Instructions to use KhimNguyen/chart2text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KhimNguyen/chart2text with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="KhimNguyen/chart2text")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("KhimNguyen/chart2text") model = AutoModelForMultimodalLM.from_pretrained("KhimNguyen/chart2text") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use KhimNguyen/chart2text with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KhimNguyen/chart2text" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KhimNguyen/chart2text", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KhimNguyen/chart2text
- SGLang
How to use KhimNguyen/chart2text 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 "KhimNguyen/chart2text" \ --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": "KhimNguyen/chart2text", "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 "KhimNguyen/chart2text" \ --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": "KhimNguyen/chart2text", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KhimNguyen/chart2text with Docker Model Runner:
docker model run hf.co/KhimNguyen/chart2text
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README.md
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# Fine-tune Donut to extract data from chart
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## Data
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The data used to train, validate and test is published by account named TeeA via this link huggingface.co/datasets/TeeA/Vietnamese-Chart-Dataset
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## Fine-tuning instruction
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The model is fine-tuned following the instruction of Niels Rogge - Transformer Tutorial, 2020-09-02 via https://github.com/NielsRogge/Transformers-Tutorials
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## Load model
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The model can be loaded by using DonutProcessor
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from transformers import DonutProcessor, VisionEncoderDecoderModel
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processor = DonutProcessor.from_pretrained("KhimNguyen/chart2text")
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model = VisionEncoderDecoderModel.from_pretrained("KhimNguyen/chart2text")
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