base_model: Youssofal/MiniMax-M2.7-abliterated-BF16 base_model_relation: quantized pipeline_tag: text-generation library_name: transformers tags:

• fp8
• compressed-tensors
• llm-compressor
• vllm
• minimax
• moe
• abliterated
• heretic
• uncensored license: other license_name: minimax-m-license

MiniMax-M2.7 Abliterated Heretic — FP8

FP8 dynamic (per-channel weight, per-token activation) quantization of Youssofal/MiniMax-M2.7-abliterated-BF16, which is itself a Heretic-method abliteration of MiniMaxAI/MiniMax-M2.7.

Lineage

• Base model: MiniMaxAI/MiniMax-M2.7 (229B MoE, 10B active, 256 experts, 200K ctx)
• Abliteration: Youssofal/MiniMax-M2.7-abliterated-BF16 — Heretic / Ablated Refusal Adaptation (ARA)
• This repo: FP8 quant of the above for fast vLLM inference

Format

• Weights: float8_e4m3fn, per-output-channel symmetric scales (float32)
• Activations: dynamic per-token FP8 at runtime
• KV cache: run with --kv-cache-dtype fp8 for full FP8 serving
• Config: compressed-tensors / format: float-quantized, ignored: lm_head
• Tensors: 96,165 total, 47 safetensors shards, ~230 GB

Serve with vLLM

vllm serve LittleNicky55/MiniMax-M2.7-abliterated-Heretic-FP8 \
  --tensor-parallel-size 2 \
  --dtype bfloat16 \
  --kv-cache-dtype fp8 \
  --max-model-len 196608 \
  --gpu-memory-utilization 0.92 \
  --trust-remote-code \
  --enable-prefix-caching

Fits comfortably in 2× H200 141GB (total VRAM budget ~230 GB + KV + compute).

Quantization method

Streaming per-shard quantization script: for each Linear weight W, compute per-output-channel scale = |W|.amax(dim=1) / 448.0, then W_fp8 = (W / scale).to(fp8_e4m3fn). No calibration data required (FP8_DYNAMIC scheme).

License

Inherits the non-commercial MiniMax M-Series license from the base model.