Instructions to use SriDoc/TrOCR-Sin-Handwritten with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SriDoc/TrOCR-Sin-Handwritten with Transformers:

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

pipe = pipeline("image-text-to-text", model="SriDoc/TrOCR-Sin-Handwritten")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("SriDoc/TrOCR-Sin-Handwritten")
model = AutoModelForImageTextToText.from_pretrained("SriDoc/TrOCR-Sin-Handwritten")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SriDoc/TrOCR-Sin-Handwritten with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SriDoc/TrOCR-Sin-Handwritten

SGLang

How to use SriDoc/TrOCR-Sin-Handwritten 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 "SriDoc/TrOCR-Sin-Handwritten" \
    --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": "SriDoc/TrOCR-Sin-Handwritten",
		"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 "SriDoc/TrOCR-Sin-Handwritten" \
        --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": "SriDoc/TrOCR-Sin-Handwritten",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SriDoc/TrOCR-Sin-Handwritten with Docker Model Runner:
```
docker model run hf.co/SriDoc/TrOCR-Sin-Handwritten
```

TrOCR-SIN-DeiT-Handwritten

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: 2.9839
Cer: 0.5253

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
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer
0.2915	3.45	100	1.8613	0.6450
0.061	6.9	200	1.8118	0.5707
0.0363	10.34	300	2.3998	0.6420
0.0202	13.79	400	2.4144	0.6353
0.0329	17.24	500	2.4393	0.6577
0.0364	20.69	600	1.9231	0.5679
0.004	24.14	700	2.4344	0.5866
0.0167	27.59	800	3.0998	0.5744
0.0269	31.03	900	2.6785	0.5804
0.0151	34.48	1000	2.2443	0.5916
0.0008	37.93	1100	2.1480	0.5684
0.0067	41.38	1200	2.3553	0.5625
0.0198	44.83	1300	2.1915	0.5492
0.0002	48.28	1400	2.0370	0.5620
0.001	51.72	1500	2.4303	0.6056
0.1666	55.17	1600	2.3324	0.5627
0.0001	58.62	1700	2.8753	0.5582
0.0	62.07	1800	2.5749	0.5355
0.0002	65.52	1900	2.8105	0.5572
0.0	68.97	2000	2.5275	0.5462
0.1231	72.41	2100	2.7452	0.5477
0.0	75.86	2200	2.4278	0.5403
0.0	79.31	2300	3.0099	0.5487
0.0	82.76	2400	3.1290	0.5467
0.0	86.21	2500	2.7705	0.5263
0.0	89.66	2600	2.7828	0.5275
0.0	93.1	2700	3.2488	0.5345
0.0	96.55	2800	3.1309	0.5273
0.0	100.0	2900	2.9839	0.5253

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.18.0
Tokenizers 0.15.1

Downloads last month: 7

Safetensors

Model size

0.2B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

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

Model tree for SriDoc/TrOCR-Sin-Handwritten

Base model

SriDoc/TrOCR-Sin-Printed

Finetuned

(4)

this model

SriDoc
/

TrOCR-Sin-Handwritten

TrOCR-SIN-DeiT-Handwritten

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for SriDoc/TrOCR-Sin-Handwritten

Space using SriDoc/TrOCR-Sin-Handwritten 1