dtype="<u2", count=num_tokens, offset=header_bytes)
+    if tokens_np.size != num_tokens:
+        raise ValueError(f"Short read for {file}")
+    return torch.from_numpy(tokens_np.astype(np.uint16, copy=False))
+
+
+_SHARD_HEADER_BYTES = 256 * np.dtype("<i4").itemsize
+_SHARD_NTOKENS_CACHE: dict[str, int] = {}
+_MMAP_CACHE: dict[str, np.memmap] = {}
+
+
+def _read_num_tokens(file: Path) -> int:
+    key = str(file)
+    cached = _SHARD_NTOKENS_CACHE.get(key)
+    if cached is not None:
+        return cached
+    header = np.fromfile(file, dtype="<i4", count=256)
+    if header.size != 256 or int(header[0]) != 20240520 or int(header[1]) != 1:
+        raise ValueError(f"Unexpected shard header for {file}")
+    n = int(header[2])
+    _SHARD_NTOKENS_CACHE[key] = n
+    return n
+
+
+def _get_shard_memmap(file: Path) -> np.memmap:
+    key = str(file)
+    mm = _MMAP_CACHE.get(key)
+    if mm is not None:
+        return mm
+    n = _read_num_tokens(file)
+    mm = np.memmap(file, mode="r", dtype="<u2", offset=_SHARD_HEADER_BYTES, shape=(n,))
+    _MMAP_CACHE[key] = mm
+    return mm
+
+
+class DistributedTokenLoader:
+    """Coprime-stride multi-shard loader. Samples windows across shards with
+    increasing diversity over training, using coprime strides for coverage."""
+
+    def __init__(self, pattern: str, rank: int, world_size: int, device: torch.device):
+        self.rank = rank
+        self.world_size = world_size
+        self.device = device
+        self.files = [Path(p) for p in sorted(glob.glob(pattern))]
+        if not self.files:
+            raise FileNotFoundError(f"No files found for pattern: {pattern}")
+        self._num_tokens = np.array([_read_num_tokens(f) for f in self.files], dtype=np.int64)
+        seed = 0
+        for f in self.files:
+            for b in str(f).encode():
+                seed = ((seed ^ b) * 1099511628211) & 0xFFFFFFFFFFFFFFFF
+        self._rng = np.random.Generator(np.random.PCG64(seed))
+        self._cfg: tuple[int, int, int, int] | None = None
+        self._eligible_shards: np.ndarray | None = None
+        self._base_block_counts: np.ndarray | None = None
+        n = len(self.files)
+        self._cursor_phase = np.zeros(n, dtype=np.int64)
+        self._cursor_block_count = np.zeros(n, dtype=np.int64)
+        self._cursor_next = np.zeros(n, dtype=np.int64)
+        self._cursor_start = np.zeros(n, dtype=np.int64)
+        self._cursor_stride = np.ones(n, dtype=np.int64)
+        self._cursor_init = np.zeros(n, dtype=np.bool_)
+        self._batches_built = 0
+
+    def _pick_coprime_stride(self, n: int) -> int:
+        if n <= 1:
+            return 1
+        while True:
+            s = int(self._rng.integers(1, n))
+            if math.gcd(s, n) == 1:
+                return s
+
+    def _reset_cursor(self, si: int, seq_len: int) -> None:
+        nt = int(self._num_tokens[si])
+        max_phase = min(seq_len - 1, max(0, nt - seq_len - 1))
+        phase = int(self._rng.integers(max_phase + 1)) if max_phase > 0 else 0
+        bc = (nt - 1 - phase) // seq_len
+        self._cursor_phase[si] = phase
+        self._cursor_block_count[si] = bc
+        self._cursor_next[si] = 0
+        self._cursor_start[si] = int(self._rng.integers(bc)) if bc > 1 else 0
+        self._cursor_stride[si] = self._pick_coprime_stride(bc)
+        self._cursor_init[si] = True
+
+    def _ensure_cursor(self, si: int, seq_len: int) -> None:
+        if not self._cursor_init[si] or self._cursor_next[si] >= self._cursor_block_count[si]:
+            self._reset_cursor(si, seq_len)
+
+    def _take_from_shard(self, si: int, seq_len: int, count: int, out: list[tuple[int, int]]) -> None:
+        rem = count
+        while rem > 0:
+            self._ensure_cursor(si, seq_len)
+            bc = int(self._cursor_block_count[si])
+            ni = int(self._cursor_next[si])
+            take = min(rem, bc - ni)
+            phase = int(self._cursor_phase[si])
+            start = int(self._cursor_start[si])
+            stride = int(self._cursor_stride[si])
+            for j in range(take):
+                bi = (start + (ni + j) * stride) % bc
+                out.append((si, phase + bi * seq_len))
+            self._cursor_next[si] = ni + take
+            rem -= take
+
+    def _init_pipeline(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> None:
+        local_tokens = global_tokens // (self.world_size * grad_accum_steps)
+        num_seqs = local_tokens // seq_len
+        global_num_seqs = num_seqs * self.world_size
+        self._cfg = (local_tokens, seq_len, num_seqs, global_num_seqs)
+        bbc = (self._num_tokens - 1) // seq_len
+        eligible = bbc > 0
+        self._eligible_shards = np.nonzero(eligible)[0].astype(np.int64)
+        self._base_block_counts = bbc[self._eligible_shards].astype(np.int64)
+
+    def _sample_global_windows(self) -> list[tuple[int, int]]:
+        assert self._cfg is not None and self._eligible_shards is not None
+        _, seq_len, _, gns = self._cfg
+        ec = int(self._eligible_shards.size)
+        progress = min(self._batches_built / 1800.0, 1.0)
+        remaining = np.empty(ec, dtype=np.float64)
+        for i, si in enumerate(self._eligible_shards.tolist()):
+            if self._cursor_init[si]:
+                r = int(self._cursor_block_count[si]) - int(self._cursor_next[si])
+                remaining[i] = float(max(r, 1))
+            else:
+                remaining[i] = float(self._base_block_counts[i])
+        alpha = 0.90 - 0.40 * progress
+        weights = np.power(remaining, alpha)
+        ws = float(weights.sum())
+        if not np.isfinite(ws) or ws <= 0.0:
+            weights = np.ones(ec, dtype=np.float64)
+            ws = float(weights.sum())
+        probs = weights / ws
+        low = min(max(8, self.world_size), ec, gns)
+        high = min(max(32, self.world_size * 8), ec, gns)
+        mix = max(1, min(int(round(low + progress * (high - low))), ec, gns))
+        cp = self._rng.choice(ec, size=mix, replace=False, p=probs)
+        cs = self._eligible_shards[cp]
+        cpr = probs[cp].copy()
+        cpr /= cpr.sum()
+        counts = np.ones(mix, dtype=np.int64)
+        extra = gns - mix
+        if extra > 0:
+            counts += self._rng.multinomial(extra, cpr).astype(np.int64)
+        perm = self._rng.permutation(mix)
+        cs, counts = cs[perm], counts[perm]
+        buckets: list[list[tuple[int, int]]] = []
+        for si, cnt in zip(cs.tolist(), counts.tolist()):
+            b: list[tuple[int, int]] = []
+            self._take_from_shard(int(si), seq_len, int(cnt), b)
+            if b:
+                if len(b) > 1:
+                    bp = self._rng.permutation(len(b))
+                    b = [b[int(k)] for k in bp.tolist()]
+                buckets.append(b)
+        windows: list[tuple[int, int]] = []
+        active = [i for i, bk in enumerate(buckets) if bk]
+        while active:
+            order = self._rng.permutation(len(active))
+            new_active: list[int] = []
+            for oi in order.tolist():
+                bi = active[oi]
+                if buckets[bi]:
+                    windows.append(buckets[bi].pop())
+                if buckets[bi]:
+                    new_active.append(bi)
+            active = new_active
+        return windows
+
+    def next_batch(self, global_tokens: int, seq_len: int, grad_accum_steps: int) -> tuple[Tensor, Tensor]:
+        if self._cfg is None:
+            self._init_pipeline(global_tokens, seq_len, grad_accum_steps)
+        _, _, num_seqs, _ = self._cfg
+        gw = self._sample_global_windows()
+        local_w = gw[self.rank::self.world_size]
+        x = torch.empty((num_seqs, seq_len), dtype=torch.int64)
+        y = torch.empty((num_seqs, seq_len), dtype=torch.int64)
+        for slot, (si, pos) in enumerate(local_w):
+            mm = _get_shard_memmap(self.files[si])
+            window = torch.as_tensor(np.array(mm[pos:pos + seq_len + 1], dtype=np.int64))
+            x[slot] = window[:-1]
+            y[slot] = window[1:]
+        self._batches_built += 1
+        return x.to(self.device, non_blocking=True), y.to(self.device, non_blocking=True)
+
+# ----------------------------------------
+# Model Architecture
+# ----------------------------------------
+
+class RMSNorm(nn.Module):
+    def __init__(self, eps: float | None = None):
+        super().__init__()
+        self.eps = eps
+
+    def forward(self, x: Tensor) -> Tensor:
+        return F.rms_norm(x, (x.size(-1),), eps=self.eps)
+
+
+class CastedLinear(nn.Linear):
+    def forward(self, x: Tensor) -> Tensor:
+        w = self.weight.to(x.dtype)
+        bias = self.bias.to(x.dtype) if self.bias is not None else None
+        return F.linear(x, w, bias)
+
+
+class Rotary(nn.Module):
+    def __init__(self, dim: int, base: float = 10000.0, train_seq_len: int = 1024, rope_dims: int = 0):
+        super().__init__()
+        self.dim = dim
+        self.base = base
+        self.train_seq_len = train_seq_len
+        self.rope_dims = rope_dims if rope_dims > 0 else dim
+        inv_freq = 1.0 / (base ** (torch.arange(0, self.rope_dims, 2, dtype=torch.float32) / self.rope_dims))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self._seq_len_cached = 0
+        self._cos_cached: Tensor | None = None
+        self._sin_cached: Tensor | None = None
+
+    def forward(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> tuple[Tensor, Tensor]:
+        if (
+            self._cos_cached is None
+            or self._sin_cached is None
+            or self._seq_len_cached != seq_len
+            or self._cos_cached.device != device
+        ):
+            rd = self.rope_dims
+            if seq_len > self.train_seq_len:
+                scale = seq_len / self.train_seq_len
+                new_base = self.base * (scale ** (rd / (rd - 2)))
+                inv_freq = 1.0 / (new_base ** (torch.arange(0, rd, 2, dtype=torch.float32, device=device) / rd))
+            else:
+                inv_freq = self.inv_freq.to(device)
+            t = torch.arange(seq_len, device=device, dtype=inv_freq.dtype)
+            freqs = torch.outer(t, inv_freq)
+            self._cos_cached = freqs.cos()[None, :, None, :]
+            self._sin_cached = freqs.sin()[None, :, None, :]
+            self._seq_len_cached = seq_len
+        return self._cos_cached.to(dtype=dtype), self._sin_cached.to(dtype=dtype)
+
+
+def apply_rotary_emb(x: Tensor, cos: Tensor, sin: Tensor, rope_dims: int = 0) -> Tensor:
+    if rope_dims > 0 and rope_dims < x.size(-1):
+        x_rope, x_pass = x[..., :rope_dims], x[..., rope_dims:]
+        half = rope_dims // 2
+        x1, x2 = x_rope[..., :half], x_rope[..., half:]
+        x_rope = torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+        return torch.cat((x_rope, x_pass), dim=-1)
+    half = x.size(-1) // 2
+    x1, x2 = x[..., :half], x[..., half:]
+    return torch.cat((x1 * cos + x2 * sin, x1 * (-sin) + x2 * cos), dim=-1)
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int,
+                 rope_base: float, qk_gain_init: float, train_seq_len: int):
+        super().__init__()
+        if dim % num_heads != 0:
+            raise ValueError("model_dim must be divisible by num_heads")
+        if num_heads % num_kv_heads != 0:
+            raise ValueError("num_heads must be divisible by num_kv_heads")
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = dim // num_heads
+        if self.head_dim % 2 != 0:
+            raise ValueError("head_dim must be even for RoPE")
+        kv_dim = self.num_kv_heads * self.head_dim
+        self.c_q = CastedLinear(dim, dim, bias=False)
+        self.c_k = CastedLinear(dim, kv_dim, bias=False)
+        self.c_v = CastedLinear(dim, kv_dim, bias=False)
+        self.proj = CastedLinear(dim, dim, bias=False)
+        self.proj._zero_init = True
+        self.q_gain = nn.Parameter(torch.full((num_heads,), qk_gain_init, dtype=torch.float32))
+        self.rope_dims = 0
+        self.rotary = Rotary(self.head_dim, base=rope_base, train_seq_len=train_seq_len)
+        self.use_xsa = False
+
+    def _xsa_efficient(self, y: Tensor, v: Tensor) -> Tensor:
+        B, T, H, D = y.shape
+        Hkv = v.size(-2)
+        group = H // Hkv
+        y_g = y.reshape(B, T, Hkv, group, D)
+        vn = F.normalize(v, dim=-1).unsqueeze(-2)
+        proj = (y_g * vn).sum(dim=-1, keepdim=True) * vn
+        return (y_g - proj).reshape(B, T, H, D)
+
+    def forward(self, x: Tensor, v_embed: Tensor | None = None) -> Tensor:
+        bsz, seqlen, dim = x.shape
+        q = self.c_q(x).reshape(bsz, seqlen, self.num_heads, self.head_dim)
+        k = self.c_k(x).reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        v = self.c_v(x)
+        if v_embed is not None:
+            v = v + v_embed
+        v = v.reshape(bsz, seqlen, self.num_kv_heads, self.head_dim)
+        q = F.rms_norm(q, (q.size(-1),))
+        k = F.rms_norm(k, (k.size(-1),))
+        cos, sin = self.rotary(seqlen, x.device, q.dtype)
+        q = apply_rotary_emb(q, cos, sin, self.rope_dims)
+        k = apply_rotary_emb(k, cos, sin, self.rope_dims)
+        q = q * self.q_gain.to(dtype=q.dtype)[None, None, :, None]
+        y = flash_attn_3_func(q, k, v, causal=True)
+        if self.use_xsa:
+            y = self._xsa_efficient(y, v)
+        y = y.reshape(bsz, seqlen, dim)
+        return self.proj(y)
+
+
+class ValueEmbedding(nn.Module):
+    def __init__(self, vocab_size: int, ve_dim: int, model_dim: int):
+        super().__init__()
+        self.embed = nn.Embedding(vocab_size, ve_dim)
+        nn.init.normal_(self.embed.weight, std=0.01)
+        self.proj = CastedLinear(ve_dim, model_dim, bias=False) if ve_dim != model_dim else None
+        if self.proj is not None:
+            nn.init.zeros_(self.proj.weight)
+        self.scale = nn.Parameter(torch.tensor(0.1, dtype=torch.float32))
+
+    def forward(self, token_ids: Tensor) -> Tensor:
+        h = self.embed(token_ids)
+        if self.proj is not None:
+            h = self.proj(h)
+        return h * self.scale.to(dtype=h.dtype)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim: int, mlp_mult: int):
+        super().__init__()
+        hidden = int(mlp_mult * dim)
+        self.fc = CastedLinear(dim, hidden, bias=False)
+        self.proj = CastedLinear(hidden, dim, bias=False)
+        self.proj._zero_init = True
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.proj(F.leaky_relu(self.fc(x), negative_slope=0.5).square())
+
+
+class Block(nn.Module):
+    def __init__(self, dim: int, num_heads: int, num_kv_heads: int, mlp_mult: int,
+                 rope_base: float, qk_gain_init: float, train_seq_len: int,
+                 layer_idx: int = 0, ln_scale: bool = False):
+        super().__init__()
+        self.attn_norm = RMSNorm()
+        self.mlp_norm = RMSNorm()
+        self.attn = CausalSelfAttention(dim, num_heads, num_kv_heads, rope_base, qk_gain_init, train_seq_len)
+        self.mlp = MLP(dim, mlp_mult)
+        self.attn_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.mlp_scale = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.resid_mix = nn.Parameter(torch.stack((torch.ones(dim), torch.zeros(dim))).float())
+        self.ln_scale_factor = 1.0 / math.sqrt(layer_idx + 1) if ln_scale else 1.0
+
+    def forward(self, x: Tensor, x0: Tensor, v_embed: Tensor | None = None) -> Tensor:
+        mix = self.resid_mix.to(dtype=x.dtype)
+        x_in = mix[0][None, None, :] * x + mix[1][None, None, :] * x0
+        attn_out = self.attn(self.attn_norm(x_in) * self.ln_scale_factor, v_embed=v_embed)
+        x_out = x_in + self.attn_scale.to(dtype=x_in.dtype)[None, None, :] * attn_out
+        x_out = x_out + self.mlp_scale.to(dtype=x_out.dtype)[None, None, :] * self.mlp(self.mlp_norm(x_out) * self.ln_scale_factor)
+        return x_out
+
+
+class GPT(nn.Module):
+    def __init__(self, h: Hyperparameters):
+        super().__init__()
+        self._ve_target_dim = h.num_kv_heads * (h.model_dim // h.num_heads)
+        if h.logit_softcap <= 0.0:
+            raise ValueError(f"logit_softcap must be positive, got {h.logit_softcap}")
+        self.tie_embeddings = h.tie_embeddings
+        self.tied_embed_init_std = h.tied_embed_init_std
+        self.logit_softcap = h.logit_softcap
+        self.tok_emb = nn.Embedding(h.vocab_size, h.embedding_dim)
+        if h.embedding_dim != h.model_dim:
+            self.embed_proj = CastedLinear(h.embedding_dim, h.model_dim, bias=False)
+            self.head_proj = CastedLinear(h.model_dim, h.embedding_dim, bias=False)
+        else:
+            self.embed_proj = None
+            self.head_proj = None
+        self.num_encoder_layers = h.num_layers // 2
+        self.num_decoder_layers = h.num_layers - self.num_encoder_layers
+        self.num_skip_weights = min(self.num_encoder_layers, self.num_decoder_layers)
+        self.skip_weights = nn.Parameter(torch.ones(self.num_skip_weights, h.model_dim, dtype=torch.float32))
+        self.skip_gates = nn.Parameter(torch.zeros(self.num_skip_weights, h.model_dim, dtype=torch.float32)) if h.skip_gates_enabled else None
+        self.blocks = nn.ModuleList([
+            Block(h.model_dim, h.num_heads, h.num_kv_heads, h.mlp_mult, h.rope_base,
+                  h.qk_gain_init, h.train_seq_len, layer_idx=i, ln_scale=h.ln_scale)
+            for i in range(h.num_layers)
+        ])
+        if h.rope_dims > 0:
+            head_dim = h.model_dim // h.num_heads
+            for block in self.blocks:
+                block.attn.rope_dims = h.rope_dims
+                block.attn.rotary = Rotary(head_dim, base=h.rope_base, train_seq_len=h.train_seq_len, rope_dims=h.rope_dims)
+        self.ve_layer_indices = [int(x) for x in h.ve_layers.split(",") if x.strip()] if h.ve_enabled else []
+        kv_dim = self._ve_target_dim
+        if self.ve_layer_indices:
+            self.ve_shared = ValueEmbedding(h.vocab_size, h.ve_dim, kv_dim)
+            self.ve_layer_scales = nn.ParameterList(
+                [nn.Parameter(torch.ones(1, dtype=torch.float32)) for _ in self.ve_layer_indices]
+            )
+        else:
+            self.ve_shared = None
+            self.ve_layer_scales = nn.ParameterList()
+        self.value_embeds = nn.ModuleList()
+        self.final_norm = RMSNorm()
+        self.lm_head = None if h.tie_embeddings else CastedLinear(h.embedding_dim, h.vocab_size, bias=False)
+        if self.lm_head is not None:
+            self.lm_head._zero_init = True
+        if h.xsa_last_n > 0:
+            for i in range(max(0, h.num_layers - h.xsa_last_n), h.num_layers):
+                self.blocks[i].attn.use_xsa = True
+        self._init_weights()
+
+    def _init_weights(self) -> None:
+        if self.tie_embeddings:
+            nn.init.normal_(self.tok_emb.weight, mean=0.0, std=self.tied_embed_init_std)
+        for name, module in self.named_modules():
+            if isinstance(module, nn.Linear):
+                if getattr(module, "_zero_init", False):
+                    nn.init.zeros_(module.weight)
+                elif module.weight.ndim == 2 and module.weight.shape[0] >= 64 and module.weight.shape[1] >= 64:
+                    nn.init.orthogonal_(module.weight, gain=1.0)
+
+    def _get_ve(self, layer_idx: int, input_ids: Tensor, ve_cache: dict | None = None) -> Tensor | None:
+        if self.ve_shared is None or layer_idx not in self.ve_layer_indices:
+            return None
+        if ve_cache is not None and 've' not in ve_cache:
+            ve_cache['ve'] = self.ve_shared(input_ids)
+        ve_base = ve_cache['ve'] if ve_cache is not None else self.ve_shared(input_ids)
+        ve_idx = self.ve_layer_indices.index(layer_idx)
+        return ve_base * self.ve_layer_scales[ve_idx].to(dtype=ve_base.dtype)
+
+    def forward_logits(self, input_ids: Tensor) -> Tensor:
+        x = self.tok_emb(input_ids)
+        x = F.rms_norm(x, (x.size(-1),))
+        if self.embed_proj is not None:
+            x = self.embed_proj(x)
+        x0 = x
+        skips: list[Tensor] = []
+        ve_cache: dict = {}
+        for i in range(self.num_encoder_layers):
+            ve = self._get_ve(i, input_ids, ve_cache)
+            x = self.blocks[i](x, x0, v_embed=ve)
+            skips.append(x)
+        for i in range(self.num_decoder_layers):
+            bi = self.num_encoder_layers + i
+            if skips:
+                scaled_skip = self.skip_weights[i].to(dtype=x.dtype)[None, None, :] * skips.pop()
+                if self.skip_gates is not None:
+                    g = torch.sigmoid(self.skip_gates[i].to(dtype=x.dtype))[None, None, :]
+                    x = torch.lerp(scaled_skip, x, g)
+                else:
+                    x = x + scaled_skip
+            ve = self._get_ve(bi, input_ids, ve_cache)
+            x = self.blocks[bi](x, x0, v_embed=ve)
+        x = self.final_norm(x)
+        if self.head_proj is not None:
+            x = self.head_proj(x)
+        if self.tie_embeddings:
+            logits_proj = F.linear(x, self.tok_emb.weight)
+        else:
+            logits_proj = self.lm_head(x)
+        return self.logit_softcap * torch.tanh(logits_proj / self.logit_softcap)
+
+    def forward(self, input_ids: Tensor, target_ids: Tensor) -> Tensor:
+        logits = self.forward_logits(input_ids)
+        return F.cross_entropy(
+            logits.reshape(-1, logits.size(-1)).float(), target_ids.reshape(-1), reduction="mean")
+
+
+def classify_param(name: str) -> str:
+    if "tok_emb" in name or "lm_head" in name:
+        return "embed"
+    if ".mlp." in name:
+        return "mlp"
+    if ".attn." in name or (".proj." in name and ".mlp." not in name):
+        return "attn"
+    return "other"
+
+# ----------------------------------------
+# Optimization
+# ----------------------------------------
+
+@torch.compile
+def zeropower_via_newtonschulz5(G: Tensor, steps: int = 10, eps: float = 1e-7) -> Tensor:
+    a, b, c = (3.4445, -4.7750, 2.0315)
+    X = G.bfloat16()
+    X /= X.norm() + eps
+    transposed = G.size(0) > G.size(1)
+    if transposed:
+        X = X.T
+    for _ in range(steps):
+        A = X @ X.T
+        B = b * A + c * A @ A
+        X = a * X + B @ X
+    return X.T if transposed else X
+
+
+class Muon(torch.optim.Optimizer):
+    def __init__(self, params, lr: float, momentum: float, backend_steps: int,
+                 nesterov: bool = True, weight_decay: float = 0.0):
+        super().__init__(
+            params,
+            dict(lr=lr, momentum=momentum, backend_steps=backend_steps,
+                 nesterov=nesterov, weight_decay=weight_decay),
+        )
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+        distributed = dist.is_available() and dist.is_initialized()
+        world_size = dist.get_world_size() if distributed else 1
+        rank = dist.get_rank() if distributed else 0
+        for group in self.param_groups:
+            params = group["params"]
+            if not params:
+                continue
+            lr = group["lr"]
+            momentum = group["momentum"]
+            backend_steps = group["backend_steps"]
+            nesterov = group["nesterov"]
+            total_params = sum(int(p.numel()) for p in params)
+            updates_flat = torch.zeros(total_params, device=params[0].device, dtype=torch.bfloat16)
+            curr = 0
+            for i, p in enumerate(params):
+                if i % world_size == rank and p.grad is not None:
+                    g = p.grad
+                    state = self.state[p]
+                    if "momentum_buffer" not in state:
+                        state["momentum_buffer"] = torch.zeros_like(g)
+                    buf = state["momentum_buffer"]
+                    buf.mul_(momentum).add_(g)
+                    if nesterov:
+                        g = g.add(buf, alpha=momentum)
+                    g = zeropower_via_newtonschulz5(g, steps=backend_steps)
+                    g *= max(1, g.size(0) / g.size(1)) ** 0.5
+                    updates_flat[curr : curr + p.numel()] = g.reshape(-1)
+                curr += p.numel()
+            if distributed:
+                dist.all_reduce(updates_flat, op=dist.ReduceOp.SUM)
+            wd = group.get("weight_decay", 0.0)
+            curr = 0
+            for p in params:
+                if wd > 0.0:
+                    p.data.mul_(1.0 - lr * wd)
+                g = updates_flat[curr : curr + p.numel()].view_as(p).to(dtype=p.dtype)
+                p.add_(g, alpha=-lr)
+                curr += p.numel()
+        return loss
+
+
+class Optimizers():
+    def __init__(self, h: Hyperparameters, base_model: GPT):
+        block_named_params = list(base_model.blocks.named_parameters())
+        matrix_params = [
+            p
+            for name, p in block_named_params
+            if p.ndim == 2 and not any(pattern in name for pattern in
+                                       CONTROL_TENSOR_NAME_PATTERNS)
+        ]
+        scalar_params = [
+            p
+            for name, p in block_named_params
+            if p.ndim < 2 or any(pattern in name for pattern in
+                                 CONTROL_TENSOR_NAME_PATTERNS)
+        ]
+        if base_model.skip_weights.numel() > 0:
+            scalar_params.append(base_model.skip_weights)
+        if base_model.skip_gates is not None and base_model.skip_gates.numel() > 0:
+            scalar_params.append(base_model.skip_gates)
+
+        token_lr = h.tied_embed_lr if h.tie_embeddings else h.embed_lr
+        tok_params = [{"params": [base_model.tok_emb.weight], "lr": token_lr, "base_lr": token_lr}]
+        if base_model.ve_shared is not None:
+            tok_params.append({"params": [base_model.ve_shared.embed.weight], "lr": token_lr, "base_lr": token_lr})
+            if base_model.ve_shared.proj is not None:
+                matrix_params.append(base_model.ve_shared.proj.weight)
+            scalar_params.append(base_model.ve_shared.scale)
+            for s in base_model.ve_layer_scales:
+                scalar_params.append(s)
+
+        self.optimizer_tok = torch.optim.AdamW(
+            tok_params,
+            betas=(h.beta1, h.beta2),
+            eps=h.adam_eps,
+            weight_decay=h.embed_wd,
+            fused=True,
+        )
+        self.optimizer_muon = Muon(
+            matrix_params,
+            lr=h.matrix_lr,
+            momentum=h.muon_momentum,
+            backend_steps=h.muon_backend_steps,
+            weight_decay=h.muon_wd,
+        )
+        for group in self.optimizer_muon.param_groups:
+            group["base_lr"] = h.matrix_lr
+        self.optimizer_scalar = torch.optim.AdamW(
+            [{"params": scalar_params, "lr": h.scalar_lr, "base_lr": h.scalar_lr}],
+            betas=(h.beta1, h.beta2),
+            eps=h.adam_eps,
+            weight_decay=h.adam_wd,
+            fused=True,
+        )
+        self.optimizers: list[torch.optim.Optimizer] = [self.optimizer_tok, self.optimizer_muon, self.optimizer_scalar]
+        if base_model.lm_head is not None:
+            self.optimizer_head = torch.optim.Adam(
+                [{"params": [base_model.lm_head.weight], "lr": h.head_lr, "base_lr": h.head_lr}],
+                betas=(h.beta1, h.beta2),
+                eps=h.adam_eps,
+                fused=True,
+            )
+            self.optimizers.insert(1, self.optimizer_head)
+        else:
+            self.optimizer_head = None
+
+    def __iter__(self):
+        return iter(self.optimizers)
+
+    def zero_grad_all(self) -> None:
+        for opt in self.optimizers:
+            opt.zero_grad(set_to_none=True)
+
+    def step(self):
+        for opt in self.optimizers:
+            opt.step()
+        self.zero_grad_all()
+
+# ----------------------------------------
+# Quantization
+# ----------------------------------------
+
+CONTROL_TENSOR_NAME_PATTERNS = tuple(
+    pattern
+    for pattern in os.environ.get(
+        "CONTROL_TENSOR_NAME_PATTERNS",
+        "attn_scale,attn_scales,mlp_scale,mlp_scales,resid_mix,resid_mixes,q_gain,skip_weight,skip_weights,skip_gates,ve_layer_scales,ve_shared.scale",
+    ).split(",")
+    if pattern
+)
+INT8_PER_ROW_SCALE_DTYPE = torch.float16
+INT8_CLIP_PERCENTILE = 99.99984
+INT8_CLIP_Q = INT8_CLIP_PERCENTILE / 100.0
+
+
+def quantize_float_tensor(t: Tensor) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        clip_abs = (
+            torch.quantile(t32.abs(), INT8_CLIP_Q, dim=1)
+            if t32.numel()
+            else torch.empty((t32.shape[0],), dtype=torch.float32)
+        )
+        clipped = torch.maximum(torch.minimum(t32, clip_abs[:, None]), -clip_abs[:, None])
+        scale = (clip_abs / 127.0).clamp_min(1.0 / 127.0)
+        q = torch.clamp(torch.round(clipped / scale[:, None]), -127, 127).to(torch.int8).contiguous()
+        return q, scale.to(dtype=INT8_PER_ROW_SCALE_DTYPE).contiguous()
+
+    clip_abs = float(torch.quantile(t32.abs().flatten(), INT8_CLIP_Q).item()) if t32.numel() else 0.0
+    scale = torch.tensor(clip_abs / 127.0 if clip_abs > 0 else 1.0, dtype=torch.float32)
+    q = torch.clamp(torch.round(torch.clamp(t32, -clip_abs, clip_abs) / scale), -127, 127).to(torch.int8).contiguous()
+    return q, scale
+
+
+def restore_fp32_params(model: nn.Module) -> None:
+    """After .bfloat16(), restore CastedLinear weights and control params to FP32."""
+    for module in model.modules():
+        if isinstance(module, CastedLinear):
+            module.float()
+    for name, param in model.named_parameters():
+        if (param.ndim < 2 or any(pattern in name for pattern in CONTROL_TENSOR_NAME_PATTERNS)) and param.dtype != torch.float32:
+            param.data = param.data.float()
+
+
+def quantize_int6_per_row(t: Tensor, clip_range: int = 31) -> tuple[Tensor, Tensor]:
+    t32 = t.float()
+    if t32.ndim == 2:
+        best_q, best_s, best_err = None, None, float('inf')
+        for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]:
+            if pct < 1.0:
+                row_clip = torch.quantile(t32.abs(), pct, dim=1)
+            else:
+                row_clip = t32.abs().amax(dim=1)
+            s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16)
+            q = torch.clamp(torch.round(t32 / s.float()[:, None]), -clip_range, clip_range).to(torch.int8)
+            recon = q.float() * s.float()[:, None]
+            err = (t32 - recon).pow(2).mean().item()
+            if err < best_err:
+                best_q, best_s, best_err = q, s, err
+        return best_q, best_s
+    amax = t32.abs().max().item()
+    scale = torch.tensor(amax / clip_range if amax > 0 else 1.0, dtype=torch.float16)
+    q = torch.clamp(torch.round(t32 / scale.float()), -clip_range, clip_range).to(torch.int8)
+    return q, scale
+
+
+def collect_hessians(
+    model: nn.Module,
+    train_loader: DistributedTokenLoader,
+    h: Hyperparameters,
+    device: torch.device,
+    n_calibration_batches: int = 64,
+) -> dict[str, Tensor]:
+    """Run calibration batches and collect H = X^T X for each CastedLinear layer."""
+    hessians: dict[str, Tensor] = {}
+    hooks = []
+
+    def make_hook(name: str):
+        def hook_fn(module, inp, out):
+            x = inp[0].detach().float()
+            if x.ndim == 3:
+                x = x.reshape(-1, x.shape[-1])
+            if name not in hessians:
+                hessians[name] = torch.zeros(
+                    x.shape[1], x.shape[1], dtype=torch.float32, device=device
+                )
+            hessians[name].addmm_(x.T, x)
+        return hook_fn
+
+    for name, module in model.named_modules():
+        if isinstance(module, CastedLinear) and module.weight.numel() > 65536:
+            cat = classify_param(name + ".weight")
+            if cat in ("mlp", "attn"):
+                hooks.append(module.register_forward_hook(make_hook(name + ".weight")))
+
+    model.eval()
+    with torch.no_grad():
+        for i in range(n_calibration_batches):
+            x, y = train_loader.next_batch(
+                h.train_batch_tokens,
+                h.train_seq_len, h.grad_accum_steps,
+            )
+            model.forward_logits(x)
+
+    for h in hooks:
+        h.remove()
+
+    for name in hessians:
+        hessians[name] = hessians[name].cpu() / n_calibration_batches
+
+    return hessians
+
+
+def gptq_quantize_weight(
+    w: Tensor,
+    H: Tensor,
+    clip_range: int = 31,
+    block_size: int = 128,
+) -> tuple[Tensor, Tensor]:
+    """GPTQ with Cholesky error compensation and actorder (Frantar et al., ICLR 2023)."""
+    W_orig = w.float().clone()
+    rows, cols = W_orig.shape
+    H = H.float().clone()
+
+    # Zero out dead columns and add damping
+    dead = torch.diag(H) == 0
+    H[dead, dead] = 1
+    damp = 0.01 * H.diag().mean()
+    H.diagonal().add_(damp)
+
+    # Column reordering by descending Hessian diagonal (actorder)
+    perm = torch.argsort(H.diag(), descending=True)
+    invperm = torch.argsort(perm)
+    W_perm = W_orig[:, perm].clone()
+    W_perm[:, dead[perm]] = 0
+    H = H[perm][:, perm]
+
+    # Upper Cholesky of the inverse
+    try:
+        Hinv = torch.cholesky_inverse(torch.linalg.cholesky(H))
+        Hinv = torch.linalg.cholesky(Hinv, upper=True)
+    except torch.linalg.LinAlgError:
+        return quantize_int6_per_row(W_orig, clip_range)
+
+    # Search over scale candidates, running full GPTQ for each
+    best_q, best_scale, best_err = None, None, float('inf')
+    for pct in [0.9990, 0.9995, 0.9999, 0.99999, 1.0]:
+        if pct < 1.0:
+            row_clip = torch.quantile(W_orig.abs(), pct, dim=1)
+        else:
+            row_clip = W_orig.abs().amax(dim=1)
+        s = (row_clip / clip_range).clamp_min(1.0 / clip_range).to(torch.float16)
+        sf = s.float()
+
+        Q = torch.zeros(rows, cols, dtype=torch.int8)
+        W_work = W_perm.clone()
+        for i1 in range(0, cols, block_size):
+            i2 = min(i1 + block_size, cols)
+            W_block = W_work[:, i1:i2].clone()
+            Hinv_block = Hinv[i1:i2, i1:i2]
+            Err = torch.zeros(rows, i2 - i1)
+            for j in range(i2 - i1):
+                w_col = W_block[:, j]
+                d = Hinv_block[j, j]
+                q_col = torch.clamp(torch.round(w_col / sf), -clip_range, clip_range)
+                Q[:, i1 + j] = q_col.to(torch.int8)
+                err = (w_col - q_col.float() * sf) / d
+                Err[:, j] = err
+                W_block[:, j:] -= err.unsqueeze(1) * Hinv_block[j, j:].unsqueeze(0)
+            if i2 < cols:
+                W_work[:, i2:] -= Err @ Hinv[i1:i2, i2:]
+
+        recon = Q.float() * sf[:, None]
+        mse = (W_perm - recon).pow(2).mean().item()
+        if mse < best_err:
+            best_q, best_scale, best_err = Q, s, mse
+
+    return best_q[:, invperm], best_scale
+
+
+def gptq_mixed_quantize_int6(
+    state_dict: dict[str, Tensor],
+    int6_cats: set[str],
+    hessians: dict[str, Tensor],
+) -> tuple[dict[str, Tensor], dict[str, object]]:
+    """Mixed quantization using full GPTQ for layers with Hessians, fallback to clip-search."""
+    result: dict[str, Tensor] = {}
+    meta: dict[str, object] = {}
+    gptq_count = 0
+    fallback_count = 0
+
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().contiguous()
+        cat = classify_param(name)
+
+        if not t.is_floating_point() or t.numel() <= 65536:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = "passthrough"
+            continue
+
+        if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS):
+            result[name] = t.float()
+            meta[name] = "passthrough_ctrl"
+            continue
+
+        if cat in int6_cats and t.ndim == 2:
+            if name in hessians:
+                q, s = gptq_quantize_weight(t, hessians[name])
+                gptq_count += 1
+                meta[name] = {"type": "int6", "method": "gptq"}
+            else:
+                q, s = quantize_int6_per_row(t)
+                fallback_count += 1
+                meta[name] = {"type": "int6", "method": "clip_search"}
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+        elif cat in int6_cats and t.ndim >= 1:
+            q, s = quantize_int6_per_row(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int6"}
+        else:
+            q, s = quantize_float_tensor(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int8"}
+
+    log(f"GPTQ quantization: {gptq_count} layers with full GPTQ, {fallback_count} fallback to clip-search")
+    return result, meta
+
+
+def mixed_quantize_int6(state_dict: dict[str, Tensor], int6_cats: set[str]):
+    result: dict[str, Tensor] = {}
+    meta: dict[str, object] = {}
+    for name, tensor in state_dict.items():
+        t = tensor.detach().cpu().contiguous()
+        cat = classify_param(name)
+        if not t.is_floating_point() or t.numel() <= 65536:
+            result[name] = t.to(torch.float16) if t.is_floating_point() else t
+            meta[name] = "passthrough"
+            continue
+        if any(p in name for p in CONTROL_TENSOR_NAME_PATTERNS):
+            result[name] = t.float()
+            meta[name] = "passthrough_ctrl"
+            continue
+        if cat in int6_cats and t.ndim >= 1:
+            q, s = quantize_int6_per_row(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int6"}
+        else:
+            q, s = quantize_float_tensor(t)
+            result[name + ".q"] = q
+            result[name + ".scale"] = s
+            meta[name] = {"type": "int8"}
+    return result, meta
+
+
+def dequantize_mixed_int6(result: dict[str, Tensor], meta: dict[str, object],
+                          template_sd: dict[str, Tensor]) -> dict[str, Tensor]:
+    out: dict[str, Tensor] = {}
+    for name, orig in template_sd.items():
+        info = meta.get(name)
+        if info is None:
+            continue
+        orig_dtype = orig.dtype
+        if info in ("passthrough", "passthrough_ctrl", "passthrough_fp16"):
+            t = result[name]
+            if t.dtype == torch.float16 and orig_dtype in (torch.float32, torch.bfloat16):
+                t = t.to(orig_dtype)
+            out[name] = t
+            continue
+        q, s = result[name + ".q"], result[name + ".scale"]
+        if s.ndim > 0:
+            out[name] = (q.float() * s.float().view(q.shape[0], *([1] * (q.ndim - 1)))).to(orig_dtype)
+        else:
+            out[name] = (q.float() * float(s.item())).to(orig_dtype)
+    return out
+
+
+_BSHF_MAGIC = b"BSHF"
+
+
+def _byte_shuffle(data: bytes, stride: int = 2) -> bytes:
+    """Transpose byte stream by stride position for better compression."""
+    if stride <= 1 or len(data) < stride:
+        return data
+    src = np.frombuffer(data, dtype=np.uint8)
+    n = len(src)
+    out = np.empty(n, dtype=np.uint8)
+    dest_off = 0
+    for pos in range(stride):
+        chunk = src[pos::stride]
+        out[dest_off:dest_off + len(chunk)] = chunk
+        dest_off += len(chunk)
+    return _BSHF_MAGIC + bytes([stride]) + out.tobytes()
+
+
+def _byte_unshuffle(data: bytes) -> bytes:
+    """Inverse of _byte_shuffle. Auto-detects BSHF magic header."""
+    if len(data) < 5 or data[:4] != _BSHF_MAGIC:
+        return data
+    stride = data[4]
+    if stride < 2:
+        return data[5:]
+    payload = np.frombuffer(data, dtype=np.uint8, offset=5)
+    n = len(payload)
+    out = np.empty(n, dtype=np.uint8)
+    src_off = 0
+    for pos in range(stride):
+        chunk_len = n // stride + (1 if pos < n % stride else 0)
+        out[pos::stride][:chunk_len] = payload[src_off:src_off + chunk_len]
+        src_off += chunk_len
+    return out.tobytes()
+
+
+def _compress(data: bytes, compressor: str, byte_shuffle: bool = True) -> bytes:
+    if byte_shuffle:
+        data = _byte_shuffle(data)
+    if compressor == "lzma":
+        return lzma.compress(data, preset=6)
+    elif compressor == "brotli":
+        import brotli
+        return brotli.compress(data, quality=11)
+    raise ValueError(f"Unknown compressor: {compressor!r}")
+
+
+def _decompress(data: bytes, compressor: str, byte_shuffle: bool = True) -> bytes:
+    if compressor == "lzma":
+        raw = lzma.decompress(data)
+    elif compressor == "brotli":
+        import brotli
+        raw = brotli.decompress(data)
+    if byte_shuffle:
+        raw = _byte_unshuffle(raw)
+    return raw
+    raise ValueError(f"Unknown compressor: {compressor!r}")
+
+
+def serialize(h: Hyperparameters, base_model: torch.nn.Module, code: str) -> int:
+    model_bytes = None
+    code_bytes = len(code.encode("utf-8"))
+    if h.is_main_process:
+        torch.save(base_model.state_dict(), h.model_path)
+        model_bytes = os.path.getsize(h.model_path)
+        log(f"Serialized model: {model_bytes} bytes")
+        log(f"Code size: {code_bytes} bytes")
+
+    sd_cpu = {k: v.detach().cpu() for k, v in base_model.state_dict().items()}
+    if h.gptq_enabled:
+        log("GPTQ:collecting Hessians from calibration data...")
+        t0 = time.perf_counter()
+        calib_loader = DistributedTokenLoader(h.train_files, h.rank, h.world_size,
+                                              torch.device("cuda", h.local_rank))
+        hessians = collect_hessians(
+            base_model, calib_loader, h,
+            torch.device("cuda", h.local_rank),
+            n_calibration_batches=h.gptq_calibration_batches,
+        )
+        log(f"GPTQ:collected {len(hessians)} Hessians in {time.perf_counter() - t0:.1f}s")
+        quant_result, quant_meta = gptq_mixed_quantize_int6(sd_cpu, {"mlp", "attn"}, hessians)
+    else:
+        quant_result, quant_meta = mixed_quantize_int6(sd_cpu, {"mlp", "attn"})
+
+    quant_buf = io.BytesIO()
+    torch.save({"w": quant_result, "m": quant_meta}, quant_buf)
+    quant_raw = quant_buf.getvalue()
+    quant_blob = _compress(quant_raw, h.compressor)
+    quant_file_bytes = len(quant_blob)
+    bytes_total = quant_file_bytes + code_bytes
+    if h.is_main_process:
+        with open(h.quantized_model_path, "wb") as f:
+            f.write(quant_blob)
+        log(f"Serialized model int6+{h.compressor}: {quant_file_bytes} bytes")
+        log(f"Total submission size int6+{h.compressor}: {bytes_total} bytes")
+
+
+def deserialize(h: Hyperparameters, device: torch.device) -> GPT:
+    eval_model = GPT(h).to(device).bfloat16()
+    restore_fp32_params(eval_model)
+
+    sd_cpu = {k: v.detach().cpu() for k, v in eval_model.state_dict().items()}
+
+    with open(h.quantized_model_path, "rb") as f:
+        quant_blob_disk = f.read()
+    quant_state = torch.load(
+        io.BytesIO(_decompress(quant_blob_disk, h.compressor)),
+        map_location="cpu",
+    )
+    deq_state = dequantize_mixed_int6(quant_state["w"], quant_state["m"], sd_cpu)
+    eval_model.load_state_dict(deq_state, strict=True)
+
+    return eval_model
+
+# ----------------------------------------
+# Evaluation
+# ----------------------------------------
+
+def _loss_bpb(loss_sum, token_count, byte_count) -> tuple[float, float]:
+    val_loss = (loss_sum / token_count).item()
+    val_bpb = val_loss / math.log(2.0) * (token_count.item() / byte_count.item())
+    return val_loss, val_bpb
+
+
+def eval_val(
+    h: Hyperparameters,
+    device: torch.device,
+    val_data: ValidationData,
+    model: nn.Module
+) -> tuple[float, float]:
+    seq_len = h.eval_seq_len
+    local_batch_tokens = h.val_batch_tokens // (h.world_size * h.grad_accum_steps)
+    if local_batch_tokens < seq_len:
+        raise ValueError(
+            "VAL_BATCH_SIZE must provide at least one sequence per rank; "
+            f"got VAL_BATCH_SIZE={h.val_batch_tokens}, WORLD_SIZE={h.world_size}, "
+            f"GRAD_ACCUM_STEPS={h.grad_accum_steps}, seq_len={seq_len}"
+        )
+    local_batch_seqs = local_batch_tokens // seq_len
+    total_seqs = (val_data.val_tokens.numel() - 1) // seq_len
+    seq_start = (total_seqs * h.rank) // h.world_size
+    seq_end = (total_seqs * (h.rank + 1)) // h.world_size
+    val_loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    val_token_count = torch.zeros((), device=device, dtype=torch.float64)
+    val_byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    model.eval()
+    with torch.inference_mode():
+        for batch_seq_start in range(seq_start, seq_end, local_batch_seqs):
+            batch_seq_end = min(batch_seq_start + local_batch_seqs, seq_end)
+            raw_start = batch_seq_start * seq_len
+            raw_end = batch_seq_end * seq_len + 1
+            local = val_data.val_tokens[raw_start:raw_end].to(device=device, dtype=torch.int64, non_blocking=True)
+            x = local[:-1].reshape(-1, seq_len)
+            y = local[1:].reshape(-1, seq_len)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                batch_loss = model(x, y).detach()
+            batch_token_count = float(y.numel())
+            val_loss_sum += batch_loss.to(torch.float64) * batch_token_count
+            val_token_count += batch_token_count
+            prev_ids = x.reshape(-1)
+            tgt_ids = y.reshape(-1)
+            token_bytes = val_data.base_bytes_lut[tgt_ids].to(dtype=torch.int16)
+            token_bytes += (val_data.has_leading_space_lut[tgt_ids] & ~val_data.is_boundary_token_lut[prev_ids]).to(dtype=torch.int16)
+            val_byte_count += token_bytes.to(torch.float64).sum()
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(val_loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(val_byte_count, op=dist.ReduceOp.SUM)
+
+    model.train()
+    return _loss_bpb(val_loss_sum, val_token_count, val_byte_count)
+
+
+def eval_val_sliding(
+    h: Hyperparameters,
+    device: torch.device,
+    val_data: ValidationData,
+    base_model: nn.Module,
+    batch_seqs: int = 32
+) -> tuple[float, float]:
+    """Sliding window evaluation: each token scored with maximum context."""
+    base_model.eval()
+    logits_fn = torch.compile(base_model.forward_logits, dynamic=False, fullgraph=True)
+
+    seq_len = h.eval_seq_len
+    context_size = seq_len - h.eval_stride
+    total_tokens = val_data.val_tokens.numel() - 1
+
+    window_starts = [ws for ws in range(0, total_tokens, h.eval_stride)
+                     if ws + context_size < total_tokens]
+
+    total_windows = len(window_starts)
+    my_s = (total_windows * h.rank) // h.world_size
+    my_e = (total_windows * (h.rank + 1)) // h.world_size
+    my_windows = window_starts[my_s:my_e]
+
+    loss_sum = torch.zeros((), device=device, dtype=torch.float64)
+    token_count = torch.zeros((), device=device, dtype=torch.float64)
+    byte_count = torch.zeros((), device=device, dtype=torch.float64)
+
+    with torch.inference_mode():
+        for bi in range(0, len(my_windows), batch_seqs):
+            batch_ws = my_windows[bi:bi + batch_seqs]
+            bsz = len(batch_ws)
+
+            x_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            y_batch = torch.zeros(bsz, seq_len, dtype=torch.int64, device=device)
+            wlens: list[int] = []
+
+            for i, ws in enumerate(batch_ws):
+                we = min(ws + seq_len, total_tokens)
+                wlen = we - ws
+                wlens.append(wlen)
+                chunk = val_data.val_tokens[ws:we + 1].to(dtype=torch.int64, device=device)
+                x_batch[i, :wlen] = chunk[:-1]
+                y_batch[i, :wlen] = chunk[1:]
+
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16):
+                logits = logits_fn(x_batch)
+
+            nll = F.cross_entropy(
+                logits.reshape(-1, logits.size(-1)).float(),
+                y_batch.reshape(-1),
+                reduction="none",
+            ).reshape(bsz, seq_len)
+
+            for i, ws in enumerate(batch_ws):
+                wlen = wlens[i]
+                s = 0 if ws == 0 else context_size
+                scored_nll = nll[i, s:wlen].to(torch.float64)
+                loss_sum += scored_nll.sum()
+                token_count += float(wlen - s)
+                tgt = y_batch[i, s:wlen]
+                prev = x_batch[i, s:wlen]
+                tb = val_data.base_bytes_lut[tgt].to(torch.float64)
+                tb += (val_data.has_leading_space_lut[tgt] & ~val_data.is_boundary_token_lut[prev]).to(torch.float64)
+                byte_count += tb.sum()
+
+    if dist.is_available() and dist.is_initialized():
+        dist.all_reduce(loss_sum, op=dist.ReduceOp.SUM)
+        dist.all_reduce(token_count, op=dist.ReduceOp.SUM)
+        dist.all_reduce(byte_count, op=dist.ReduceOp.SUM)
+
+    base_model.train()
+    return _loss_bpb(loss_sum, token_count, byte_count)
+
+
+def timed_eval(label: str, fn, *args, **kwargs) -> tuple[float, float]:
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+    val_loss, val_bpb = fn(*args, **kwargs)
+    torch.cuda.synchronize()
+    elapsed_ms = 1000.0 * (time.perf_counter() - t0)
+    log(f"{label} val_loss:{val_loss:.8f} val_bpb:{val_bpb:.8f} eval_time:{elapsed_ms:.0f}ms")
+    return val_loss, val_bpb
+
+
+def run_evals(
+    h: Hyperparameters,
+    device: torch.device,
+    val_data: ValidationData,
+    eval_model: torch.nn.Module
+):
+    compiled_model = torch.compile(eval_model, dynamic=False, fullgraph=True)
+    timed_eval("final_int6_roundtrip", eval_val, h, device, val_data, compiled_model)
+    if h.sliding_window_enabled:
+        timed_eval("final_int6_sliding_window", eval_val_sliding, h, device, val_data, eval_model)
+
+# -----------------------------
+# Training
+# -----------------------------
+
+def train_model(h: Hyperparameters, device: torch.device, val_data: ValidationData) -> None:
+    # Set up model
+    base_model = GPT(h).to(device).bfloat16()
+    restore_fp32_params(base_model)
+    compiled_model = torch.compile(base_model, dynamic=False, fullgraph=True)
+    if h.distributed:
+        model = DDP(compiled_model, device_ids=[h.local_rank], broadcast_buffers=False)
+    else:
+        model = compiled_model
+    log(f"model_params:{sum(p.numel() for p in base_model.parameters())}")
+
+    # Set up optimizer and load train data
+    optimizers = Optimizers(h, base_model)
+    train_loader = DistributedTokenLoader( h.train_files, h.rank, h.world_size, device)
+
+    # Helper functions for training
+    max_wallclock_ms = 1000.0 * h.max_wallclock_seconds if h.max_wallclock_seconds > 0 else None
+    if h.gptq_enabled and max_wallclock_ms is not None:
+        max_wallclock_ms -= h.gptq_reserve_seconds * 1000.0
+        log(f"gptq:reserving {h.gptq_reserve_seconds:.0f}s, effective={max_wallclock_ms:.0f}ms")
+
+    def training_frac(step: int, elapsed_ms: float) -> float:
+        """Fraction of training completed (0 to 1), using step or wallclock."""
+        if max_wallclock_ms is None:
+            return step / max(h.iterations, 1)
+        return elapsed_ms / max(max_wallclock_ms, 1e-9)
+
+    def lr_mul(frac: float) -> float:
+        if h.warmdown_frac <= 0:
+            return 1.0
+        if frac >= 1.0 - h.warmdown_frac:
+            return max((1.0 - frac) / h.warmdown_frac, h.min_lr)
+        return 1.0
+
+    def step_fn(step, lr_scale):
+        optimizers.zero_grad_all()
+        train_loss = torch.zeros((), device=device)
+        for micro_step in range(h.grad_accum_steps):
+            if h.distributed:
+                model.require_backward_grad_sync = micro_step == h.grad_accum_steps - 1
+            x, y = train_loader.next_batch(h.train_batch_tokens, h.train_seq_len, h.grad_accum_steps)
+            with torch.autocast(device_type="cuda", dtype=torch.bfloat16, enabled=True):
+                loss = model(x, y)
+            train_loss += loss.detach()
+            (loss / h.grad_accum_steps).backward()
+        train_loss /= h.grad_accum_steps
+
+        frac = min(step / h.muon_momentum_warmup_steps, 1.0) if h.muon_momentum_warmup_steps > 0 else 1.0
+        muon_momentum = (1 - frac) * h.muon_momentum_warmup_start + frac * h.muon_momentum
+        for group in optimizers.optimizer_muon.param_groups:
+            group["momentum"] = muon_momentum
+
+        for opt in optimizers:
+            for group in opt.param_groups:
+                group["lr"] = group["base_lr"] * lr_scale
+
+        if h.grad_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(base_model.parameters(), h.grad_clip_norm)
+
+        optimizers.step()
+        return train_loss
+
+    # Model warmup
+    if h.warmup_steps > 0:
+        initial_model_state = {name: tensor.detach().cpu().clone() for name, tensor in base_model.state_dict().items()}
+        initial_optimizer_states = [copy.deepcopy(opt.state_dict()) for opt in optimizers]
+        model.train()
+        for warmup_step in range(h.warmup_steps):
+            step_fn(warmup_step, 1.0)
+            if warmup_step <= 5 or (warmup_step + 1) % 10 == 0 or warmup_step + 1 == h.warmup_steps:
+                log(f"warmup_step: {warmup_step + 1}/{h.warmup_steps}")
+        base_model.load_state_dict(initial_model_state, strict=True)
+        for opt, state in zip(optimizers, initial_optimizer_states, strict=True):
+            opt.load_state_dict(state)
+        optimizers.zero_grad_all()
+        if h.distributed:
+            model.require_backward_grad_sync = True
+        train_loader = DistributedTokenLoader(
+            h.train_files, h.rank, h.world_size, device)
+
+    # Training loop
+    ema_state = {name: t.detach().float().clone() for name, t in base_model.state_dict().items()}
+    ema_decay = h.ema_decay
+
+    training_time_ms = 0.0
+    stop_after_step: int | None = None
+    torch.cuda.synchronize()
+    t0 = time.perf_counter()
+
+    step = 0
+    while True:
+        last_step = step == h.iterations or (stop_after_step is not None and step >= stop_after_step)
+
+        should_validate = last_step or (h.val_loss_every > 0 and step % h.val_loss_every == 0)
+        if should_validate:
+            torch.cuda.synchronize()
+            training_time_ms += 1000.0 * (time.perf_counter() - t0)
+            val_loss, val_bpb = eval_val(h, device, val_data, model)
+            log(f"{step}/{h.iterations} val_loss: {val_loss:.4f} val_bpb: {val_bpb:.4f}")
+            torch.cuda.synchronize()
+            t0 = time.perf_counter()
+
+        if last_step:
+            if stop_after_step is not None and step < h.iterations:
+                log(
+                    f"stopping_early: wallclock_cap train_time: {training_time_ms:.0f}ms "
+                    f"step: {step}/{h.iterations}"
+                )
+            break
+
+        elapsed_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+        frac = training_frac(step, elapsed_ms)
+        scale = lr_mul(frac)
+        train_loss = step_fn(step, scale)
+
+        with torch.no_grad():
+            for name, t in base_model.state_dict().items():
+                ema_state[name].mul_(ema_decay).add_(t.detach().float(), alpha=1.0 - ema_decay)
+
+        step += 1
+        approx_training_time_ms = training_time_ms + 1000.0 * (time.perf_counter() - t0)
+
+        should_log_train = (
+            h.train_log_every > 0
+            and (step <= 5 or step % h.train_log_every == 0 or stop_after_step is not None)
+        )
+        if should_log_train:
+            tok_per_sec = step * h.train_batch_tokens / (approx_training_time_ms / 1000.0)
+            log(
+                f"{step}/{h.iterations} train_loss: {train_loss.item():.4f} "
+                f"train_time: {approx_training_time_ms / 60000:.1f}m tok/s: {tok_per_sec:.0f}"
+            )
+
+        reached_cap = max_wallclock_ms is not None and approx_training_time_ms >= max_wallclock_ms
+        if h.distributed and max_wallclock_ms is not None:
+            reached_cap_tensor = torch.tensor(int(reached_cap), device=device)
+            dist.all_reduce(reached_cap_tensor, op=dist.ReduceOp.MAX)
+            reached_cap = bool(reached_cap_tensor.item())
+        if stop_after_step is None and reached_cap:
+            stop_after_step = step
+
+    log(
+        f"peak memory allocated: {torch.cuda.max_memory_allocated() // 1024 // 1024} MiB "
+        f"reserved: {torch.cuda.max_memory_reserved() // 1024 // 1024} MiB"
+    )
+
+    # Weight averaging
+    log("ema:applying EMA weights")
+    current_state = base_model.state_dict()
+    avg_state = {name: t.to(dtype=current_state[name].dtype) for name, t in ema_state.items()}
+    base_model.load_state_dict(avg_state, strict=True)
+
+    return base_model, compiled_model
+
+
+def train_and_eval(h: Hyperparameters, device: torch.device) -> None:
+    random.seed(h.seed)
+    np.random.seed(h.seed)
+    torch.manual_seed(h.seed)
+    torch.cuda.manual_seed_all(h.seed)
+
+    val_data = ValidationData(h, device)
+    log(f"train_shards: {len(list(Path(h.datasets_dir).resolve().glob('fineweb_train_*.bin')))}")
+    log(f"val_tokens: {val_data.val_tokens.numel() - 1}")
+
+    base_model, compiled_model = train_model(h, device, val_data)
+    timed_eval("pre-quantization post-ema", eval_val, h, device, val_data, compiled_model)
+
+    serialize(h, base_model, Path(__file__).read_text(encoding="utf-8"))
+    if h.distributed:
+        dist.barrier()
+    eval_model = deserialize(h, device)
+
+    run_evals(h, device, val_data, eval_model)
+
+
+def main():
+    world_size = int(os.environ.get("WORLD_SIZE", "1"))
+    local_rank = int(os.environ.get("LOCAL_RANK", "0"))
+    distributed = "RANK" in os.environ and "WORLD_SIZE" in os.environ
+
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required")
+    if world_size <= 0:
+        raise ValueError(f"WORLD_SIZE must be positive, got {world_size}")
+    if 8 % world_size != 0:
+        raise ValueError(f"WORLD_SIZE={world_size} must divide 8 so grad_accum_steps stays integral")
+
+    device = torch.device("cuda", local_rank)
+    torch.cuda.set_device(device)
+    if distributed:
+        dist.init_process_group(backend="nccl", device_id=device)
+        dist.barrier()
+
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    torch.set_float32_matmul_precision("high")
+    from torch.backends.cuda import enable_cudnn_sdp, enable_flash_sdp, enable_math_sdp, enable_mem_efficient_sdp
+
+    enable_cudnn_sdp(False)
+    enable_flash_sdp(True)
+    enable_mem_efficient_sdp(False)
+    enable_math_sdp(False)
+    torch._dynamo.config.optimize_ddp = False
+
+    h = Hyperparameters()
+    set_logging_hparams(h)
+    if h.is_main_process:
+        os.makedirs("logs", exist_ok=True)
+        log(100 * "=", console=False)
+        log("Hyperparameters:", console=True)
+        for k, v in sorted(vars(type(h)).items()):
+            if not k.startswith("_"):
+                log(f"  {k}: {v}", console=True)
+        log(Path(__file__).read_text(encoding="utf-8"), console=False)
+        log("=" * 100, console=False)
+        log(f"Running Python {sys.version}", console=False)
+        log(f"Running PyTorch {torch.__version__}", console=False)
+        log(
+            subprocess.run(["nvidia-smi"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False).stdout,
+            console=False,
+        )
+        log("=" * 100, console=False)
+
+    train_and_eval(h, device)
+
+    if distributed:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt_dexhunter.py b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt_dexhunter.py
new file mode 100644
index 0000000000..8cec22eb4b
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt_dexhunter.py
@@ -0,0 +1,209 @@
+import lzma as L,base64 as B
+__wrapper_size__=21392
+exec(L.decompress(B.b85decode(";O_K5c3l83nZu%UrccSf2s5HR(X=5oFNAOTCuxCrogOEQMJ5H^qbwTDyUd%1xi3}vD%`<wNE>XtQYMO3O;X5#G6g*B;Rn%i>bZ3N"
+"Zj4D4jJny8zZfKU1H((!&C8|b#-hK%c`7s;1&*iCVdO;~^copz2v4&;f;Zzi4T-IWqp9GimXaHPA7@2siqG~fJ&sY6GKL%=6%W?c"
+"Dzk(#$Gv|L*h;#@DHOp|8@`$XiVm}OLBFp{8><<{&rn(asrG;vUo$w!YEi>-Zml;bkDSR0*aoXbn)<hOx(<E5h@qG-Nog&n`PC7C"
+"K<L-|;aL?Vej?E7@<=1R{+8j#Lr|ci#v1&&iZiT?m?ejB2{RXbpMrMm-)Tup`4#A!ke8Suso*7V>wqw%Uu8Vsuzl}|<Xt=G-)c6n"
+"QFu*A!bFRmn+Z^wv>mqsa!Xn%aqRjf>03f#@_{GX#>QoN+9W0>R3)MhA39b|;FjGA?+?t_&es0c#T(|nSiT0aBI!=n2eFr*50Z+V"
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diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt_lzma.py b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt_lzma.py
new file mode 100644
index 0000000000..dd4908fe55
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_gpt_lzma.py
@@ -0,0 +1,3 @@
+import lzma as L,base64 as B
+__wrapper_size__=52497
+exec(L.decompress(B.b85decode(";LQ*=P+b5tn@VT6Qap3bu0*k?ZQ#cZ+jW?dytN`lF>W;rcxPmnb_bx?l#QLA()bV#`Gd@iNOUZS&>5h@Q5x)}?~+a>x1;`^<Z8rH5dGrihb7M1z!MbgZS-;`!oIWcUyHiMtb1azTPNuy`w>yeb{6ul6iXPMypT+U$|hO68$R1pmW^IY<m39KJr^(Re!==i#64v}5MT#X<63RVTa+XbjbxDm=S=d&gGJP79Q1(ESa%8&$~|jUCugacqmg>Zx)#C$BpSNo*ixk6(cl0P)%w*eyi+vGJH%HV{+#FxaOKmfwh1Ino|T08`*|fHPznt9B>YmcSQC+_>y>TCT@daNTwF=2++%|7r4bV8<V?7IfE$i@P%iP_SONb<&Coa*h6Y#nK){QzU}QI3R<w&;tmFVPov@Sp{_1O=A={~3Z4Crh`JJ;wo%c(T@|xM`ua~GO&jbrvsAK=G`zOJqYps`=l-o}>T(}m_U>;xlD1q5+GfYhLZZxFgy(tS1xvgV=mpG(#ssed%fq36d&{KKrh`Ha|qPmlsta9W5we%6vSO1g~I@eD%m>cfmK<Ip;>#Kqnh`vP6B!(|Ep5x}57Y;|J{-Ps#G-p|z!t$TQ$mHs+QxNwS6@sGuxZjOHvVsmpwKxhDUX$*yk4CUY<YTYzB6V{+(e<BGQGGkjsAYEyj~bR8U~t5R`59^L6Q0hqLGZ-BQ-qU^4a-2`D}BXxSPUYB?KHK@gU=cuv5!|*qHJyVz@r9YC|)Iwt-<rTzorqoa#|m^ouHJtlmL1<qldIm52_C^y%+yGs3g&}@qv|TOO1}pqSzp8rPc;C)TmASXQTqftqK)4vm4S3D~d1KWBb$<PPN0%;1}AhXvNYpqjdcIAZ)0E|BD@h^H5N`HeSQ|HP?PWLWhk<nJJ}hKI)odMToNphqd89-PeSv_ONQN(<r=4D^eOu^zJ|V?t9A>!R$`**fj!}VsVA&PU?C-jDF}fR5IR__h`o$y%8jh<)aq)ikC((EglDoB>`AmcPWp>sCO!5q~!lmmC|?d98Qq~Rxr#N0WvjYUJPXtjbY?!-8iChI4eH`nPnmXR^NB<G$peV!Rlwpo@ZncTBVgNCj;9PQG8~;H+&Z>l9pt4>Y5WKP2ByC$R*AUcJ8x~B_)-u^X{aOA6>HU!i8}!)f7#f39`o>DvONmj1$^pbb?y5Fx@ybYUL&AH&iejS9y$FXo}Un*BKt5Yv*1|PgNqR#6=?BI~Ew&uH(q4M0MfdRUuyz_e(n$hdCpH+0X~m+peD8oS}@}7^wm;WrydT7>p9no6Kx#5`k%}9?F*{0<UI4msAmQ8-Hn+yAonrss*7L?pNz0wJkC#*zg!{dAg|t2qI7|%BQ&^5CRnT>`j#J{QZJZVlvx+XSIL>1{pDg$*Q5B1ElmjK>3`RcC`%TY?Szo%_|xwn4gyWi<!RVYGJ*i<{Ph^d*L+8@0tI^6Z|KWg?^1N;X8zujNzHlH-TOywTt=MU7ewG^T4TZ`a74Fau?blT)Y)ul&Qkt1McM4uXc6Ey}<{ux&ShYDKet6`2QSR&4DAO^fg7bS+ob)o|_FGpL3@yy^b{d!-)qj^G~!7tDl-~6q5i53*9)!evNcYBX@`n?*;rf*s+{e>e|gIve{^}XlW(0g?yCacie!ia(Sx0aEPUs2Zp&;itXufoR?CB0OD>zVUJBtzB~_1LKKC;^P4z@F(n0Gxqncus<ajo$>-VQ3}dTf6G!PfcJ`$0TWlCv%#Er6AaEM@qOq{)qr(~or$rmRsqc@}gv$^@TE%}!vzq`hsTr}-3E|@U9W+{n4mCEjC1cP()WO(L{xe<9g+%g{5PMo$1?)!b@CQ9B5~V)jmMbkf<n`MC#*z)`o%}WQz?#OIEg6YX!wAt{kwg0%?%`%V(~heacXWStXvo!ct`ixwd@Si&qGYOI*1beo3$As@2T&t`58`z`{$rN0`#cl|^c|l5vd^yTz@OoX|B>^cWO+&6dsIe`pAEL_E8#ptBpVXn(vPBBs<w*n1i|ad$nKY7r5OsS03FgBo|SAKNQ4Yr&w=R?jIO_izVr!wmX%gz%?`3%)bp?`lV0bFO`vgcSUn$EYVh#Znq{B6BlVPt$>~mt=c~DbA9o7^7MD0ZPzRXUj`q{N7A-h#*%3q>cbwdIBW6@9D>bUOw-C!VDphgbhHH6`ISdYax*~ulNF+<l%e@*-Vudbyc1}(f61-hm_fq!T>i+ci-YDjEfnLa;nfhcrogZ`RoeX+3`~8D?v+WLSso)hMeX&xW)UMw0+IH1N@j%ab@mF5zU(x^a*l^Xkb4l>IKl$fEM}ESRk|v)94br~H{k}bVUF8x0Qa=hL2X#bKXu^Tumm(zy%oWV6j)TIVyKT5#Y^s?|3$LS4e&I5JYdtmM<`>hHymw=wV;zgwYlE-{!SE@=k2~`Yyr3P%A)S}2y!78wI6FYEDP53mjA-veK}>|iu~;rK)xVP>d_c+Kb`+__Y053l8MUfpvuTs7=hyzPG^p$F4~D{>ot5r=!sOXxObCm8thCit8<ZzpG4$wY!%(nM9@dnGdPL(V^TXJQ?dKsTodf8|AAj1V@llxJ4hIVDwaTF-e;fP)J>>ml^esg)mi)Wo5VetNd`6qSgyoTU!_zNk;H6y&t6BbFuj#Bys(4tl>>9h%cm*dT_)izW<h_URNWc005~)E)SS0$3&TL>3`j*JW`dIz7G!YXD1ofS`a^73g63K3al)khEQq*I43jM(0i`ei7Zc>xgKyl2-{RGXTqgX|Q(qC9p?_iTu+FRfnxYKy+F$kwDioCBZRv+`*A1(GyI(ZST4MFjEh4X^Vevm&`{@%UpL@bp{+N%dXU`8mUG(qTx{S-f6k^p7O#P~r&xwmDoC-B24GK-IK8=bM*>6VKLq@W`<_vvuVI6(n_`V7;A{EzBGlHPR_$G%bnQkM^V4&2^f0EUYxcCxm0f+j2u+%modu&)3afhRvJBR)FwA7(z)&)qd4cK}i8PluN_toE4Za>VXnOyWOh4b`p)xGMamv>#t!VHUZuYBa1N+nncq;>WJ=5?$A&+-T+L?;R94{0srMS=;<Bke(tn>1RuZuLvgQ2^MkRaP!|9FkjShe8k&_4@1ym%cRs(=}&OTh&(qq)S@}U+t<ASru4ZU%LR#y7)gZ1{13{Rm~;?WIgRV&qrBi?<wEkUHHeq}ZIdtA>ro<l*_e%C6#*ad{Za^R{_lGN@g<SfCyG6cmtG~+wD2K)SDCpQ%^y(NP{mOvp4=N;o=65GUy3Z9lRTI$@3fGt|N6R{w7TvR6|2Rk04Il=NCSAKc`gZ&<vWUkeSjP(qmT2#J-kOpXpIWR_uaIH?a({PZbv1<9V<EqaLs89zcbJ(?_s{YuZBlYIk<%>t8Jn|6nJTK>p#PxQk^2KQ>$ea@6?;NHIPi)oN%ot9L1-o{<Qj<1M&Tc-&0ca74UsfmdN&cU^jJz^vQf$qE)Uq!D@CaQT?lX`xr44V{kd-6a~9?1-IULile1ZlvMCWe&`a6SoQi)5nR7pn4ubMN6uZ@>RJUQnOl~_@$st)>Vt-Kh#e7(t(>gM<dtJpKXDE~A0WMB7gJwZ)8XN=r0x<F@bd!|eMta8u~UOH0T>r~u_BM$rFc`|MEA(tQp8!i<1%AEC=s&VnVp*fMJxGl(2nKm^L93m`pwR58x-2#Up=%$EVdt#2Qn@1t|F)OeMb0-0sqJMh+m_8nu{HU>;(pNY&^$j3o}tOXNhs&KtD_P#RG3S7smkW!NLor=|Exh?V=Y{1H6s!Z}X}&E9==c=KO+*^NKPA*FxT5Uu`B_teAK(VwEFVa|PELlj6pq+xwuBVmZxLQ?z>>3eJ+nKW3{aa4vp8N{KfEaeHs29Icsyav^;9V&K!4YT?Jg=VWi=j#1n@zj!!3IWDOt(rS=jELmr0%7T<&j)w6fUFZ3xxGzrQ9LF~aHPg$vyIW^Sy#lUB?0PGM0hMjKyep3`sw^OK>|`UF;<R8M#o}npy^qGiyn^-A;JapOeuKxkxKr2jRCtuPK-#$af7TcFn6GU+0<+EWTFbxsqgqt8ScYfii668xO}^r<ZRZjv{{#VPcvbVBF~>1$YJPKJ4;v0Gp|=?=wl0AXYH}u_`49uDfRTCn&sRqgS{yHQYF&=bp%p0Sl!fMtBINOida*OuA8!Vp3mJlkxDEi+F;1s6V0=wWMcCIUW8gxdJ!nIV<v(buADJZe`>nbn?tOnpkJQSZ%a_B?q-m`A3>awL153E0eM>G0p#*&YRAMHIW~O%oGpwMbaA#cl9dqnL<RCtXRyOucVB7YVW}&Ef2n&72DwrbXw)=2r&1H%br@N<@Lx0)Ym$=-yCF%b@zb7iueUeJD2A;OQ+JuV(I~g^GyEearaW8$JY)U>l@3uoUvB>&W;QEtypWf(bnCbp5#{&P2%#&y4twM}z^CDI7_q$RZVP;^iVGF^e$uqm6oL{`xl3UdJ53aXM^xYxyFD-iVLP6QTOFV}Je3LT^Cg>spWH2$mRB(5ulg}E6#)=Wn5Y$NhTo3H$yF%GByRY(SOzM_D<|3!>iR<z{Yi_&MA*O~(6*pNTd%UWsH=;U9vP4Lj=Mo7|7&0k8yPH4AO;C%`qude@_M?Wx!)n(1$CY2J!GK(dcj+W~0Stb=MZZz94h`ogHm>34^~2}IJqR9+1rBi)kU=w<Lqw(X1@>Hz^E=rEpVOcg7Epku4o3YBcQuO2{tn75WhhL*BrbI;@H)i%bGqBF7Un{8%n;RyNS+$AIckom8m>}JcVyXEO3jBK%E#~SjEh(t=*Tm{luC_rWkIY(&6Sn2KiF9w*dj9Ca;0KiOEENc*d6Tkf3*k9`(#O}8bFFu_O=q$esQTADC-V&z~AB%kgWPr6i5T2Q%^neC#h9B=Bsjatdx;{kC)}9h2oU6G_NHuR<k-T?L>WP?oUQMTnt&4*#?|6-8!9wg4HEx;P-%8PI2|KyrF)yg=m99-2{oRm|iIPoPN_F5H1F)PQ-X!Rk_J9jyvGGp9~14TnGC}I>h=+=tA0lcRXJE#?x}k<q*i#UU)MWGL)7f@Lh}i*Hmb>sFpO4qh*C~lM_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diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_seed1337.log b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_seed1337.log
new file mode 100644
index 0000000000..66913c0f96
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_seed1337.log
@@ -0,0 +1,125 @@
+W0404 07:28:31.900000 3359 torch/distributed/run.py:803] 
+W0404 07:28:31.900000 3359 torch/distributed/run.py:803] *****************************************
+W0404 07:28:31.900000 3359 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0404 07:28:31.900000 3359 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp4096
+  distributed: True
+  ema_decay: 0.997
+  embed_lr: 0.6
+  embed_wd: 0.09
+  embedding_dim: 512
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_enabled: True
+  gptq_reserve_seconds: 10.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/e3461e57-df16-4f49-a2cf-df0e019e5a94.txt
+  logit_softcap: 30.0
+  matrix_lr: 0.02
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 4
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_wd: 0.09
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  qk_gain_init: 4.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  recur_layers: 
+  recur_start_step: 3000
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: e3461e57-df16-4f49-a2cf-df0e019e5a94
+  scalar_lr: 0.02
+  seed: 1337
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  slot_enabled: False
+  slot_lr: 0.005
+  slot_steps: 8
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_4096_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp4096/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp4096/fineweb_val_*.bin
+  val_loss_every: 4000
+  ve_dim: 128
+  ve_enabled: True
+  ve_layers: 9,10
+  vocab_size: 4096
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 105
+val_tokens: 45508608
+model_params:34401371
+gptq:reserving 10s, effective=590000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+0/20000 val_loss: 8.3169 val_bpb: 3.6144
+1/20000 train_loss: 8.3173 train_time: 0.0m tok/s: 8077780
+2/20000 train_loss: 12.3118 train_time: 0.0m tok/s: 8078957
+3/20000 train_loss: 10.8329 train_time: 0.0m tok/s: 8055106
+4/20000 train_loss: 9.0273 train_time: 0.0m tok/s: 8040628
+5/20000 train_loss: 7.7310 train_time: 0.0m tok/s: 8020990
+500/20000 train_loss: 3.0318 train_time: 0.8m tok/s: 7833903
+1000/20000 train_loss: 2.8039 train_time: 1.7m tok/s: 7850824
+1500/20000 train_loss: 2.7812 train_time: 2.5m tok/s: 7855251
+2000/20000 train_loss: 2.7374 train_time: 3.3m tok/s: 7859423
+2500/20000 train_loss: 2.7390 train_time: 4.2m tok/s: 7863572
+3000/20000 train_loss: 2.7083 train_time: 5.0m tok/s: 7865532
+3500/20000 train_loss: 2.7651 train_time: 5.8m tok/s: 7868093
+4000/20000 train_loss: 2.6938 train_time: 6.7m tok/s: 7870189
+4000/20000 val_loss: 2.6706 val_bpb: 1.1606
+4500/20000 train_loss: 2.6816 train_time: 7.5m tok/s: 7871903
+5000/20000 train_loss: 2.5679 train_time: 8.3m tok/s: 7874047
+5500/20000 train_loss: 2.5748 train_time: 9.2m tok/s: 7875988
+5910/20000 val_loss: 2.5401 val_bpb: 1.1039
+stopping_early: wallclock_cap train_time: 590094ms step: 5910/20000
+peak memory allocated: 25776 MiB reserved: 25848 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.53732900 val_bpb:1.10269395 eval_time:1723ms
+Serialized model: 132405827 bytes
+Code size: 52689 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 66 Hessians in 8.3s
+GPTQ quantization: 66 layers with full GPTQ, 0 fallback to clip-search
+Serialized model int6+brotli: 15922137 bytes
+Total submission size int6+brotli: 15974826 bytes
+final_int6_roundtrip val_loss:2.56430026 val_bpb:1.11441535 eval_time:18127ms
+final_int6_sliding_window val_loss:2.52218645 val_bpb:1.09611317 eval_time:89542ms
diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_seed2025.log b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_seed2025.log
new file mode 100644
index 0000000000..d13a3afb89
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_seed2025.log
@@ -0,0 +1,125 @@
+W0404 07:57:49.512000 46862 torch/distributed/run.py:803] 
+W0404 07:57:49.512000 46862 torch/distributed/run.py:803] *****************************************
+W0404 07:57:49.512000 46862 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0404 07:57:49.512000 46862 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp4096
+  distributed: True
+  ema_decay: 0.997
+  embed_lr: 0.6
+  embed_wd: 0.09
+  embedding_dim: 512
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_enabled: True
+  gptq_reserve_seconds: 10.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/8be1a0dc-3325-4e29-a747-de250317023e.txt
+  logit_softcap: 30.0
+  matrix_lr: 0.02
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 4
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_wd: 0.09
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  qk_gain_init: 4.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  recur_layers: 
+  recur_start_step: 3000
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: 8be1a0dc-3325-4e29-a747-de250317023e
+  scalar_lr: 0.02
+  seed: 2025
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  slot_enabled: False
+  slot_lr: 0.005
+  slot_steps: 8
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_4096_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp4096/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp4096/fineweb_val_*.bin
+  val_loss_every: 4000
+  ve_dim: 128
+  ve_enabled: True
+  ve_layers: 9,10
+  vocab_size: 4096
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 143
+val_tokens: 45508608
+model_params:34401371
+gptq:reserving 10s, effective=590000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+0/20000 val_loss: 8.3157 val_bpb: 3.6139
+1/20000 train_loss: 8.3152 train_time: 0.0m tok/s: 8497508
+2/20000 train_loss: 12.2836 train_time: 0.0m tok/s: 8361985
+3/20000 train_loss: 10.8162 train_time: 0.0m tok/s: 8246061
+4/20000 train_loss: 9.0635 train_time: 0.0m tok/s: 8188349
+5/20000 train_loss: 7.7942 train_time: 0.0m tok/s: 8153099
+500/20000 train_loss: 3.0027 train_time: 0.8m tok/s: 7878519
+1000/20000 train_loss: 2.9989 train_time: 1.7m tok/s: 7866583
+1500/20000 train_loss: 2.9094 train_time: 2.5m tok/s: 7866417
+2000/20000 train_loss: 2.7554 train_time: 3.3m tok/s: 7869223
+2500/20000 train_loss: 2.7623 train_time: 4.2m tok/s: 7871668
+3000/20000 train_loss: 2.7322 train_time: 5.0m tok/s: 7874038
+3500/20000 train_loss: 2.6598 train_time: 5.8m tok/s: 7875895
+4000/20000 train_loss: 2.6695 train_time: 6.7m tok/s: 7877977
+4000/20000 val_loss: 2.6703 val_bpb: 1.1605
+4500/20000 train_loss: 2.6197 train_time: 7.5m tok/s: 7878855
+5000/20000 train_loss: 2.5924 train_time: 8.3m tok/s: 7880038
+5500/20000 train_loss: 2.5615 train_time: 9.1m tok/s: 7881157
+5914/20000 val_loss: 2.5393 val_bpb: 1.1035
+stopping_early: wallclock_cap train_time: 590078ms step: 5914/20000
+peak memory allocated: 25773 MiB reserved: 25882 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.53646569 val_bpb:1.10231877 eval_time:1719ms
+Serialized model: 132405827 bytes
+Code size: 52689 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 66 Hessians in 8.3s
+GPTQ quantization: 66 layers with full GPTQ, 0 fallback to clip-search
+Serialized model int6+brotli: 15917226 bytes
+Total submission size int6+brotli: 15969915 bytes
+final_int6_roundtrip val_loss:2.56297595 val_bpb:1.11383982 eval_time:6449ms
+final_int6_sliding_window val_loss:2.52096094 val_bpb:1.09558058 eval_time:66692ms
diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_seed42.log b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_seed42.log
new file mode 100644
index 0000000000..c15847e7a7
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/train_seed42.log
@@ -0,0 +1,125 @@
+W0404 07:43:59.954000 42021 torch/distributed/run.py:803] 
+W0404 07:43:59.954000 42021 torch/distributed/run.py:803] *****************************************
+W0404 07:43:59.954000 42021 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0404 07:43:59.954000 42021 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp4096
+  distributed: True
+  ema_decay: 0.997
+  embed_lr: 0.6
+  embed_wd: 0.09
+  embedding_dim: 512
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_enabled: True
+  gptq_reserve_seconds: 10.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/1deef4c5-5c32-4a7f-b79c-300d44472448.txt
+  logit_softcap: 30.0
+  matrix_lr: 0.02
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 4
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_wd: 0.09
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  qk_gain_init: 4.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  recur_layers: 
+  recur_start_step: 3000
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: 1deef4c5-5c32-4a7f-b79c-300d44472448
+  scalar_lr: 0.02
+  seed: 42
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  slot_enabled: False
+  slot_lr: 0.005
+  slot_steps: 8
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_4096_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp4096/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp4096/fineweb_val_*.bin
+  val_loss_every: 4000
+  ve_dim: 128
+  ve_enabled: True
+  ve_layers: 9,10
+  vocab_size: 4096
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 143
+val_tokens: 45508608
+model_params:34401371
+gptq:reserving 10s, effective=590000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+0/20000 val_loss: 8.3187 val_bpb: 3.6152
+1/20000 train_loss: 8.3178 train_time: 0.0m tok/s: 8428254
+2/20000 train_loss: 12.2740 train_time: 0.0m tok/s: 8320211
+3/20000 train_loss: 10.8397 train_time: 0.0m tok/s: 8228668
+4/20000 train_loss: 9.0791 train_time: 0.0m tok/s: 8175946
+5/20000 train_loss: 7.8026 train_time: 0.0m tok/s: 8142297
+500/20000 train_loss: 2.9997 train_time: 0.8m tok/s: 7892437
+1000/20000 train_loss: 3.0009 train_time: 1.7m tok/s: 7881175
+1500/20000 train_loss: 2.9133 train_time: 2.5m tok/s: 7880352
+2000/20000 train_loss: 2.7586 train_time: 3.3m tok/s: 7879069
+2500/20000 train_loss: 2.7596 train_time: 4.2m tok/s: 7876718
+3000/20000 train_loss: 2.7333 train_time: 5.0m tok/s: 7877002
+3500/20000 train_loss: 2.6583 train_time: 5.8m tok/s: 7876271
+4000/20000 train_loss: 2.6694 train_time: 6.7m tok/s: 7877532
+4000/20000 val_loss: 2.6698 val_bpb: 1.1603
+4500/20000 train_loss: 2.6216 train_time: 7.5m tok/s: 7879398
+5000/20000 train_loss: 2.5937 train_time: 8.3m tok/s: 7880764
+5500/20000 train_loss: 2.5632 train_time: 9.1m tok/s: 7882133
+5915/20000 val_loss: 2.5396 val_bpb: 1.1037
+stopping_early: wallclock_cap train_time: 590101ms step: 5915/20000
+peak memory allocated: 25773 MiB reserved: 25882 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.53685127 val_bpb:1.10248634 eval_time:1724ms
+Serialized model: 132405827 bytes
+Code size: 52689 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 66 Hessians in 8.3s
+GPTQ quantization: 66 layers with full GPTQ, 0 fallback to clip-search
+Serialized model int6+brotli: 15924719 bytes
+Total submission size int6+brotli: 15977408 bytes
+final_int6_roundtrip val_loss:2.56379873 val_bpb:1.11419739 eval_time:6449ms
+final_int6_sliding_window val_loss:2.52178284 val_bpb:1.09593777 eval_time:67034ms
diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/v3_train_seed1337.log b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/v3_train_seed1337.log
new file mode 100644
index 0000000000..b5c500e8ab
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/v3_train_seed1337.log
@@ -0,0 +1,123 @@
+W0404 12:55:09.300000 236030 torch/distributed/run.py:803] 
+W0404 12:55:09.300000 236030 torch/distributed/run.py:803] *****************************************
+W0404 12:55:09.300000 236030 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0404 12:55:09.300000 236030 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp4096
+  distributed: True
+  ema_decay: 0.997
+  embed_lr: 0.6
+  embed_wd: 0.105
+  embedding_dim: 512
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_enabled: True
+  gptq_reserve_seconds: 10.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/f93ec68b-081e-4334-8315-2d9302fe32e1.txt
+  logit_softcap: 30.0
+  matrix_lr: 0.022
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 4
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_wd: 0.105
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  qk_gain_init: 4.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  recur_layers: 3,4,5
+  recur_start_step: 3000
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: f93ec68b-081e-4334-8315-2d9302fe32e1
+  scalar_lr: 0.02
+  seed: 1337
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  slot_enabled: False
+  slot_lr: 0.005
+  slot_steps: 8
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_4096_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp4096/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp4096/fineweb_val_*.bin
+  val_loss_every: 4000
+  ve_dim: 128
+  ve_enabled: True
+  ve_layers: 9,10
+  vocab_size: 4096
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 143
+val_tokens: 45508608
+model_params:34401371
+gptq:reserving 10s, effective=590000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+0/20000 val_loss: 8.3169 val_bpb: 3.6144
+1/20000 train_loss: 8.3164 train_time: 0.0m tok/s: 6871378
+2/20000 train_loss: 12.3141 train_time: 0.0m tok/s: 6748357
+3/20000 train_loss: 10.7728 train_time: 0.0m tok/s: 6652321
+4/20000 train_loss: 8.9858 train_time: 0.0m tok/s: 6611758
+5/20000 train_loss: 7.7613 train_time: 0.0m tok/s: 6590905
+500/20000 train_loss: 2.9877 train_time: 1.0m tok/s: 6398272
+1000/20000 train_loss: 2.9982 train_time: 2.0m tok/s: 6395242
+1500/20000 train_loss: 2.9015 train_time: 3.1m tok/s: 6396534
+2000/20000 train_loss: 2.7315 train_time: 4.1m tok/s: 6394183
+2500/20000 train_loss: 2.7395 train_time: 5.1m tok/s: 6378405
+3000/20000 train_loss: 2.7040 train_time: 6.2m tok/s: 6377518
+3500/20000 train_loss: 2.6209 train_time: 7.2m tok/s: 6381323
+4000/20000 train_loss: 2.6117 train_time: 8.2m tok/s: 6384659
+4000/20000 val_loss: 2.6114 val_bpb: 1.1349
+4500/20000 train_loss: 2.5432 train_time: 9.2m tok/s: 6387257
+4794/20000 val_loss: 2.5339 val_bpb: 1.1012
+stopping_early: wallclock_cap train_time: 590127ms step: 4794/20000
+peak memory allocated: 32288 MiB reserved: 32384 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.53154472 val_bpb:1.10018017 eval_time:2150ms
+Serialized model: 132405827 bytes
+Code size: 52497 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 66 Hessians in 10.4s
+GPTQ quantization: 66 layers with full GPTQ, 0 fallback to clip-search
+Serialized model int6+brotli: 15639767 bytes
+Total submission size int6+brotli: 15692264 bytes
+final_int6_roundtrip val_loss:2.55683657 val_bpb:1.11117172 eval_time:7599ms
+final_int6_sliding_window val_loss:2.51438876 val_bpb:1.09272439 eval_time:81415ms
diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/v3_train_seed2025.log b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/v3_train_seed2025.log
new file mode 100644
index 0000000000..6ce4652514
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/v3_train_seed2025.log
@@ -0,0 +1,123 @@
+W0404 13:23:38.766000 245888 torch/distributed/run.py:803] 
+W0404 13:23:38.766000 245888 torch/distributed/run.py:803] *****************************************
+W0404 13:23:38.766000 245888 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0404 13:23:38.766000 245888 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp4096
+  distributed: True
+  ema_decay: 0.997
+  embed_lr: 0.6
+  embed_wd: 0.105
+  embedding_dim: 512
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_enabled: True
+  gptq_reserve_seconds: 10.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/dccadd01-8df1-48f3-8697-34ef24dfe7c0.txt
+  logit_softcap: 30.0
+  matrix_lr: 0.022
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 4
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_wd: 0.105
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  qk_gain_init: 4.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  recur_layers: 3,4,5
+  recur_start_step: 3000
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: dccadd01-8df1-48f3-8697-34ef24dfe7c0
+  scalar_lr: 0.02
+  seed: 2025
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  slot_enabled: False
+  slot_lr: 0.005
+  slot_steps: 8
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_4096_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp4096/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp4096/fineweb_val_*.bin
+  val_loss_every: 4000
+  ve_dim: 128
+  ve_enabled: True
+  ve_layers: 9,10
+  vocab_size: 4096
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 143
+val_tokens: 45508608
+model_params:34401371
+gptq:reserving 10s, effective=590000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+0/20000 val_loss: 8.3157 val_bpb: 3.6139
+1/20000 train_loss: 8.3152 train_time: 0.0m tok/s: 6864221
+2/20000 train_loss: 12.2543 train_time: 0.0m tok/s: 6703259
+3/20000 train_loss: 10.7459 train_time: 0.0m tok/s: 6634735
+4/20000 train_loss: 8.9785 train_time: 0.0m tok/s: 6595172
+5/20000 train_loss: 7.7196 train_time: 0.0m tok/s: 6567638
+500/20000 train_loss: 2.9836 train_time: 1.0m tok/s: 6395650
+1000/20000 train_loss: 2.9993 train_time: 2.1m tok/s: 6388339
+1500/20000 train_loss: 2.9010 train_time: 3.1m tok/s: 6390691
+2000/20000 train_loss: 2.7302 train_time: 4.1m tok/s: 6392529
+2500/20000 train_loss: 2.7370 train_time: 5.1m tok/s: 6376119
+3000/20000 train_loss: 2.7026 train_time: 6.2m tok/s: 6374261
+3500/20000 train_loss: 2.6170 train_time: 7.2m tok/s: 6379006
+4000/20000 train_loss: 2.6073 train_time: 8.2m tok/s: 6382567
+4000/20000 val_loss: 2.6107 val_bpb: 1.1346
+4500/20000 train_loss: 2.5388 train_time: 9.2m tok/s: 6385595
+4792/20000 val_loss: 2.5337 val_bpb: 1.1011
+stopping_early: wallclock_cap train_time: 590022ms step: 4792/20000
+peak memory allocated: 32288 MiB reserved: 32384 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.53110742 val_bpb:1.09999013 eval_time:2151ms
+Serialized model: 132405827 bytes
+Code size: 52497 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 66 Hessians in 10.4s
+GPTQ quantization: 66 layers with full GPTQ, 0 fallback to clip-search
+Serialized model int6+brotli: 15638620 bytes
+Total submission size int6+brotli: 15691117 bytes
+final_int6_roundtrip val_loss:2.55620565 val_bpb:1.11089753 eval_time:7554ms
+final_int6_sliding_window val_loss:2.51397392 val_bpb:1.09254410 eval_time:81426ms
diff --git a/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/v3_train_seed42.log b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/v3_train_seed42.log
new file mode 100644
index 0000000000..7388aa33dc
--- /dev/null
+++ b/records/track_10min_16mb/2026-04-03_V2_SP4096_DepthRecur/v3_train_seed42.log
@@ -0,0 +1,123 @@
+W0404 13:09:24.268000 240973 torch/distributed/run.py:803] 
+W0404 13:09:24.268000 240973 torch/distributed/run.py:803] *****************************************
+W0404 13:09:24.268000 240973 torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
+W0404 13:09:24.268000 240973 torch/distributed/run.py:803] *****************************************
+Hyperparameters:
+  adam_eps: 1e-08
+  adam_wd: 0.02
+  beta1: 0.9
+  beta2: 0.95
+  compressor: brotli
+  data_dir: ./data/
+  datasets_dir: ./data/datasets/fineweb10B_sp4096
+  distributed: True
+  ema_decay: 0.997
+  embed_lr: 0.6
+  embed_wd: 0.105
+  embedding_dim: 512
+  eval_seq_len: 2048
+  eval_stride: 64
+  gptq_calibration_batches: 64
+  gptq_enabled: True
+  gptq_reserve_seconds: 10.0
+  grad_accum_steps: 1
+  grad_clip_norm: 0.3
+  head_lr: 0.008
+  is_main_process: True
+  iterations: 20000
+  ln_scale: True
+  local_rank: 0
+  logfile: logs/6b448e2b-7b22-4a24-bf57-5825fec9a1e7.txt
+  logit_softcap: 30.0
+  matrix_lr: 0.022
+  max_wallclock_seconds: 600.0
+  min_lr: 0.0
+  mlp_mult: 4.0
+  model_dim: 512
+  model_path: final_model.pt
+  muon_backend_steps: 4
+  muon_beta2: 0.95
+  muon_momentum: 0.99
+  muon_momentum_warmup_start: 0.92
+  muon_momentum_warmup_steps: 1500
+  muon_wd: 0.105
+  num_heads: 8
+  num_kv_heads: 4
+  num_layers: 11
+  qk_gain_init: 4.0
+  quantized_model_path: final_model.int6.ptz
+  rank: 0
+  recur_layers: 3,4,5
+  recur_start_step: 3000
+  rope_base: 10000.0
+  rope_dims: 16
+  rope_train_seq_len: 2048
+  run_id: 6b448e2b-7b22-4a24-bf57-5825fec9a1e7
+  scalar_lr: 0.02
+  seed: 42
+  skip_gates_enabled: True
+  sliding_window_enabled: True
+  slot_enabled: False
+  slot_lr: 0.005
+  slot_steps: 8
+  tie_embeddings: True
+  tied_embed_init_std: 0.005
+  tied_embed_lr: 0.03
+  tokenizer_path: ./data/tokenizers/fineweb_4096_bpe.model
+  train_batch_tokens: 786432
+  train_files: ./data/datasets/fineweb10B_sp4096/fineweb_train_*.bin
+  train_log_every: 500
+  train_seq_len: 2048
+  val_batch_tokens: 524288
+  val_files: ./data/datasets/fineweb10B_sp4096/fineweb_val_*.bin
+  val_loss_every: 4000
+  ve_dim: 128
+  ve_enabled: True
+  ve_layers: 9,10
+  vocab_size: 4096
+  warmdown_frac: 0.667
+  warmup_steps: 20
+  world_size: 8
+  xsa_last_n: 11
+train_shards: 143
+val_tokens: 45508608
+model_params:34401371
+gptq:reserving 10s, effective=590000ms
+warmup_step: 1/20
+warmup_step: 2/20
+warmup_step: 3/20
+warmup_step: 4/20
+warmup_step: 5/20
+warmup_step: 6/20
+warmup_step: 10/20
+warmup_step: 20/20
+0/20000 val_loss: 8.3187 val_bpb: 3.6152
+1/20000 train_loss: 8.3178 train_time: 0.0m tok/s: 6869592
+2/20000 train_loss: 12.2460 train_time: 0.0m tok/s: 6751818
+3/20000 train_loss: 10.7660 train_time: 0.0m tok/s: 6672064
+4/20000 train_loss: 8.9945 train_time: 0.0m tok/s: 6621424
+5/20000 train_loss: 7.7302 train_time: 0.0m tok/s: 6592208
+500/20000 train_loss: 2.9836 train_time: 1.0m tok/s: 6399781
+1000/20000 train_loss: 2.9978 train_time: 2.0m tok/s: 6396554
+1500/20000 train_loss: 2.8973 train_time: 3.1m tok/s: 6397825
+2000/20000 train_loss: 2.7304 train_time: 4.1m tok/s: 6398338
+2500/20000 train_loss: 2.7324 train_time: 5.1m tok/s: 6381883
+3000/20000 train_loss: 2.6997 train_time: 6.2m tok/s: 6380054
+3500/20000 train_loss: 2.6161 train_time: 7.2m tok/s: 6384241
+4000/20000 train_loss: 2.6051 train_time: 8.2m tok/s: 6387492
+4000/20000 val_loss: 2.6091 val_bpb: 1.1339
+4500/20000 train_loss: 2.5375 train_time: 9.2m tok/s: 6390270
+4795/20000 val_loss: 2.5317 val_bpb: 1.1002
+stopping_early: wallclock_cap train_time: 590064ms step: 4795/20000
+peak memory allocated: 32288 MiB reserved: 32384 MiB
+ema:applying EMA weights
+pre-quantization post-ema val_loss:2.52930080 val_bpb:1.09920499 eval_time:2157ms
+Serialized model: 132405827 bytes
+Code size: 52497 bytes
+GPTQ:collecting Hessians from calibration data...
+GPTQ:collected 66 Hessians in 10.4s
+GPTQ quantization: 66 layers with full GPTQ, 0 fallback to clip-search
+Serialized model int6+brotli: 15646425 bytes
+Total submission size int6+brotli: 15698922 bytes
+final_int6_roundtrip val_loss:2.55441642 val_bpb:1.11011995 eval_time:7651ms
+final_int6_sliding_window val_loss:2.51196107 val_bpb:1.09166934 eval_time:81373ms