Instructions to use machinelearningzuu/sinhala-book-captioning-repo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use machinelearningzuu/sinhala-book-captioning-repo with Transformers:

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

pipe = pipeline("image-text-to-text", model="machinelearningzuu/sinhala-book-captioning-repo")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("machinelearningzuu/sinhala-book-captioning-repo")
model = AutoModelForImageTextToText.from_pretrained("machinelearningzuu/sinhala-book-captioning-repo")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use machinelearningzuu/sinhala-book-captioning-repo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/machinelearningzuu/sinhala-book-captioning-repo

SGLang

How to use machinelearningzuu/sinhala-book-captioning-repo 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 "machinelearningzuu/sinhala-book-captioning-repo" \
    --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": "machinelearningzuu/sinhala-book-captioning-repo",
		"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 "machinelearningzuu/sinhala-book-captioning-repo" \
        --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": "machinelearningzuu/sinhala-book-captioning-repo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use machinelearningzuu/sinhala-book-captioning-repo with Docker Model Runner:
```
docker model run hf.co/machinelearningzuu/sinhala-book-captioning-repo
```

sinhala-book-captioning-repo

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: 0.0053
Wer Score: 1.3218

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: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Score
5.2519	0.3	50	4.5638	1.0
0.6662	0.6	100	0.3785	1.0
0.0214	0.9	150	0.0154	0.8161
0.0123	1.2	200	0.0104	0.7356
0.0077	1.5	250	0.0095	0.7126
0.0091	1.8	300	0.0080	0.6897
0.009	2.1	350	0.0076	0.6897
0.008	2.4	400	0.0076	0.7356
0.0066	2.69	450	0.0071	0.6667
0.0065	2.99	500	0.0068	0.6437
0.0068	3.29	550	0.0063	0.6207
0.0066	3.59	600	0.0063	5.1494
0.0063	3.89	650	0.0061	0.6322
0.0063	4.19	700	0.0055	0.5287
0.007	4.49	750	0.0053	0.5172
0.0054	4.79	800	0.0053	1.3218

Framework versions

Transformers 4.30.2
Pytorch 1.13.0
Datasets 2.11.0
Tokenizers 0.13.0

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