---
license: other
license_name: health-ai-developer-foundations
license_link: https://developers.google.com/health-ai-developer-foundations/terms
library_name: transformers
pipeline_tag: image-text-to-text
language: en
extra_gated_heading: Access MedGemma on Hugging Face
extra_gated_prompt: To access MedGemma on Hugging Face, you're required to review
  and agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
  To do this, please ensure you're logged in to Hugging Face and click below. Requests
  are processed immediately.
extra_gated_button_content: Acknowledge license
tags:
- medical
- x-ray
- pathology
- dermatology
- fundus
- radiology report generation
- chest-x-ray
- medical-embeddings
- image-classification
- zero-shot-image-classification
- image-feature-extraction
- image-text-to-text
- heretic
- uncensored
- decensored
- abliterated
- mlx
- mlx-my-repo
base_model: genevera/medgemma-27b-it-heretic
---

# Wwayu/medgemma-27b-it-heretic-mlx-6Bit

The Model [Wwayu/medgemma-27b-it-heretic-mlx-6Bit](https://huggingface.co/Wwayu/medgemma-27b-it-heretic-mlx-6Bit) was converted to MLX format from [genevera/medgemma-27b-it-heretic](https://huggingface.co/genevera/medgemma-27b-it-heretic) using mlx-lm version **0.31.2**.

## Use with mlx

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

```python
from mlx_lm import load, generate

model, tokenizer = load("Wwayu/medgemma-27b-it-heretic-mlx-6Bit")

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)
```