Instructions to use dasomoh88/Donut_KCI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dasomoh88/Donut_KCI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="dasomoh88/Donut_KCI")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("dasomoh88/Donut_KCI") model = AutoModelForImageTextToText.from_pretrained("dasomoh88/Donut_KCI") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dasomoh88/Donut_KCI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dasomoh88/Donut_KCI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dasomoh88/Donut_KCI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dasomoh88/Donut_KCI
- SGLang
How to use dasomoh88/Donut_KCI 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 "dasomoh88/Donut_KCI" \ --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": "dasomoh88/Donut_KCI", "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 "dasomoh88/Donut_KCI" \ --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": "dasomoh88/Donut_KCI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dasomoh88/Donut_KCI with Docker Model Runner:
docker model run hf.co/dasomoh88/Donut_KCI
Training in progress, epoch 0
Browse files- config.json +2 -2
- generation_config.json +1 -1
- pytorch_model.bin +2 -2
config.json
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"typical_p": 1.0,
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"use_bfloat16": false,
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"use_cache": true,
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"vocab_size":
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"decoder_start_token_id":
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"encoder": {
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"_name_or_path": "",
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"add_cross_attention": false,
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"typical_p": 1.0,
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"use_bfloat16": false,
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"use_cache": true,
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"vocab_size": 57528
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"decoder_start_token_id": 57527,
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"encoder": {
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"_name_or_path": "",
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"add_cross_attention": false,
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"decoder_start_token_id":
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"eos_token_id": 2,
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"forced_eos_token_id": 2,
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"max_length": 768,
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"decoder_start_token_id": 57527,
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"eos_token_id": 2,
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"forced_eos_token_id": 2,
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"max_length": 768,
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pytorch_model.bin
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size 809191193
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