Text Generation
MLX
Safetensors
minimax_m2
osaurus
jangtq
jangtq-prestack
jangtq-k
mixed-precision
minimax
minimax-m2
Mixture of Experts
apple-silicon
conversational
reasoning
chain-of-thought
quantization
230b
custom_code
Instructions to use OsaurusAI/MiniMax-M2.7-JANGTQ_K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/MiniMax-M2.7-JANGTQ_K 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("OsaurusAI/MiniMax-M2.7-JANGTQ_K") 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 Settings
- LM Studio
- Pi
How to use OsaurusAI/MiniMax-M2.7-JANGTQ_K with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OsaurusAI/MiniMax-M2.7-JANGTQ_K"
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": "OsaurusAI/MiniMax-M2.7-JANGTQ_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use OsaurusAI/MiniMax-M2.7-JANGTQ_K 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 "OsaurusAI/MiniMax-M2.7-JANGTQ_K"
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 OsaurusAI/MiniMax-M2.7-JANGTQ_K
Run Hermes
hermes
- MLX LM
How to use OsaurusAI/MiniMax-M2.7-JANGTQ_K with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "OsaurusAI/MiniMax-M2.7-JANGTQ_K"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OsaurusAI/MiniMax-M2.7-JANGTQ_K" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OsaurusAI/MiniMax-M2.7-JANGTQ_K", "messages": [ {"role": "user", "content": "Hello"} ] }'
Upload jang_config.json with huggingface_hub
Browse files- jang_config.json +39 -0
jang_config.json
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{
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"version": 2,
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"weight_format": "mxtq",
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"profile": "JANGTQ_K",
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"source_model": {
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"name": "MiniMax-M2.7",
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"org": "MiniMaxAI",
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"architecture": "minimax_m2"
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},
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"mxtq_seed": 42,
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"mxtq_bits": {
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"routed_expert": {
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"gate_proj": 2,
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"down_proj": 4,
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"up_proj": 2
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},
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"attention": 8,
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"shared_expert": 8,
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"embed_tokens": 8,
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"lm_head": 8,
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"norms_router_biases": 16
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},
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"quantization": {
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"method": "affine+mxtq",
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"group_size": 64,
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"bits_default": 4
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},
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"capabilities": {
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"reasoning_parser": "qwen3",
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"tool_parser": "minimax",
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"think_in_template": true,
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"supports_tools": true,
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"supports_thinking": true,
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"family": "minimax_m2",
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"modality": "text",
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"cache_type": "kv"
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},
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"routed_expert_layout": "prestacked"
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}
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