---
base_model: ContactDoctor/Bio-Medical-3B-CoT-012025
datasets:
- collaiborateorg/BioMedData
library_name: transformers
license: other
tags:
- generated_from_trainer
- medical
- Healthcare & Lifesciences
- BioMed
- chain-of-thought
- mlx
thumbnail: https://collaiborate.com/logo/logo-blue-bg-1.png
model-index:
- name: Bio-Medical-3B-CoT-012025
  results: []
---

# mlx-community/Bio-Medical-3B-CoT-012025

The Model [mlx-community/Bio-Medical-3B-CoT-012025](https://huggingface.co/mlx-community/Bio-Medical-3B-CoT-012025) was
converted to MLX format from [ContactDoctor/Bio-Medical-3B-CoT-012025](https://huggingface.co/ContactDoctor/Bio-Medical-3B-CoT-012025)
using mlx-lm version **0.20.1**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Bio-Medical-3B-CoT-012025")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```