Instructions to use cyttic/trocr-small-BY5-bridgeinit-selfinit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyttic/trocr-small-BY5-bridgeinit-selfinit with Transformers:

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

pipe = pipeline("image-text-to-text", model="cyttic/trocr-small-BY5-bridgeinit-selfinit")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("cyttic/trocr-small-BY5-bridgeinit-selfinit")
model = AutoModelForMultimodalLM.from_pretrained("cyttic/trocr-small-BY5-bridgeinit-selfinit", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cyttic/trocr-small-BY5-bridgeinit-selfinit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyttic/trocr-small-BY5-bridgeinit-selfinit"
# 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-small-BY5-bridgeinit-selfinit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cyttic/trocr-small-BY5-bridgeinit-selfinit

SGLang

How to use cyttic/trocr-small-BY5-bridgeinit-selfinit 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-small-BY5-bridgeinit-selfinit" \
    --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-small-BY5-bridgeinit-selfinit",
		"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-small-BY5-bridgeinit-selfinit" \
        --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-small-BY5-bridgeinit-selfinit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cyttic/trocr-small-BY5-bridgeinit-selfinit with Docker Model Runner:
```
docker model run hf.co/cyttic/trocr-small-BY5-bridgeinit-selfinit
```

trocr-small-BY5-bridgeinit-selfinit

This model is a fine-tuned version of cyttic/trocr-hebrew-small-untrained on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 6.0410
Cer: 0.7729
Wer: 1.1879

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
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: 1550
num_epochs: 1.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Cer	Wer
13.5321	0.1290	2000	6.5331	0.7890	1.2575
12.6771	0.2581	4000	6.3124	0.7675	1.1423
12.8559	0.3871	6000	6.2843	0.7982	1.2820
12.5482	0.5161	8000	6.1586	0.7657	1.1255
12.3941	0.6452	10000	6.1102	0.7802	1.1589
12.5326	0.7742	12000	6.0781	0.7771	1.1789
12.4375	0.9032	14000	6.0520	0.7742	1.2098
12.1724	1.0	15500	6.0410	0.7729	1.1879

Framework versions

Transformers 5.15.0
Pytorch 2.10.0+cu128
Datasets 5.0.1
Tokenizers 0.22.2

Downloads last month: 42

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for cyttic/trocr-small-BY5-bridgeinit-selfinit

Base model

cyttic/trocr-hebrew-small-untrained

Finetuned

(6)

this model