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
- LM Studio
- Pi new
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"} ] }'
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| "architectures": [ | |
| "MiniMaxM2ForCausalLM" | |
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| "auto_map": { | |
| "AutoConfig": "configuration_minimax_m2.MiniMaxM2Config", | |
| "AutoModelForCausalLM": "modeling_minimax_m2.MiniMaxM2ForCausalLM" | |
| }, | |
| "dtype": "bfloat16", | |
| "head_dim": 128, | |
| "hidden_act": "silu", | |
| "hidden_size": 3072, | |
| "intermediate_size": 1536, | |
| "max_position_embeddings": 196608, | |
| "model_type": "minimax_m2", | |
| "mtp_transformer_layers": 0, | |
| "num_attention_heads": 48, | |
| "num_experts_per_tok": 8, | |
| "num_hidden_layers": 62, | |
| "num_key_value_heads": 8, | |
| "num_local_experts": 256, | |
| "num_mtp_modules": 0, | |
| "qk_norm_type": "per_layer", | |
| "rms_norm_eps": 1e-06, | |
| "rope_theta": 5000000, | |
| "rotary_dim": 64, | |
| "scoring_func": "sigmoid", | |
| "shared_intermediate_size": 0, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "4.46.1", | |
| "use_cache": true, | |
| "use_mtp": false, | |
| "use_qk_norm": true, | |
| "use_routing_bias": true, | |
| "vocab_size": 200064, | |
| "_name_or_path": "M2.5-SLURPY", | |
| "quantization": { | |
| "bits": 8, | |
| "mode": "affine", | |
| "group_size": 64, | |
| "routed_expert_bits": { | |
| "gate_proj": 2, | |
| "down_proj": 4, | |
| "up_proj": 2 | |
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| "mxtq_bits": { | |
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| "gate_proj": 2, | |
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| "attention": 8, | |
| "shared_expert": 8, | |
| "embed_tokens": 8, | |
| "lm_head": 8, | |
| "norms_router_biases": 16 | |
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| "weight_format": "mxtq" | |
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