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--- |
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base_model: google/medgemma-4b-it |
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library_name: transformers |
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model_name: medgemma-4b-it-sft-lora-stroke |
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tags: |
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- generated_from_trainer |
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- trl |
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- sft |
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licence: license |
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--- |
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# Model Card for medgemma-4b-it-sft-lora-stroke |
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This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it). |
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It has been trained using [TRL](https://github.com/huggingface/trl). |
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## Quick start |
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```python |
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from transformers import pipeline |
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from PIL import Image |
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import requests |
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import torch |
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pipe = pipeline( |
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"image-text-to-text", |
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model="BTX24/medgemma-4b-it-sft-lora-stroke", |
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torch_dtype=torch.bfloat16, |
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device="cuda", |
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) |
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# Image attribution: Stillwaterising, CC0, via Wikimedia Commons |
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image_url = "https://storage.googleapis.com/kagglesdsdata/datasets/6652053/10729545/%C4%B0NME%20VER%C4%B0%20SET%C4%B0/Kanama/PNG/10033.png?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20250605%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20250605T135753Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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" |
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image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw) |
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messages = [ |
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{ |
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"role": "system", |
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"content": [{"type": "text", "text": "You are an expert radiologist."}] |
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}, |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "text", "text": "Describe this X-ray"} |
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{"type": "image", "image": image}, |
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] |
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} |
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] |
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output = pipe(text=messages, max_new_tokens=200) |
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print(output[0]["generated_text"][-1]["content"]) |
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``` |
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## Training procedure |
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This model was trained with SFT. |
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### Framework versions |
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- TRL: 0.18.1 |
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- Transformers: 4.52.4 |
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- Pytorch: 2.6.0+cu124 |
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- Datasets: 3.6.0 |
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- Tokenizers: 0.21.1 |
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## Citations |
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Cite TRL as: |
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```bibtex |
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@misc{vonwerra2022trl, |
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title = {{TRL: Transformer Reinforcement Learning}}, |
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, |
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year = 2020, |
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journal = {GitHub repository}, |
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publisher = {GitHub}, |
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howpublished = {\url{https://github.com/huggingface/trl}} |
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} |
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``` |
