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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Ex0bit/MiniMax-M2.1-PRISM",
	filename="MiniMax-M2.1-PRISM-IQ2_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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-server -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

Hermes Agent new

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-server -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

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

Ex0bit commited on Jan 1

Commit

46dc900

verified ·

1 Parent(s): 404884c

Update README.md

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README.md +13 -18

README.md CHANGED Viewed

@@ -15,15 +15,18 @@ pipeline_tag: text-generation
 # MiniMax-M2.1-PRISM (UNCENSORED)
-** MiniMax-M2.1 Uncensored via PRISM Advanced Abliteration**
-[![Support me on Ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/ericelbaz)
 ---
 ## Model Description
-**MiniMax-M2.1-PRISM** is the abliterated version of MiniMax-M2.1, processed using PRISM (Projected Refusal Isolation via Subspace Modification) to remove refusal behaviors while preserving and even enhancing full model capabilities.
 ### Base Model: MiniMax-M2.1
@@ -42,15 +45,7 @@ MiniMax-M2.1 is an open-source agentic language model designed for robust perfor
 ### Method: Projected Refusal Isolation via Subspace Modification
-This model was abliterated using **PRISM v5** - a state-of-the-art abliteration methodology combining multiple principled techniques for effective refusal removal while preserving model capabilities.
-### Weight Distribution
-The abliteration strength follows a triangular distribution centered on the peak layer:
-- Layers 16-31: Weight increases from 0.5 to 3.0
-- Layers 31-46: Weight decreases from 3.0 to 0.5
 ---
@@ -71,15 +66,15 @@ The abliteration strength follows a triangular distribution centered on the peak
 | Metric | Result |
 |--------|--------|
-| Adversarial Prompts Responded | 20/20 (100%) |
-| Benign Coherence | 100% |
-| Response Quality | Full technical accuracy preserved |
-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)
 | Format | Size | Description |
 |--------|------|-------------|

 # MiniMax-M2.1-PRISM (UNCENSORED)
+** MiniMax-M2.1 Uncensored PRISM Advanced Abliteration**
+<div align="center">
+  <a href="https://ko-fi.com/ericelbaz">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/63adf1fa42fd3b8dbaeb0c92/Qe17Xd59xWbucl1ZOl1jF.png" width="50%"/>
+  </a>
+</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
 ### 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 model capabilities.
 ---
 | 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 |
 |--------|------|-------------|