Every contribution directly funds my time & resources for the next major SOTA release.
Interested in Sponsoring?
If you're a company, research lab, or individual and want to see specific models or support this research at scale, I'd love to hear from you.
Sponsorship opportunities include:
Priority abliteration of models
Custom PRISM use-case configurations
Early access to new releases
Your logo/credit on model cards
📧 Reach out: Open a discussion on this repo or connect via Ko-fi
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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)
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.
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%.
Available Formats (contact for full tensors | additional quant work)
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.
