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

Libraries

How to use PepPixie/git-base-duski_captioner_customtrainer with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="PepPixie/git-base-duski_captioner_customtrainer")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("PepPixie/git-base-duski_captioner_customtrainer")
model = AutoModelForImageTextToText.from_pretrained("PepPixie/git-base-duski_captioner_customtrainer")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use PepPixie/git-base-duski_captioner_customtrainer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PepPixie/git-base-duski_captioner_customtrainer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PepPixie/git-base-duski_captioner_customtrainer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PepPixie/git-base-duski_captioner_customtrainer

SGLang

How to use PepPixie/git-base-duski_captioner_customtrainer 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 "PepPixie/git-base-duski_captioner_customtrainer" \
    --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": "PepPixie/git-base-duski_captioner_customtrainer",
		"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 "PepPixie/git-base-duski_captioner_customtrainer" \
        --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": "PepPixie/git-base-duski_captioner_customtrainer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PepPixie/git-base-duski_captioner_customtrainer with Docker Model Runner:
```
docker model run hf.co/PepPixie/git-base-duski_captioner_customtrainer
```

git-base-duski_captioner_customtrainer

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: 4.1536
Wer Score: 8.0755

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: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Score
7.5116	25.0	50	5.5551	8.1132
4.5914	50.0	100	4.1536	8.0755

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.6
Tokenizers 0.13.3

Downloads last month: 14

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