Instructions to use OsaurusAI/LFM2.5-230M-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OsaurusAI/LFM2.5-230M-MXFP8 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/LFM2.5-230M-MXFP8") 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/LFM2.5-230M-MXFP8 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/LFM2.5-230M-MXFP8"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/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/LFM2.5-230M-MXFP8" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use OsaurusAI/LFM2.5-230M-MXFP8 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/LFM2.5-230M-MXFP8"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OsaurusAI/LFM2.5-230M-MXFP8" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OsaurusAI/LFM2.5-230M-MXFP8", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use OsaurusAI/LFM2.5-230M-MXFP8 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/LFM2.5-230M-MXFP8"
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/LFM2.5-230M-MXFP8
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use OsaurusAI/LFM2.5-230M-MXFP8 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OsaurusAI/LFM2.5-230M-MXFP8"
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 "OsaurusAI/LFM2.5-230M-MXFP8" \ --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"
File size: 1,069 Bytes
7585cec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | {
"version": 2,
"weight_format": "mxfp8",
"profile": "MXFP8",
"source_model": {
"name": "LFM2.5-230M",
"architecture": "lfm2"
},
"has_vision": true,
"quantization": {
"method": "mxfp8",
"quantization_backend": "mx.quantize",
"mode": "mxfp8",
"group_size": 32,
"bits": 8,
"norm_convention": "qwen3_5_language_mlx_plus_one",
"vision": "fp16_passthrough",
"mtp_policy": "native_mxfp8_linears_fp16_norms",
"passthrough_bit_widths_used": [
16
],
"passthrough_tensor_count": 49
},
"runtime": {
"total_weight_bytes": 236948016,
"total_weight_gb": 0.22
},
"capabilities": {
"reasoning_parser": "qwen3",
"tool_parser": "lfm2",
"think_in_template": false,
"supports_tools": true,
"supports_thinking": true,
"family": "lfm2",
"modality": "text",
"modalities": {
"text": true,
"vision": false,
"audio": false,
"video": false
},
"has_vision": false,
"has_audio": false,
"has_video": false,
"cache_type": "hybrid"
}
} |