Instructions to use mlx-community/II-Medical-8B-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/II-Medical-8B-bf16 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("mlx-community/II-Medical-8B-bf16") 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
- MLX LM
How to use mlx-community/II-Medical-8B-bf16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/II-Medical-8B-bf16"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/II-Medical-8B-bf16" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/II-Medical-8B-bf16", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 851 Bytes
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library_name: mlx
tags:
- mlx
base_model: Intelligent-Internet/II-Medical-8B
pipeline_tag: text-generation
---
# mlx-community/II-Medical-8B-bf16
This model [mlx-community/II-Medical-8B-bf16](https://huggingface.co/mlx-community/II-Medical-8B-bf16) was
converted to MLX format from [Intelligent-Internet/II-Medical-8B](https://huggingface.co/Intelligent-Internet/II-Medical-8B)
using mlx-lm version **0.25.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/II-Medical-8B-bf16")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
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