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license: llama2
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---
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---
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license: llama2
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---
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<div align="center">
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<h1>
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CheXagent
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</h1>
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</div>
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<p align="center">
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📝 <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://huggingface.co/StanfordAIMI/RadLLaMA-7b" target="_blank">Hugging Face</a> • 🧩 <a href="https://github.com/Stanford-AIMI/aimi-fms" target="_blank">Project</a>
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</p>
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<div align="center">
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</div>
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## ✨ Latest News
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- [01/20/2023]: Model released in [Hugging Face](https://huggingface.co/StanfordAIMI/RadLLaMA-7b).
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## 🎬 Get Started
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```python
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from transformers import AutoTokenizer
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from transformers import AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("StanfordAIMI/RadLLaMA-7b", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("StanfordAIMI/RadLLaMA-7b")
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prompt = "Hi"
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conv = [{"from": "human", "value": prompt}]
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input_ids = tokenizer.apply_chat_template(conv, add_generation_prompt=True, return_tensors="pt")
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outputs = model.generate(input_ids)
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response = tokenizer.decode(outputs[0])
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print(response)
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```
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## ✏️ Citation
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```
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@article{chexagent-2024,
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title={},
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author={},
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journal={arXiv preprint arXiv:xxxx.xxxxx},
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url={https://arxiv.org/abs/xxxx.xxxxx},
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year={2024}
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}
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```
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