Instructions to use tlstkr/git-base-clothes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tlstkr/git-base-clothes with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="tlstkr/git-base-clothes")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("tlstkr/git-base-clothes") model = AutoModelForImageTextToText.from_pretrained("tlstkr/git-base-clothes") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use tlstkr/git-base-clothes with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tlstkr/git-base-clothes" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tlstkr/git-base-clothes", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tlstkr/git-base-clothes
- SGLang
How to use tlstkr/git-base-clothes 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 "tlstkr/git-base-clothes" \ --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": "tlstkr/git-base-clothes", "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 "tlstkr/git-base-clothes" \ --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": "tlstkr/git-base-clothes", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tlstkr/git-base-clothes with Docker Model Runner:
docker model run hf.co/tlstkr/git-base-clothes
git-base-clothes
This model is a fine-tuned version of microsoft/git-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2143
- Wer Score: 2.4047
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Score |
|---|---|---|---|---|
| 7.1475 | 3.5714 | 50 | 4.4300 | 0.8516 |
| 2.384 | 7.1429 | 100 | 0.6209 | 0.7782 |
| 0.3154 | 10.7143 | 150 | 0.2059 | 2.0570 |
| 0.1406 | 14.2857 | 200 | 0.1841 | 2.6379 |
| 0.0927 | 17.8571 | 250 | 0.1831 | 2.5492 |
| 0.062 | 21.4286 | 300 | 0.1891 | 2.6554 |
| 0.0423 | 25.0 | 350 | 0.1938 | 2.5195 |
| 0.0292 | 28.5714 | 400 | 0.1996 | 2.5295 |
| 0.0214 | 32.1429 | 450 | 0.2034 | 2.4541 |
| 0.0169 | 35.7143 | 500 | 0.2082 | 2.5956 |
| 0.0139 | 39.2857 | 550 | 0.2105 | 2.2852 |
| 0.0119 | 42.8571 | 600 | 0.2129 | 2.4300 |
| 0.0106 | 46.4286 | 650 | 0.2138 | 2.4355 |
| 0.01 | 50.0 | 700 | 0.2143 | 2.4047 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
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Model tree for tlstkr/git-base-clothes
Base model
microsoft/git-base