Instructions to use uwwee/git-base-naruto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uwwee/git-base-naruto with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="uwwee/git-base-naruto")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("uwwee/git-base-naruto") model = AutoModelForMultimodalLM.from_pretrained("uwwee/git-base-naruto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use uwwee/git-base-naruto with vLLM:
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 }'Use Docker
docker model run hf.co/uwwee/git-base-naruto
- SGLang
How to use uwwee/git-base-naruto 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 "uwwee/git-base-naruto" \ --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": "uwwee/git-base-naruto", "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 "uwwee/git-base-naruto" \ --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": "uwwee/git-base-naruto", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use uwwee/git-base-naruto with Docker Model Runner:
docker model run hf.co/uwwee/git-base-naruto
| license: mit | |
| base_model: microsoft/git-base | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: git-base-naruto | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # git-base-naruto | |
| This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/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 | |