snoweddy/MiniMax-M2.7-Abliterated-Heretic-Q8_0-GGUF

MiniMax-M2.7-Abliterated-Heretic-GGUF

This is a GGUF release of an abliterated version of MiniMaxAI's MiniMax-M2.7.

By applying Heretic's Ablated Refusal Adaptation (ARA), the base refusal behavior was removed at the weight level. The result keeps MiniMax-M2.7's sparse MoE reasoning, long-context instruction following, and general capability profile, but no longer defaults to the original refusal pattern.

Methodology & Model Notes

MiniMax-M2.7 is a 229B sparse MoE model with 10B active parameters per token, 62 layers, hybrid attention, 256 local experts with 8 active per token, and a 200K context window.

This release was produced with a direct Heretic ARA run using the fixed parameter set below:

• 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

The direct ARA run completed with Refusals: 0/25.

The resulting abliterated checkpoint was exported to BF16 and then converted to GGUF for llama.cpp-compatible deployment.

Files

• MiniMax-M2.7-abliterated-BF16/: BF16 GGUF split into 10 parts
• MiniMax-M2.7-abliterated-Q8_0/: Q8_0 GGUF split into 5 parts
• MiniMax-M2.7-abliterated-Q3_K_M/: Q3_K_M GGUF split for Hub delivery
• Additional quants will be added from the same abliterated BF16 GGUF source

Prompt Format

]~!b[]~b]system
{system_prompt}[e~[
]~b]user
{prompt}[e~[
]~b]ai
<think>

Running

llama-server \
  -m <quant-file.gguf> \
  -ngl 999 -c 32768 --jinja \
  --reasoning-format auto -fa \
  --temp 1.0 --top-p 0.95 --top-k 40

Model Architecture

Spec	Value
Total Parameters	229B (sparse MoE)
Active Parameters	10B per token
Experts	256 local, 8 per token
Layers	62
Attention	Hybrid: 7 Lightning + 1 softmax per 8-block
Context	200K
Base Model	MiniMaxAI/MiniMax-M2.7

Disclaimer

This model has had refusal behavior removed at the weight level. It will answer prompts that the base model would normally refuse. You are responsible for how you use it.

Credits

• Base model: MiniMaxAI/MiniMax-M2.7
• Refusal removal pipeline: Heretic with the ARA method
• GGUF runtime and quantization: llama.cpp

License

This release inherits the base MiniMax-M2.7 license.

NON-COMMERCIAL. Commercial use requires written authorization from MiniMax.