Text Generation
MLX
Safetensors
English
minimax_m2
quantized
mixed-precision
minimax
Mixture of Experts
conversational
custom_code
4-bit precision
Instructions to use baa-ai/MiniMax-M2.7-RAM-155GB-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use baa-ai/MiniMax-M2.7-RAM-155GB-MLX with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("baa-ai/MiniMax-M2.7-RAM-155GB-MLX") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use baa-ai/MiniMax-M2.7-RAM-155GB-MLX with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "baa-ai/MiniMax-M2.7-RAM-155GB-MLX"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "baa-ai/MiniMax-M2.7-RAM-155GB-MLX" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use baa-ai/MiniMax-M2.7-RAM-155GB-MLX with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "baa-ai/MiniMax-M2.7-RAM-155GB-MLX"
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 baa-ai/MiniMax-M2.7-RAM-155GB-MLX
Run Hermes
hermes
- MLX LM
How to use baa-ai/MiniMax-M2.7-RAM-155GB-MLX with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "baa-ai/MiniMax-M2.7-RAM-155GB-MLX"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "baa-ai/MiniMax-M2.7-RAM-155GB-MLX" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "baa-ai/MiniMax-M2.7-RAM-155GB-MLX", "messages": [ {"role": "user", "content": "Hello"} ] }'
MiniMax-M2.7 — 155 GB (MLX)
Earlier build of the MiniMax-M2.7 mixed-precision MLX family, by baa.ai.
Current builds
For updated builds with HumanEval results and recommended inference settings, see:
| Variant | Size | Link |
|---|---|---|
| 100 GB | 100.1 GB | baa-ai/MiniMax-M2.7-RAM-100GB-MLX |
| 111 GB | 110.9 GB | baa-ai/MiniMax-M2.7-RAM-111GB-MLX |
| 116 GB | 116.0 GB | baa-ai/MiniMax-M2.7-RAM-116GB-MLX |
| 120 GB | 120.1 GB | baa-ai/MiniMax-M2.7-RAM-120GB-MLX |
Usage
from mlx_lm import load, generate
model, tokenizer = load("baa-ai/MiniMax-M2.7-RAM-155GB-MLX")
response = generate(model, tokenizer, prompt="Hello!", max_tokens=512)
print(response)
License
Inherited from the upstream MiniMax-M2.7 license: non-commercial use permitted; commercial use requires written authorization from MiniMax.
Quantized by baa.ai
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Model size
229B params
Tensor type
BF16
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U32 ·
F32 ·
Hardware compatibility
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4-bit
Model tree for baa-ai/MiniMax-M2.7-RAM-155GB-MLX
Base model
MiniMaxAI/MiniMax-M2.7