Instructions to use IshanWarshamana/checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IshanWarshamana/checkpoints with Transformers:

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

pipe = pipeline("image-text-to-text", model="IshanWarshamana/checkpoints")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("IshanWarshamana/checkpoints")
model = AutoModelForMultimodalLM.from_pretrained("IshanWarshamana/checkpoints")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IshanWarshamana/checkpoints with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/IshanWarshamana/checkpoints

SGLang

How to use IshanWarshamana/checkpoints 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 "IshanWarshamana/checkpoints" \
    --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": "IshanWarshamana/checkpoints",
		"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 "IshanWarshamana/checkpoints" \
        --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": "IshanWarshamana/checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IshanWarshamana/checkpoints with Docker Model Runner:
```
docker model run hf.co/IshanWarshamana/checkpoints
```

TrOCR_Printed_Sinahala_1

This model is a fine-tuned version of kavg/TrOCR-SIN-DeiT on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.6949
Cer: 0.1689

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
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.0989	0.0444	100	1.0041	0.1787
0.1265	0.0889	200	0.7479	0.2270
0.0302	0.1333	300	0.9396	0.1863
0.0483	0.1778	400	0.6949	0.1689
0.0637	0.2222	500	0.6022	0.1816
0.0726	0.2667	600	0.6801	0.2083
0.0169	0.3111	700	0.5828	0.1721
0.0084	0.3556	800	0.5437	0.1750
0.0239	0.4	900	0.5451	0.1744
0.0215	0.4444	1000	0.5470	0.1717

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Downloads last month: 3

Safetensors

Model size

0.2B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

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Base model

SriDoc/TrOCR-Sin-Printed

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