operationrange/MiniMax-M2.7-BF16

MiniMax-M2.7 — BF16 (dequantized from FP8)

Plain bfloat16 weights of MiniMaxAI/MiniMax-M2.7, reconstructed from the upstream block-FP8 (E4M3, 128×128 blocks) checkpoint via shard-by-shard blockwise dequantization. No calibration, no rounding loss beyond the original FP8→BF16 cast — every block is materialized exactly:

bf16_block = (fp8_block.float() * scale_fp32).bfloat16()

Why this exists

MiniMaxAI/MiniMax-M2.7 ships natively in FP8. On Ampere and earlier (e.g. RTX A5000) FP8 tensor cores don't exist and inference engines have to emulate FP8 through FP16 — paying double bandwidth without the speed benefit. For further offline quantization (AWQ, GPTQ, RTN INT8, …) you need plain BF16 weights anyway: transformers + torch_dtype=bfloat16 won't materialize the attention projections under the FP8 quant config, which trips up llmcompressor's GPTQ tracer.

This repo is the missing intermediate: upstream MiniMax-M2.7 weights in plain BF16 safetensors, ready to be fed into any standard quantization pipeline.

Contents

• —47 shards model-NNNNN-of-00047.safetensors
• —rebuilt model.safetensors.index.json (no *.weight_scale_inv entries)
• —config.json with the upstream quantization_config stripped
• —tokenizer + custom modeling .py files copied verbatim from the FP8 source

Total ≈ 458 GB.

Provenance

Produced on a single 48 GB GPU pod (~30 minutes wall time) using a ~150-line script — see `dequant_fp8_blockwise.py`.

Process per shard:

1. 1.open model-XXXXX-of-00130.safetensors from the FP8 source
2. 2.for each *.weight (FP8 e4m3fn): look up *.weight_scale_inv (FP32, 128×128)
3. 3.broadcast scale to weight shape, multiply, cast to BF16
4. 4.drop the scale tensor
5. 5.write model-NNNNN-of-00047.safetensors (5 GB shards)

Other tensors (embeddings, layer norms, MoE routers/gates that were already unquantized in the upstream config's modules_to_not_convert) are passed through with a BF16 cast.

Quick load

from transformers import AutoModelForCausalLM, AutoTokenizer

m = AutoModelForCausalLM.from_pretrained(
    "operationrange/MiniMax-M2.7-BF16",
    torch_dtype="bfloat16",
    device_map="auto",
    trust_remote_code=True,
)
tok = AutoTokenizer.from_pretrained("operationrange/MiniMax-M2.7-BF16", trust_remote_code=True)

Inference at full BF16 needs ≥ ~470 GB combined GPU+CPU memory, so this checkpoint is mostly intended as a starting point for further compression (AWQ-INT4, GPTQ-INT8, etc.) rather than direct serving.

License

Inherits the MiniMax-M2 license from the upstream model. No weights were modified — only the storage format.