Instructions to use Ex0bit/MiniMax-M2.1-PRISM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Ex0bit/MiniMax-M2.1-PRISM with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M
# Run inference directly in the terminal:
llama cli -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M
# Run inference directly in the terminal:
llama cli -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

Use Docker

docker model run hf.co/Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

LM Studio
Jan

vLLM

How to use Ex0bit/MiniMax-M2.1-PRISM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ex0bit/MiniMax-M2.1-PRISM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ex0bit/MiniMax-M2.1-PRISM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

Ollama
How to use Ex0bit/MiniMax-M2.1-PRISM with Ollama:
```
ollama run hf.co/Ex0bit/MiniMax-M2.1-PRISM:IQ2_M
```

Unsloth Studio

How to use Ex0bit/MiniMax-M2.1-PRISM with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ex0bit/MiniMax-M2.1-PRISM to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ex0bit/MiniMax-M2.1-PRISM to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ex0bit/MiniMax-M2.1-PRISM to start chatting

How to use Ex0bit/MiniMax-M2.1-PRISM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Ex0bit/MiniMax-M2.1-PRISM:IQ2_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Ex0bit/MiniMax-M2.1-PRISM with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Ex0bit/MiniMax-M2.1-PRISM:IQ2_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Ex0bit/MiniMax-M2.1-PRISM with Docker Model Runner:
```
docker model run hf.co/Ex0bit/MiniMax-M2.1-PRISM:IQ2_M
```

Lemonade

How to use Ex0bit/MiniMax-M2.1-PRISM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

Run and chat with the model

lemonade run user.MiniMax-M2.1-PRISM-IQ2_M

List all available models

lemonade list

Hermes Agent

How to use Ex0bit/MiniMax-M2.1-PRISM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Ex0bit/MiniMax-M2.1-PRISM:IQ2_M

Run Hermes

hermes

Atomic Chat

MiniMax-M2.1-PRISM / README.md

Ex0bit

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	---
	license: mit
	base_model: MiniMaxAI/MiniMax-M2.1
	tags:
	- abliterated
	- uncensored
	- prism
	- minimax
	- moe
	- finetune
	language:
	- en
	- zh
	pipeline_tag: text-generation
	---

	# MiniMax-M2.1-PRISM (UNCENSORED)

	MiniMax-M2.1 Uncensored PRISM Advanced Abliteration


	---
	<div align="center">

	### ☕ Support Our Work

	If you enjou our work and find it useful, please consider sponsoring or supporting us!

	[![Ko-fi](https://img.shields.io/badge/Ko--fi-Support%20Us-ff5e5b?logo=ko-fi&logoColor=white)](https://ko-fi.com/ericelbaz)


	\| Option \| Description \|
	\|--------\|-------------\|
	\| [PRISM VIP Membership](https://ko-fi.com/summary/6bae206c-a751-4868-8dc7-f531afd1fb4c) \| Access to all PRISM models \|
	\| [One-Time Support](https://ko-fi.com/ericelbaz) \| Every contribution directly funds my time & resources for the next major SOTA release. \|

	</div>

	### 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

	---

	<div align="center">

	"Freedom of information isn't free — but together, we can make it accessible to all."

	Thank you for believing in true Open AI.

	</div>

	---

	## 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)

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

	---

	## Recommended Inference Parameters

	```python
	temperature = 1.0
	top_p = 0.95
	top_k = 40
	```

	### Default System Prompt
	```
	You are a helpful assistant.
	```

	---

	## 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

	```bash
	./llama-cli -m MiniMax-M2.1-PRISM-IQ1_S.gguf -ngl 99 --temp 1.0 --ctx-size 4096
	```

	---

	## 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.

	---

	## License

	This model inherits the [Modified-MIT License](https://github.com/MiniMax-AI/MiniMax-M2.1/blob/main/LICENSE) from the base MiniMax-M2.1 model.

	---

	## Credits

	- Base Model: [MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1) by MiniMax AI
	- PRISM Abliteration: Ex0bit
	- Quantization: Using [llama.cpp](https://github.com/ggml-org/llama.cpp) with unsloth imatrix

	---

	## Support

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

	[![Support me on Ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/ericelbaz#tier17681523526070)

	---

	## Contact

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