| --- |
| license: apache-2.0 |
| datasets: |
| - eltorio/ROCO-radiology |
| language: |
| - en |
| base_model: |
| - HuggingFaceM4/Idefics3-8B-Llama3 |
| pipeline_tag: image-text-to-text |
| library_name: peft |
| --- |
| |
| # IDEFICS3_ROCO |
| |
| [](https://colab.research.google.com/#fileId=https://huggingface.co/eltorio/IDEFICS3_ROCO/blob/main/ROCO-idefics3.ipynb) |
| |
| ## Star the project |
| |
| **If you appreciate my work, please consider giving it a like! 🤩** |
| **I'm also looking for donations of free GPU time to complete the fine-tuning process.** |
| **Please contact me if you can help! 🙏** |
| |
| ## A Fine-tuned Radiology-focused Model based on Hugging Face's Idefics3 Model |
| |
| This repository contains a fine-tuned version of the Hugging Face [Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) model, built on top of the Meta Llama 3.1 8B architecture. Our model, `IDEFICS3_ROCO`, has been fine-tuned on the [Radiology Objects in Context (ROCO)](https://huggingface.co/datasets/eltorio/ROCO-radiology) dataset, a large-scale medical and multimodal imaging collection. |
|
|
| ## TL;DR |
|
|
| For immediate use, you can load the model directly from Hugging Face: |
|
|
| ```python |
| from transformers import AutoProcessor, Idefics3ForConditionalGeneration, image_utils |
| import torch |
| device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') # on CPU it requires ≈ 3h/query 🙈 |
| processor = AutoProcessor.from_pretrained(v) |
| model = Idefics3ForConditionalGeneration.from_pretrained( |
| v, torch_dtype=torch.bfloat16 |
| ).to(device) |
| |
| model.load_adapter("eltorio/IDEFICS3_ROCO") |
| ``` |
|
|
| ### Model Information |
|
|
| * **Base Model:** Idefics3-8B-Llama3 |
| * **Fine-tuning Dataset:** Radiology Objects in Context (ROCO) |
| * **License:** Apache-2.0 |
| * **Current Status:** Fine-tuning process is finished. Contributions to complete the fine-tuning / vallidation / test processes are welcome! |
|
|
| ### Training Progress Status |
|
|
| * Current checkpoint: 12267 (100% completed) |
| * Estimated remaining GPU time: 0 hours |
| * Hardware requirements: T4 GPU with >16GB VRAM |
| * Last update: november, 12th 2024 |
|
|
| ### Fine-tuning Code |
|
|
| The fine-tuning code is available as a Jupyter Notebook in the [ROCO-radiology dataset repository](https://huggingface.co/datasets/eltorio/ROCO-radiology) on Hugging Face: |
|
|
| * [ROCO-idefics3.ipynb](https://huggingface.co/eltorio/IDEFICS3_ROCO/blob/main/ROCO-idefics3.ipynb) |
|
|
| The [Junyper Notebook](https://colab.research.google.com/#fileId=https%3A//huggingface.co/eltorio/IDEFICS3_ROCO/blob/main/ROCO-idefics3.ipynb) [](https://colab.research.google.com/#fileId=https://huggingface.co/eltorio/IDEFICS3_ROCO/blob/main/ROCO-idefics3.ipynb) contains the code to fine-tune the Idefics3-8B-Llama3 model on the ROCO dataset. The fine-tuning process is currently halted at checkpoint 640 (out of 24,000) due to limitations with Colab Free T4 GPU unit. Contributions to complete the fine-tuning process are welcome! |
|
|
| ### Contributions Welcome |
|
|
| If you have the resources to complete the fine-tuning process, we would appreciate your contribution. Please fork this repository, finish the fine-tuning process, and submit a pull request with your updates. |
|
|
| ### Citation |
|
|
| If you use this model in your work, please cite the original Idefics3 model and our fine-tuned model: |
|
|
| * [Idefics3-8B-Llama3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) |
| * [IDEFICS3_ROCO](https://huggingface.co/eltorio/IDEFICS3_ROCO) |
|
|
| ### Contribution Guide |
|
|
| 1. **Technical Requirements** |
| * Access to powerful GPU (T4, V100, A100 or equivalent) |
| * Python environment with PyTorch |
| * Disk space: ~100GB |
|
|
| 2. **Getting Started** |
| * Fork the repository |
| * Resume from checkpoint 12267 |
| * Follow instructions in [ROCO-idefics3.ipynb](https://huggingface.co/eltorio/IDEFICS3_ROCO/blob/main/ROCO-idefics3.ipynb) [](https://colab.research.google.com/#fileId=https://huggingface.co/eltorio/IDEFICS3_ROCO/blob/main/ROCO-idefics3.ipynb) |
|
|
| 3. **Contact** |
| * For questions: [link to issues/discussions](https://huggingface.co/eltorio/IDEFICS3_ROCO/discussions) |
|
|
| ### Docker Image |
|
|
| A AI training docker image is available for this model. The image and includes all necessary dependencies to run the fine-tuning process. |
| You need to set the `HF_TOKEN` environment variable to your Hugging Face API token. |
| You also need to have NVidia Docker container runtime installed. |
| Finnaly, you need to run the container with GPU support with `--gpus all` option. |
| The image is available on Docker Hub: |
|
|
| ```bash |
| export HF_TOKEN=hf_some_token |
| docker run --gpus all --user=42420:42420 -e HF_TOKEN=$HF_TOKEN -it sctg/roco-idefics3:latest bash -i /start.sh $HF_TOKEN |
| ``` |
|
|
| The Dockerfile is available in the [IDEFICS_ROCO repository](https://huggingface.co/eltorio/IDEFICS3_ROCO/blob/main/Dockerfile). |
|
|
| ### Use this model |
|
|
| According to the Apache license you should cite this model with: |
|
|
| ```bibtex |
| @misc {ronan_l.m._2024, |
| author = { {Ronan L.M.} }, |
| title = { IDEFICS3_ROCO (Revision b02598a) }, |
| year = 2024, |
| url = { https://huggingface.co/eltorio/IDEFICS3_ROCO }, |
| doi = { 10.57967/hf/3504 }, |
| publisher = { Hugging Face } |
| } |
| ``` |
|
|
| ### Acknowledgments |
|
|
| This work was made possible by the [Hugging Face Transformers](https://huggingface.co/) library and the [ROCO-radiology dataset](https://huggingface.co/datasets/eltorio/ROCO-radiology). |
