Instructions to use cyttic/trocr-freefonts-s42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyttic/trocr-freefonts-s42 with Transformers:

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

pipe = pipeline("image-text-to-text", model="cyttic/trocr-freefonts-s42")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("cyttic/trocr-freefonts-s42")
model = AutoModelForMultimodalLM.from_pretrained("cyttic/trocr-freefonts-s42", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cyttic/trocr-freefonts-s42 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyttic/trocr-freefonts-s42"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyttic/trocr-freefonts-s42",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cyttic/trocr-freefonts-s42

SGLang

How to use cyttic/trocr-freefonts-s42 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 "cyttic/trocr-freefonts-s42" \
    --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": "cyttic/trocr-freefonts-s42",
		"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 "cyttic/trocr-freefonts-s42" \
        --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": "cyttic/trocr-freefonts-s42",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cyttic/trocr-freefonts-s42 with Docker Model Runner:
```
docker model run hf.co/cyttic/trocr-freefonts-s42
```

trocr-freefonts-s42

This model is a fine-tuned version of cyttic/exp2-frozen-benyehuda-cont on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.4985
Cer: 0.0265
Wer: 0.0737

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 3712
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
4.7562	0.1617	2000	2.1726	0.2026	0.4062
3.6432	0.3233	4000	1.6432	0.1259	0.2877
2.7686	0.4850	6000	1.2582	0.0945	0.2224
2.4240	0.6466	8000	1.0569	0.0731	0.1818
1.9056	0.8083	10000	0.9249	0.0622	0.1577
1.7642	0.9700	12000	0.8333	0.0554	0.1418
1.2725	1.1316	14000	0.7680	0.0479	0.1269
1.3085	1.2933	16000	0.7127	0.0441	0.1166
1.1557	1.4549	18000	0.6728	0.0414	0.1093
1.1567	1.6166	20000	0.6353	0.0385	0.1012
1.1508	1.7782	22000	0.6103	0.0344	0.0927
1.1195	1.9399	24000	0.5686	0.0311	0.0872
0.7635	2.1015	26000	0.5583	0.0310	0.0854
0.8668	2.2632	28000	0.5392	0.0298	0.0826
0.7609	2.4248	30000	0.5275	0.0285	0.0796
0.8330	2.5865	32000	0.5138	0.0276	0.0759
0.7614	2.7482	34000	0.5056	0.0273	0.0756
0.8053	2.9098	36000	0.5016	0.0266	0.0740
0.7447	3.0	37116	0.4985	0.0265	0.0737

Framework versions

Transformers 5.15.0
Pytorch 2.11.0+cu128
Datasets 5.0.1
Tokenizers 0.22.2

Downloads last month: 22

Safetensors

Model size

0.3B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

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Model tree for cyttic/trocr-freefonts-s42

Base model

cyttic/exp2-frozen-benyehuda-cont

Finetuned

(36)

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