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
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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.
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