MiniMax-M2.7-abliterated-heretic-ara-AWQ

AWQ W4A16 (group_size 128, symmetric) quantization of Youssofal/MiniMax-M2.7-abliterated-BF16 — a Heretic-ARA abliterated derivative of MiniMaxAI/MiniMax-M2.7.

⚠️ Decensored model. Safety guardrails have been deliberately removed. Research and experimentation only. See full disclaimer below.

Quantization Details

Parameter	Value
Method	AWQ (Activation-aware Weight Quantization)
Scheme	W4A16 (symmetric)
Weight Bits	4
Activation Bits	16
Group Size	128
Format	compressed-tensors
Calibration Dataset	HuggingFaceH4/ultrachat_200k
Calibration Samples	128
Max Sequence Length	512
Router Gates	Unquantized (full precision)
LM Head	Unquantized (full precision)
Experts Calibrated	All 256 per layer (custom all-experts patch)
Compatible Inference Engine	vLLM

Quantization Notes

• Router gates kept full precision: MiniMax-M2's MoE uses sigmoid routing with an e_score_correction_bias. Quantizing the gate or this bias destroys routing decisions and produces multilingual gibberish. Both are explicitly excluded from quantization.
• All 256 experts calibrated: With top-8 routing on a 256-expert model, naive AWQ leaves most experts with insufficient calibration data. This quantization uses a custom MoE forward patch during calibration that runs every expert on every batch (sparse for routed tokens, with a small dummy forward for unrouted experts to fire activation hooks).
• QK-Norm absorbs Q/K smoothing: MiniMax-M2 has use_qk_norm=true. The RMSNorm weights of q_norm/k_norm absorb AWQ's per-channel scales applied to q_proj/k_proj outputs, preserving correctness.
• GQA v→o smoothing skipped: With 8 KV heads and 48 query heads, the v_proj output dimensions don't match o_proj input dimensions for per-channel smoothing. llm-compressor correctly identifies this incompatibility and skips it.
• MTP heads not preserved: The base checkpoint contains multi-token prediction (MTP) module weights that HF's MiniMaxM2ForCausalLM doesn't use. These are dropped from the quantized model.

Deployment

Recommended inference with vLLM:

vllm serve alonsoko/MiniMax-M2.7-abliterated-heretic-ara-AWQ \
    --trust-remote-code \
    --tensor-parallel-size 4 \
    --tool-call-parser minimax_m2 \
    --reasoning-parser minimax_m2 \
    --enable-auto-tool-choice

Recommended sampling: temperature=1.0, top_p=0.95, top_k=40 (per upstream MiniMax-M2 guidance).

MiniMax-M2 is an interleaved thinking model — when chaining assistant turns, preserve <think>...</think> blocks from prior turns in the message history.

Hardware Requirements

Approximate VRAM for inference at this quantization (W4A16-G128):

• Weights: ~115 GB
• KV cache (per request, varies with context length): ~2-8 GB
• Recommended: 2× 80GB GPUs (e.g., A100/H100) with --tensor-parallel-size 2, or 4× 48GB GPUs (e.g., L40S/A6000) with --tensor-parallel-size 4
• Minimum: A single 141GB H200 should fit weights + modest context

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This is a decensored version of MiniMaxAI/MiniMax-M2.7, made using Heretic v1.2.0+custom with the Arbitrary-Rank Ablation (ARA) method

⚠️ Disclaimer

This model is intended for research, experimentation, and testing purposes only.

• This model may produce harmful, offensive, inappropriate, or otherwise objectionable content.
• The abliteration process removes safety guardrails that were intentionally built into the original model.
• Do not use this model in production systems, consumer-facing applications, or any context where harmful outputs could cause real-world harm.
• The authors and contributors of this toolkit bear no responsibility for any misuse of this model or any harm caused by outputs generated by this model.
• By using this model, you agree that you are solely responsible for ensuring its use complies with all applicable laws and ethical guidelines.

This model is shared purely for academic and technical exploration of model internals.

Abliteration parameters

Parameter	Value
start_layer_index	30
end_layer_index	51
preserve_good_behavior_weight	0.4512
steer_bad_behavior_weight	0.0037
overcorrect_relative_weight	0.8804
neighbor_count	14

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About the Base Model

Original model: MiniMaxAI/MiniMax-M2.7

MiniMax-M2.7 is a 230B-parameter sparse MoE (10B active) built for agentic workflows, coding, and tool use. It uses interleaved thinking with <think>...</think> blocks. See the base model card for capabilities, benchmarks, and deployment details.