git-base-naruto
This model is a fine-tuned version of microsoft/git-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0400
- Wer Score: 0.3529
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|---|---|---|---|---|
| 7.3756 | 5.8824 | 50 | 4.6183 | 7.9118 |
| 2.5329 | 11.7647 | 100 | 0.6340 | 7.0 |
| 0.199 | 17.6471 | 150 | 0.0438 | 0.7941 |
| 0.0155 | 23.5294 | 200 | 0.0390 | 0.8529 |
| 0.0051 | 29.4118 | 250 | 0.0385 | 0.3529 |
| 0.0025 | 35.2941 | 300 | 0.0392 | 0.3235 |
| 0.0018 | 41.1765 | 350 | 0.0397 | 0.3529 |
| 0.0016 | 47.0588 | 400 | 0.0400 | 0.3529 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Base model
microsoft/git-base
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "uwwee/git-base-naruto"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "uwwee/git-base-naruto", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'