Instructions to use davelotito/donut_experiment_bayesian_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davelotito/donut_experiment_bayesian_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="davelotito/donut_experiment_bayesian_3")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("davelotito/donut_experiment_bayesian_3") model = AutoModelForMultimodalLM.from_pretrained("davelotito/donut_experiment_bayesian_3") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use davelotito/donut_experiment_bayesian_3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "davelotito/donut_experiment_bayesian_3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "davelotito/donut_experiment_bayesian_3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/davelotito/donut_experiment_bayesian_3
- SGLang
How to use davelotito/donut_experiment_bayesian_3 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 "davelotito/donut_experiment_bayesian_3" \ --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": "davelotito/donut_experiment_bayesian_3", "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 "davelotito/donut_experiment_bayesian_3" \ --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": "davelotito/donut_experiment_bayesian_3", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use davelotito/donut_experiment_bayesian_3 with Docker Model Runner:
docker model run hf.co/davelotito/donut_experiment_bayesian_3
Training in progress, epoch 2
Browse files
metrics.jsonl
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{"eval_loss": 0.8003033995628357, "eval_bleu": 0.03888514151556339, "eval_precisions": [0.6645021645021645, 0.5061728395061729, 0.4396551724137931, 0.3745704467353952], "eval_brevity_penalty": 0.0801536770551255, "eval_length_ratio": 0.28378378378378377, "eval_translation_length": 462, "eval_reference_length": 1628, "eval_cer": 0.7748649914772914, "eval_wer": 0.8614735352562218, "eval_runtime": 181.4627, "eval_samples_per_second": 0.314, "eval_steps_per_second": 0.314, "epoch": 1.0}
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{"eval_loss": 0.8003033995628357, "eval_bleu": 0.03888514151556339, "eval_precisions": [0.6645021645021645, 0.5061728395061729, 0.4396551724137931, 0.3745704467353952], "eval_brevity_penalty": 0.0801536770551255, "eval_length_ratio": 0.28378378378378377, "eval_translation_length": 462, "eval_reference_length": 1628, "eval_cer": 0.7748649914772914, "eval_wer": 0.8614735352562218, "eval_runtime": 181.4627, "eval_samples_per_second": 0.314, "eval_steps_per_second": 0.314, "epoch": 1.0}
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{"eval_loss": 0.6344355940818787, "eval_bleu": 0.052030851722171666, "eval_precisions": [0.7112970711297071, 0.5890736342042755, 0.5357142857142857, 0.4935064935064935], "eval_brevity_penalty": 0.0901881044262292, "eval_length_ratio": 0.2936117936117936, "eval_translation_length": 478, "eval_reference_length": 1628, "eval_cer": 0.7785648277776906, "eval_wer": 0.8579665473564559, "eval_runtime": 71.1013, "eval_samples_per_second": 0.802, "eval_steps_per_second": 0.802, "epoch": 2.0}
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model.safetensors
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