Instructions to use inferencerlabs/MiMo-V2.5-LM-MLX-Q9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use inferencerlabs/MiMo-V2.5-LM-MLX-Q9 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("inferencerlabs/MiMo-V2.5-LM-MLX-Q9") 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 inferencerlabs/MiMo-V2.5-LM-MLX-Q9 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "inferencerlabs/MiMo-V2.5-LM-MLX-Q9"
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": "inferencerlabs/MiMo-V2.5-LM-MLX-Q9" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use inferencerlabs/MiMo-V2.5-LM-MLX-Q9 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 "inferencerlabs/MiMo-V2.5-LM-MLX-Q9"
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 inferencerlabs/MiMo-V2.5-LM-MLX-Q9
Run Hermes
hermes
- OpenClaw new
How to use inferencerlabs/MiMo-V2.5-LM-MLX-Q9 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "inferencerlabs/MiMo-V2.5-LM-MLX-Q9"
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 "inferencerlabs/MiMo-V2.5-LM-MLX-Q9" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use inferencerlabs/MiMo-V2.5-LM-MLX-Q9 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "inferencerlabs/MiMo-V2.5-LM-MLX-Q9"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "inferencerlabs/MiMo-V2.5-LM-MLX-Q9" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inferencerlabs/MiMo-V2.5-LM-MLX-Q9", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
| "add_full_attention_sink_bias": false, | |
| "add_swa_attention_sink_bias": true, | |
| "architectures": [ | |
| "MiMoV2ForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_chunk_size": 128, | |
| "attention_dropout": 0.0, | |
| "attention_projection_layout": "fused_qkv", | |
| "attention_value_scale": 0.707, | |
| "auto_map": { | |
| "AutoConfig": "configuration_mimo_v2.MiMoV2Config", | |
| "AutoModel": "modeling_mimo_v2.MiMoV2Model", | |
| "AutoModelForCausalLM": "modeling_mimo_v2.MiMoV2ForCausalLM" | |
| }, | |
| "dtype": "bfloat16", | |
| "eos_token_id": [ | |
| 151643, | |
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| 1561672 | |
| ], | |
| "head_dim": 192, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "hybrid_block_size": null, | |
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| "intermediate_size": 16384, | |
| "layernorm_epsilon": 1e-05, | |
| "max_position_embeddings": 262144, | |
| "mlx-sanitized": "0.30.7", | |
| "model_type": "mimo_v2", | |
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| "n_group": 1, | |
| "n_routed_experts": 256, | |
| "n_shared_experts": null, | |
| "norm_topk_prob": true, | |
| "num_attention_heads": 64, | |
| "num_experts_per_tok": 8, | |
| "num_hidden_layers": 48, | |
| "num_key_value_heads": 4, | |
| "pad_token_id": 151643, | |
| "partial_rotary_factor": 0.334, | |
| "quantization_config": { | |
| "group_size": 32 | |
| }, | |
| "rope_scaling": { | |
| "rope_type": "default", | |
| "type": "default" | |
| }, | |
| "rope_theta": 5000000, | |
| "routed_scaling_factor": null, | |
| "scoring_func": "sigmoid", | |
| "sliding_window": 128, | |
| "sliding_window_size": 128, | |
| "swa_head_dim": 192, | |
| "swa_num_attention_heads": 64, | |
| "swa_num_key_value_heads": 8, | |
| "swa_rope_theta": 10000, | |
| "swa_v_head_dim": 128, | |
| "tie_word_embeddings": false, | |
| "topk_group": 1, | |
| "topk_method": "noaux_tc", | |
| "transformers_version": "4.57.1", | |
| "use_cache": true, | |
| "v_head_dim": 128, | |
| "video_token_id": 151656, | |
| "vision_end_token_id": 151653, | |
| "vision_model_type": "mimovl", | |
| "vision_start_token_id": 151652, | |
| "vocab_size": 152576 | |
| } |