Instructions to use dragonstar/image-text-captcha-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dragonstar/image-text-captcha-v2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="dragonstar/image-text-captcha-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("dragonstar/image-text-captcha-v2")
model = AutoModelForImageTextToText.from_pretrained("dragonstar/image-text-captcha-v2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dragonstar/image-text-captcha-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dragonstar/image-text-captcha-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dragonstar/image-text-captcha-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dragonstar/image-text-captcha-v2

SGLang

How to use dragonstar/image-text-captcha-v2 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 "dragonstar/image-text-captcha-v2" \
    --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": "dragonstar/image-text-captcha-v2",
		"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 "dragonstar/image-text-captcha-v2" \
        --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": "dragonstar/image-text-captcha-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dragonstar/image-text-captcha-v2 with Docker Model Runner:
```
docker model run hf.co/dragonstar/image-text-captcha-v2
```

image-text-captcha-v2

This model is a fine-tuned version of microsoft/trocr-base-printed on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5030
Cer: 0.0588

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: 8
eval_batch_size: 8
seed: 42
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	Cer
13.43	1.0	373	0.6973	0.1290
0.6695	2.0	746	0.5942	0.1082
0.2885	3.0	1119	0.5038	0.0930
0.2885	4.0	1492	0.4981	0.0636
0.0946	5.0	1865	0.5030	0.0588

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3

Downloads last month: 14

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for dragonstar/image-text-captcha-v2

Base model

microsoft/trocr-base-printed

Finetuned

(24)

this model

dragonstar
/

image-text-captcha-v2

image-text-captcha-v2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for dragonstar/image-text-captcha-v2

Space using dragonstar/image-text-captcha-v2 1