Text Generation
MLX
Safetensors
English
maple
mixture-of-experts
quantized
experimental
openmed
conversational
custom_code
4-bit precision
Instructions to use OpenMed/maple-preview-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenMed/maple-preview-4bit-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("OpenMed/maple-preview-4bit-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 Settings
- LM Studio
- Pi
How to use OpenMed/maple-preview-4bit-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 "OpenMed/maple-preview-4bit-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": "OpenMed/maple-preview-4bit-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use OpenMed/maple-preview-4bit-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 "OpenMed/maple-preview-4bit-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OpenMed/maple-preview-4bit-mlx" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenMed/maple-preview-4bit-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use OpenMed/maple-preview-4bit-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 "OpenMed/maple-preview-4bit-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 OpenMed/maple-preview-4bit-mlx
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use OpenMed/maple-preview-4bit-mlx with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "OpenMed/maple-preview-4bit-mlx"
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 "OpenMed/maple-preview-4bit-mlx" \ --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"
Add OpenMed validation model card
Browse files
README.md
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---
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base_model: deepgrove/maple-preview
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base_model_relation: quantized
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language:
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- en
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library_name: mlx
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license: mit
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pipeline_tag: text-generation
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tags:
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- mlx
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- maple
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- mixture-of-experts
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- quantized
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- experimental
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- openmed
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---
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# Maple Preview 4-bit MLX — experimental archive
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> **Do not deploy this export.** It is retained privately as a reproducibility
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> artifact for a failed development validation result.
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This OpenMed export was converted from
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[`deepgrove/maple-preview`](https://huggingface.co/deepgrove/maple-preview)
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revision `ac1ddd79d2b5cb4406f5d2bebdf95406ce505a07` with 4-bit affine
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quantization and group size 128. The included `openmed-maple-export.json`
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records the pinned source and runtime-code revisions and conversion settings.
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The checkpoint converted and loaded on Apple Silicon, but its synthetic
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structured-task run exhausted the 4,096-token generation budget before
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producing final JSON. Successful tensor conversion is not evidence of useful
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or safe generation. No clinical-quality, direct-identifier recall,
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critical-leakage, source-parity, or physical-device claim is made.
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See the validation table and reproducible export instructions in
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[`docs/maple-on-device.md`](https://github.com/maziyarpanahi/openmed/blob/master/docs/maple-on-device.md).
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Maple Preview is a research model, not a medical device. The upstream model is
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licensed under MIT; see the upstream repository for its license and model card.
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