Instructions to use KiteAether/khmer-trocr-b16-10ep-18-6-25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KiteAether/khmer-trocr-b16-10ep-18-6-25 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="KiteAether/khmer-trocr-b16-10ep-18-6-25")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("KiteAether/khmer-trocr-b16-10ep-18-6-25") model = AutoModelForMultimodalLM.from_pretrained("KiteAether/khmer-trocr-b16-10ep-18-6-25", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use KiteAether/khmer-trocr-b16-10ep-18-6-25 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "KiteAether/khmer-trocr-b16-10ep-18-6-25" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "KiteAether/khmer-trocr-b16-10ep-18-6-25", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/KiteAether/khmer-trocr-b16-10ep-18-6-25
- SGLang
How to use KiteAether/khmer-trocr-b16-10ep-18-6-25 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 "KiteAether/khmer-trocr-b16-10ep-18-6-25" \ --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": "KiteAether/khmer-trocr-b16-10ep-18-6-25", "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 "KiteAether/khmer-trocr-b16-10ep-18-6-25" \ --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": "KiteAether/khmer-trocr-b16-10ep-18-6-25", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use KiteAether/khmer-trocr-b16-10ep-18-6-25 with Docker Model Runner:
docker model run hf.co/KiteAether/khmer-trocr-b16-10ep-18-6-25
khmer-trocr-b16-10ep-18-6-25
This model is a fine-tuned version of microsoft/trocr-large-handwritten on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.4942
- Cer: 0.8083
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 4.8169 | 1.0 | 841 | 5.1886 | 0.9504 |
| 3.9345 | 2.0 | 1682 | 4.8674 | 0.8794 |
| 3.4115 | 3.0 | 2523 | 4.5805 | 0.8546 |
| 2.8371 | 4.0 | 3364 | 4.4570 | 0.8164 |
| 2.2272 | 5.0 | 4205 | 4.4942 | 0.8083 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
- Downloads last month
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Model tree for KiteAether/khmer-trocr-b16-10ep-18-6-25
Base model
microsoft/trocr-large-handwritten