Image-Text-to-Text
Transformers
TensorBoard
Safetensors
vision-encoder-decoder
Generated from Trainer
Instructions to use ManBib/trocr-large-printed-im2latex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManBib/trocr-large-printed-im2latex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ManBib/trocr-large-printed-im2latex")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("ManBib/trocr-large-printed-im2latex") model = AutoModelForImageTextToText.from_pretrained("ManBib/trocr-large-printed-im2latex") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ManBib/trocr-large-printed-im2latex with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ManBib/trocr-large-printed-im2latex" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ManBib/trocr-large-printed-im2latex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ManBib/trocr-large-printed-im2latex
- SGLang
How to use ManBib/trocr-large-printed-im2latex 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 "ManBib/trocr-large-printed-im2latex" \ --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": "ManBib/trocr-large-printed-im2latex", "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 "ManBib/trocr-large-printed-im2latex" \ --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": "ManBib/trocr-large-printed-im2latex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ManBib/trocr-large-printed-im2latex with Docker Model Runner:
docker model run hf.co/ManBib/trocr-large-printed-im2latex
trocr-large-printed-im2latex100k
This model is a fine-tuned version of microsoft/trocr-large-stage1 on an unknown dataset.
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1
- Datasets 2.17.0
- Tokenizers 0.15.2
- Downloads last month
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Model tree for ManBib/trocr-large-printed-im2latex
Base model
microsoft/trocr-large-stage1