---
base_model: HPAI-BSC/Qwen2.5-Aloe-Beta-7B
datasets:
- HPAI-BSC/Aloe-Beta-General-Collection
- HPAI-BSC/chain-of-diagnosis
- HPAI-BSC/MedS-Ins
- HPAI-BSC/ultramedical
- HPAI-BSC/pubmedqa-cot-llama31
- HPAI-BSC/medqa-cot-llama31
- HPAI-BSC/medmcqa-cot-llama31
- HPAI-BSC/headqa-cot-llama31
- HPAI-BSC/MMLU-medical-cot-llama31
- HPAI-BSC/Polymed-QA
- HPAI-BSC/Aloe-Beta-General-Collection
- HPAI-BSC/Aloe-Beta-General-Collection
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: question-answering
tags:
- biology
- medical
- healthcare
- mlx
---

# mlx-community/Qwen2.5-Aloe-Beta-7B

The Model [mlx-community/Qwen2.5-Aloe-Beta-7B](https://huggingface.co/mlx-community/Qwen2.5-Aloe-Beta-7B) was
converted to MLX format from [HPAI-BSC/Qwen2.5-Aloe-Beta-7B](https://huggingface.co/HPAI-BSC/Qwen2.5-Aloe-Beta-7B)
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/Qwen2.5-Aloe-Beta-7B")

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)
```