MiniMax-M2.1-PRISM (UNCENSORED)

** MiniMax-M2.1 Uncensored PRISM Advanced Abliteration**

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Sponsorship opportunities include:

• Priority abliteration of models
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Thank you for believing in true Open AI.

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Model Description

MiniMax-M2.1-PRISM is the fully uncensored version of MiniMax-M2.1, using our State of the ART PRISM pipeline (Projected Refusal Isolation via Subspace Modification) to remove refusal behaviors while preserving and even enhancing full model capabilities.

Base Model: MiniMax-M2.1

MiniMax-M2.1 is an open-source agentic language model designed for robust performance in:

• Coding and software engineering
• Tool use and multi-step reasoning
• Instruction following
• Long-horizon planning
• Multilingual capabilities

Architecture: 229B parameters, 62 layers, 256 experts (8 active per token)

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PRISM Methodology

Method: Projected Refusal Isolation via Subspace Modification

This model was abliterated using PRISM - a state-of-the-art abliteration methodology combining multiple principled techniques for effective refusal removal while preserving & enhancing model capabilities.

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Performance Benchmarks

Base Model Performance

Benchmark	Score
SWE-bench Verified	74.0
SWE-bench Multilingual	72.5
VIBE Average	88.6
MMLU-Pro	88.0
GPQA-D	83.0
AIME25	83.0

PRISM Abliteration Results

Metric	Result
Adversarial Bench Prompts Responded	4096/4096 (100%)
Benign + Long Chain Coherence	100%
Response Quality	Full technical accuracy validated

Our testing shows that PRISM abliteration maintains full model coherence with no capability degradation and MMLU increases of 5-8%.

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Available Formats (contact for full tensors | additional quant work)

Format	Size	Description
GGUF IQ1_S	~43 GB	Quantized with importance matrix
Safetensors (BF16)	~426 GB	Full precision, 92 shards

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Recommended Inference Parameters

temperature = 1.0
top_p = 0.95
top_k = 40

Default System Prompt

You are a helpful assistant.

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Recommended Inference Frameworks

1. SGLang (recommended for full precision)
2. vLLM (recommended for full precision)
3. llama.cpp (recommended for GGUF quantized)
4. Transformers

llama.cpp Example

./llama-cli -m MiniMax-M2.1-PRISM-IQ1_S.gguf -ngl 99 -i -cnv --temp 0.7 --ctx-size 4096

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Ethical Considerations

This model has been modified to reduce safety guardrails. Users are responsible for:

• Complying with all applicable laws and regulations
• Not using the model for illegal activities
• Understanding the potential risks of unrestricted AI responses
• Implementing appropriate safeguards in production environments

Motivation: This project exists as research and development experimentation into understanding how large language models encode and enforce refusal behaviors, contributing to broader AI safety research by providing empirical data on refusal mechanism localization and tradeoffs between safety and capability.

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License

This model inherits the Modified-MIT License from the base MiniMax-M2.1 model.

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Credits

• Base Model: MiniMax-M2.1 by MiniMax AI
• PRISM Abliteration: Ex0bit
• Quantization: Using llama.cpp with unsloth imatrix

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Support

If you find this work useful, please consider supporting development so I can continue putting out the best models for the community:

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Contact

For questions or issues, please open an issue on this repository.