[docs] gguf

docs/source/en/quantization/gguf.md

@@ -25,6 +25,14 @@ rendered properly in your Markdown viewer.
 
 ## Load GGUF models
 
+Install [kernels](https://huggingface.co/docs/kernels/index), otherwise the packed path falls back to full [dequantization](#dequantize) at load.
+
+```bash
+pip install kernels
+```
+
+Weights stay packed when the Hub kernel [transformers-community/ggml-quantization](https://huggingface.co/transformers-community/ggml-quantization) is available. The loader defaults to MPS when that kernel is present and runs matmuls directly on the packed blocks. If the kernel isn't available, the model is dequantized at load.
+
 ```py
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -35,14 +43,21 @@ model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename)
 tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename)
 ```
 
-The weights only stay in their GGUF blocks on Metal (MPS) devices, where the [llama.cpp](https://github.com/ggerganov/llama.cpp) kernels, fetched from the Hub, run the matmuls directly on the packed blocks to keep inference fast.
+The same load works for Qwen3.5 MoE.
+
+```py
+moe_model_id = "unsloth/Qwen3.5-35B-A3B-GGUF"
+moe_filename = "Qwen3.5-35B-A3B-Q4_K_M.gguf"
+
+moe_model = AutoModelForCausalLM.from_pretrained(moe_model_id, gguf_file=moe_filename)
+moe_tokenizer = AutoTokenizer.from_pretrained(moe_model_id, gguf_file=moe_filename)
+```
 
-Right now, the only architecture supported is Qwen3.5. Everything else falls back. On another device or quantization type, the model is [dequantized](#dequantize) at load time, and an architecture that isn't supported yet goes through the legacy loader.
+The packed path currently supports Qwen3.5 and Qwen3.5 MoE. Packed loads use float32 automatically because it's faster on MPS. If you set another `dtype`, the loader returns a warning. Other architectures go through the legacy loader.
 
 ## Attention
 
-On Metal, attention has a ggml kernel too: [ggml-attn](https://huggingface.co/transformers-community/ggml-attn), the same
-flash attention llama.cpp runs, for both decode and prefill.
+On Metal, with kernels installed, attention can use [ggml-attn](https://huggingface.co/transformers-community/ggml-attn), the same flash-attention kernel llama.cpp uses for decode and prefill.
 
 ```py
 model = AutoModelForCausalLM.from_pretrained(
@@ -65,16 +80,16 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 ```
 
-The model that comes out is a regular dense model, so this is also how you take a GGUF checkpoint into the dtype you passed. 
+You get a regular dense model in the `dtype` you passed.
 
-Architectures other than Qwen3.5 are read by the legacy loader which dequantize the model also.
+Architectures other than Qwen3.5 and Qwen3.5 MoE go through the legacy loader, which always dequantizes.
 
 > [!TIP]
 > The legacy loader supports Llama, Mistral, Qwen2, Qwen2Moe, Phi3, Bloom, Falcon, StableLM, GPT2, Starcoder2, and [more](https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/ggml.py).
 
 ## Serve
 
-[`transformers serve`](../serve-cli/serving) names a GGUF model `<repo>:<file>.gguf`, since a repository holds
+[transformers serve](../serve-cli/serving) lists each `.gguf` file in a repository as its own model. A GGUF model is named `<repo>:<file>.gguf`, since a repository holds
 several quantizations and the id has to say which one to load. Requests name it the same way.
 
 ```shell

docs/source/en/quantization/overview.md

@@ -32,7 +32,7 @@ Use the Space below to help you pick a quantization method depending on your har
 | [EETQ](./eetq)                            | 🟢                   | 🔴              | 🟢        | 🔴        | 🔴                                 | 🔴              | ?               | 8            | 🟢               | 🟢                          | 🟢                      | https://github.com/NetEase-FuXi/EETQ        |
 | [Four Over Six](./fouroversix)            | 🟢                   | 🟢              | 🟢        | 🔴        | 🔴                                 | 🔴              | 🟢              | 4            | 🔴               | 🟢                          | 🟢                      | https://github.com/mit-han-lab/fouroversix |
 | [FP-Quant](./fp_quant)                          | 🟢                   | 🔴              | 🟢        | 🔴        | 🔴                                 | 🔴              | 🟢              | 4           | 🔴               | 🟢                          | 🟢                      | https://github.com/IST-DASLab/FP-Quant      |
-| [GGUF / GGML (llama.cpp)](./gguf)        | 🟢                   | 🟢              | 🟢        | 🔴        | 🟢                                 | 🟢              | 🟢              | 1/8          | 🔴               | 🔴                          | [See Notes](./gguf) | https://github.com/ggerganov/llama.cpp      |
+| [GGUF / GGML (llama.cpp)](./gguf)        | 🔴                   | 🟢              | 🟢        | 🔴        | 🟢                                 | 🟢              | 🟢              | 1/8          | 🔴               | 🔴                          | [See Notes](./gguf) | https://github.com/ggerganov/llama.cpp      |
 | [GPT-QModel](./gptq)                     | 🔴                   | 🟢 | 🟢        | 🟢        | 🟢                                 | 🟢 | 🔴              | 2/3/4/8      | 🟢               | 🟢                          | 🟢                      | https://github.com/ModelCloud/GPTQModel        |
 | [HIGGS](./higgs)                          | 🟢                   | 🔴              | 🟢        | 🔴        | 🔴                                 | 🔴              | 🟢              | 2/4          | 🔴               | 🟢                          | 🟢                      | https://github.com/HanGuo97/flute           |
 | [HQQ](./hqq)                              | 🟢                   | 🟢              | 🟢        | 🔴        | 🔴                                 | 🟢              | 🟢              | 1/8          | 🟢               | 🔴                          | 🟢                      | https://github.com/mobiusml/hqq/            |