Instructions to use Shashwath01/Idefic_medical_VQA_merged_4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shashwath01/Idefic_medical_VQA_merged_4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Shashwath01/Idefic_medical_VQA_merged_4bit")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("Shashwath01/Idefic_medical_VQA_merged_4bit") model = AutoModelForImageTextToText.from_pretrained("Shashwath01/Idefic_medical_VQA_merged_4bit") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Shashwath01/Idefic_medical_VQA_merged_4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Shashwath01/Idefic_medical_VQA_merged_4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Shashwath01/Idefic_medical_VQA_merged_4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Shashwath01/Idefic_medical_VQA_merged_4bit
- SGLang
How to use Shashwath01/Idefic_medical_VQA_merged_4bit with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Shashwath01/Idefic_medical_VQA_merged_4bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Shashwath01/Idefic_medical_VQA_merged_4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Shashwath01/Idefic_medical_VQA_merged_4bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Shashwath01/Idefic_medical_VQA_merged_4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Shashwath01/Idefic_medical_VQA_merged_4bit with Docker Model Runner:
docker model run hf.co/Shashwath01/Idefic_medical_VQA_merged_4bit
Contributed by:
- Shashwath P
- Shashank Ashok
- Akilan Yohendiran
Total downloads all time - 2106
Model Card for Model ID
The following model is an experimental fine tuned model of the IDEFIC 9B version, for medical Visual Question Answering. It uses a dataset combined from SLAKE and VQARAD. Check the following repository for the notebooks of training,merging and inference. https://github.com/Shashwathp/Idefic_medical_vqa
Model Description
This is the model card of a 馃 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: [@Shashwath01,@Akill19,@Shashank91097 ]
- Model type: [Multimodal, Visual Question Answering]
- Language(s) (NLP): [English]
- License: [Apache - 2.0]
- Finetuned from model [optional]: [IDEFIC 9B]
Dataset
https://huggingface.co/datasets/Shashwath01/VQARAD_SLAKE
Model Sources
- Repository: https://github.com/Shashwathp/Idefic_medical_vqa
How to Get Started with the Model
Check the below link to get started with inferencing. https://github.com/Shashwathp/Idefic_medical_vqa/blob/main/inference.ipynb
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