Instructions to use cyttic/trocr-bigram5-BY with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyttic/trocr-bigram5-BY with Transformers:

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

pipe = pipeline("image-text-to-text", model="cyttic/trocr-bigram5-BY")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("cyttic/trocr-bigram5-BY")
model = AutoModelForMultimodalLM.from_pretrained("cyttic/trocr-bigram5-BY", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cyttic/trocr-bigram5-BY with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyttic/trocr-bigram5-BY"
# 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-bigram5-BY",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cyttic/trocr-bigram5-BY

SGLang

How to use cyttic/trocr-bigram5-BY 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-bigram5-BY" \
    --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-bigram5-BY",
		"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-bigram5-BY" \
        --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-bigram5-BY",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cyttic/trocr-bigram5-BY with Docker Model Runner:
```
docker model run hf.co/cyttic/trocr-bigram5-BY
```

trocr-bigram5-BY / README.md

cyttic

End of training

a98c0b8 verified 9 days ago

preview code

Raw

History Blame Contribute Delete

3.25 kB

	---
	library_name: transformers
	base_model: cyttic/exp2-frozen-benyehuda-cont
	tags:
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: trocr-bigram5-BY
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# trocr-bigram5-BY

	This model is a fine-tuned version of [cyttic/exp2-frozen-benyehuda-cont](https://huggingface.co/cyttic/exp2-frozen-benyehuda-cont) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5955
	- Cer: 0.0292
	- Wer: 0.0838

	## 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: 4650
	- num_epochs: 3

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| Wer \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\|:------:\|
	\| 4.0314 \| 0.1290 \| 2000 \| 1.7102 \| 0.1973 \| 0.3994 \|
	\| 2.9555 \| 0.2581 \| 4000 \| 1.4864 \| 0.1415 \| 0.3105 \|
	\| 2.8902 \| 0.3871 \| 6000 \| 1.2645 \| 0.1040 \| 0.2529 \|
	\| 2.5335 \| 0.5161 \| 8000 \| 1.1481 \| 0.0841 \| 0.2173 \|
	\| 2.2147 \| 0.6452 \| 10000 \| 1.0777 \| 0.0772 \| 0.2002 \|
	\| 2.1987 \| 0.7742 \| 12000 \| 0.9946 \| 0.0666 \| 0.1747 \|
	\| 2.1294 \| 0.9032 \| 14000 \| 0.9288 \| 0.0605 \| 0.1616 \|
	\| 1.7200 \| 1.0323 \| 16000 \| 0.8747 \| 0.0541 \| 0.1501 \|
	\| 1.5009 \| 1.1613 \| 18000 \| 0.8430 \| 0.0500 \| 0.1391 \|
	\| 1.4939 \| 1.2903 \| 20000 \| 0.7990 \| 0.0451 \| 0.1266 \|
	\| 1.3924 \| 1.4194 \| 22000 \| 0.7779 \| 0.0426 \| 0.1194 \|
	\| 1.3807 \| 1.5484 \| 24000 \| 0.7468 \| 0.0431 \| 0.1200 \|
	\| 1.5015 \| 1.6774 \| 26000 \| 0.7113 \| 0.0355 \| 0.1034 \|
	\| 1.3585 \| 1.8065 \| 28000 \| 0.6908 \| 0.0350 \| 0.1011 \|
	\| 1.3697 \| 1.9355 \| 30000 \| 0.6745 \| 0.0359 \| 0.1016 \|
	\| 1.0135 \| 2.0645 \| 32000 \| 0.6620 \| 0.0346 \| 0.0994 \|
	\| 0.9966 \| 2.1935 \| 34000 \| 0.6456 \| 0.0349 \| 0.0985 \|
	\| 0.9714 \| 2.3226 \| 36000 \| 0.6364 \| 0.0324 \| 0.0934 \|
	\| 0.9632 \| 2.4516 \| 38000 \| 0.6226 \| 0.0307 \| 0.0886 \|
	\| 0.9263 \| 2.5806 \| 40000 \| 0.6129 \| 0.0303 \| 0.0887 \|
	\| 1.0649 \| 2.7097 \| 42000 \| 0.6051 \| 0.0298 \| 0.0863 \|
	\| 1.0234 \| 2.8387 \| 44000 \| 0.5983 \| 0.0285 \| 0.0823 \|
	\| 1.0024 \| 2.9677 \| 46000 \| 0.5955 \| 0.0293 \| 0.0839 \|
	\| 0.8827 \| 3.0 \| 46500 \| 0.5955 \| 0.0292 \| 0.0838 \|


	### Framework versions

	- Transformers 5.15.0
	- Pytorch 2.11.0+cu128
	- Datasets 5.0.1
	- Tokenizers 0.22.2