---
license: llama2
---

<div align="center">
<h1>
  AIMI FMs: A Collection of Foundation Models in Radiology
</h1>
</div>

<p align="center">
📝 <a href="https://arxiv.org/" target="_blank">Paper</a> • 🤗 <a href="https://huggingface.co/StanfordAIMI/RadLLaMA-7b" target="_blank">Hugging Face</a> • 🧩 <a href="https://github.com/Stanford-AIMI/aimi-fms" target="_blank">Github</a> • 🪄 <a href="https://github.com/Stanford-AIMI/aimi-fms" target="_blank">Project</a>
</p>
<div align="center">
</div>

## ✨ Latest News

- [01/20/2023]: Model released in [Hugging Face](https://huggingface.co/StanfordAIMI/RadLLaMA-7b).

## 🎬 Get Started

```python
from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("StanfordAIMI/RadLLaMA-7b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("StanfordAIMI/RadLLaMA-7b")

prompt = "Hi"
conv = [{"from": "human", "value": prompt}]
input_ids = tokenizer.apply_chat_template(conv, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids)
response = tokenizer.decode(outputs[0])
print(response)
```

## ✏️ Citation

```
@article{aimifms-2024,
  title={},
  author={},
  journal={arXiv preprint arXiv:xxxx.xxxxx},
  url={https://arxiv.org/abs/xxxx.xxxxx},
  year={2024}
}
```