Text Generation
MLX
Safetensors
English
maple
mlx-swift
mixture-of-experts
quantized
openmed
conversational
custom_code
8-bit precision
Instructions to use OpenMed/maple-preview-8bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenMed/maple-preview-8bit-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-8bit-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-8bit-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-8bit-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-8bit-mlx" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use OpenMed/maple-preview-8bit-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-8bit-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-8bit-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"
- MLX LM
How to use OpenMed/maple-preview-8bit-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-8bit-mlx"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "OpenMed/maple-preview-8bit-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-8bit-mlx", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use OpenMed/maple-preview-8bit-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-8bit-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-8bit-mlx
Run Hermes
hermes
| base_model: deepgrove/maple-preview | |
| base_model_relation: quantized | |
| language: | |
| - en | |
| library_name: mlx | |
| license: mit | |
| pipeline_tag: text-generation | |
| tags: | |
| - mlx | |
| - mlx-swift | |
| - maple | |
| - mixture-of-experts | |
| - quantized | |
| - openmed | |
| # Maple Preview 8-bit MLX | |
| Private OpenMed development export of | |
| [`deepgrove/maple-preview`](https://huggingface.co/deepgrove/maple-preview) for | |
| MLX. It was converted from source revision | |
| `ac1ddd79d2b5cb4406f5d2bebdf95406ce505a07` with 8-bit affine quantization and | |
| group size 128. The included `openmed-maple-export.json` records the pinned | |
| source and runtime-code revisions and conversion settings. | |
| ## Validation status | |
| On Apple Silicon, this export loaded and completed OpenMed's synthetic task | |
| contracts for PII span proposals, clinical entities, relations, concise | |
| reasoning, and grounded visible chat. These are development contract checks, | |
| not clinical-quality evidence. Direct-identifier recall, critical-leakage, | |
| source-parity, memory, thermal, and physical-device release gates remain. | |
| ## OpenMed use | |
| The OpenMedKit integration, prompt contracts, and native iOS scanning demo are | |
| documented in | |
| [`docs/maple-on-device.md`](https://github.com/maziyarpanahi/openmed/blob/master/docs/maple-on-device.md). | |
| Structured outputs must pass OpenMed's span, label-vocabulary, relation, and | |
| grounding validators before they are shown or applied. Do not log prompts that | |
| may contain protected information. | |
| Maple Preview is a research model with limited post-training. It is not a | |
| medical device and must not automatically trigger diagnosis, treatment, | |
| disclosure, or another consequential decision. Human review is required. | |
| ## Attribution and license | |
| This is a quantized derivative of DeepGrove's Maple Preview. The upstream model | |
| is licensed under MIT; see the upstream repository for its license and model | |
| card. OpenMed's SDK source is separately licensed under Apache-2.0. | |