Add GGUF support for MiniMax-M2.1 model

[JoursBleu]

What does this PR do?

Add GGUF loading support for MiniMax-M2.1 (456B MoE) model.

MiniMax-M2.1 is a large Mixture-of-Experts model with 456B total parameters (45.9B active), 256 experts and 8 experts per token. This PR enables loading its GGUF-quantized checkpoints (e.g. unsloth/MiniMax-M2.1-GGUF) via from_pretrained(..., gguf_file=...).

Changes

src/transformers/integrations/ggml.py

• Add "minimax_m2" entry to GGUF_CONFIG_MAPPING with model-specific config fields (including MoE fields: expert_count, expert_used_count, expert_feed_forward_length, expert_gating_func).
• Convert expert_gating_func integer (from GGUF metadata) to scoring_func string ({0: "none", 1: "softmax", 2: "sigmoid"}).
• Register GGUFQwen2Converter for minimax_m2 in GGUF_TO_FAST_CONVERTERS (tokenizer is compatible with Qwen2).

src/transformers/models/minimax_m2/modeling_minimax_m2.py

• Add _checkpoint_conversion_mapping = {"block_sparse_moe": "mlp"} to MiniMaxM2PreTrainedModel. This enables vLLM's gguf_loader revert_hf_rename to convert dummy model parameter names (mlp.*) back to checkpoint-compatible names (block_sparse_moe.*), bridging the naming gap between the transformers native model and the HF safetensors checkpoint without modifying vLLM model code.

src/transformers/modeling_gguf_pytorch_utils.py

• Add MiniMaxM2TensorProcessor class following the TensorProcessor API introduced in Qwen2/3 MoE + GGUF model support (restored) #42854 (same pattern as Qwen2MoeTensorProcessor):
  • preprocess_name(): strips per-expert indices from HF weight names so that multiple experts can map to one fused GGUF tensor.
  • perform_fallback_tensor_mapping(): maps merged gate_up_proj to both ffn_gate_exps and ffn_up_exps GGUF tensors; maps e_score_correction_bias to exp_probs_b.bias.
  • process(): matches GGUF MoE expert tensors and merges gate+up into gate_up_proj [num_experts, 2*intermediate, hidden].
  • _set_moe_expert_tensor(): merges gate and up weights into the fused gate_up_proj tensor, passes down weights directly.
• Register processor in TENSOR_PROCESSORS, add model type and architecture mappings.

Testing

Due to the model size (456B parameters, 227GB for Q8_0 GGUF), no CI-compatible unit tests are included. This is consistent with other large MoE models (e.g., Qwen3-30B-A3B in #42854).

Verified end-to-end on 8×AMD W7900D (48GB each) via vLLM serving the Q8_0 GGUF checkpoint:

• GSM8K 8-shot: 91.5% (official BF16 baseline: 92.0%)
• MMLU 5-shot: 85.66% (official BF16 baseline: 86.2%)

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you read the contributor guideline, Pull Request section?
• Was this discussed/approved via a Github issue or the forum? Please add a link to it if that's the case.
• Did you make sure to update the documentation with your changes? Here are the documentation guidelines, and here are tips on formatting docstrings.
• Did you write any new necessary tests?

Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR.

@SunMarc @MekkCyber @ArthurZucker

[github-actions]

[For maintainers] Suggested jobs to run (before merge)

run-slow: minimax_m2