Image-Text-to-Text
Transformers
TensorBoard
Safetensors
vision-encoder-decoder
Generated from Trainer
Instructions to use yoon1000/TrOCR_0208-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yoon1000/TrOCR_0208-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="yoon1000/TrOCR_0208-2")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("yoon1000/TrOCR_0208-2") model = AutoModelForMultimodalLM.from_pretrained("yoon1000/TrOCR_0208-2") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yoon1000/TrOCR_0208-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yoon1000/TrOCR_0208-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yoon1000/TrOCR_0208-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yoon1000/TrOCR_0208-2
- SGLang
How to use yoon1000/TrOCR_0208-2 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 "yoon1000/TrOCR_0208-2" \ --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": "yoon1000/TrOCR_0208-2", "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 "yoon1000/TrOCR_0208-2" \ --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": "yoon1000/TrOCR_0208-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yoon1000/TrOCR_0208-2 with Docker Model Runner:
docker model run hf.co/yoon1000/TrOCR_0208-2
TrOCR_0208-2
This model is a fine-tuned version of microsoft/trocr-base-stage1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.2584
- Cer: 0.1211
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 1.3873 | 1.71 | 500 | 1.6813 | 0.2361 |
| 0.8298 | 3.42 | 1000 | 1.7390 | 0.2441 |
| 0.5587 | 5.14 | 1500 | 1.5896 | 0.2090 |
| 0.376 | 6.85 | 2000 | 1.4717 | 0.1775 |
| 0.2847 | 8.56 | 2500 | 1.5528 | 0.1928 |
| 0.2376 | 10.27 | 3000 | 1.4412 | 0.1727 |
| 0.2101 | 11.99 | 3500 | 1.3770 | 0.1592 |
| 0.2551 | 13.7 | 4000 | 1.4311 | 0.1564 |
| 0.226 | 15.41 | 4500 | 1.2536 | 0.1337 |
| 0.1365 | 17.12 | 5000 | 1.2753 | 0.1272 |
| 0.14 | 18.84 | 5500 | 1.2584 | 0.1211 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.13.0
- Tokenizers 0.15.0
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Model tree for yoon1000/TrOCR_0208-2
Base model
microsoft/trocr-base-stage1