Instructions to use tlstkr/git-base-clothes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("tlstkr/git-base-clothes")
model = AutoModelForMultimodalLM.from_pretrained("tlstkr/git-base-clothes", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 / README.md

tlstkr

End of training

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	---
	library_name: transformers
	license: mit
	base_model: microsoft/git-base
	tags:
	- generated_from_trainer
	model-index:
	- name: git-base-clothes
	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-clothes

	This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/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