Instructions to use cyttic/trocr-noise-bigram2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyttic/trocr-noise-bigram2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="cyttic/trocr-noise-bigram2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("cyttic/trocr-noise-bigram2")
model = AutoModelForMultimodalLM.from_pretrained("cyttic/trocr-noise-bigram2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cyttic/trocr-noise-bigram2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyttic/trocr-noise-bigram2"
# 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-noise-bigram2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/cyttic/trocr-noise-bigram2

SGLang

How to use cyttic/trocr-noise-bigram2 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-noise-bigram2" \
    --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-noise-bigram2",
		"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-noise-bigram2" \
        --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-noise-bigram2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use cyttic/trocr-noise-bigram2 with Docker Model Runner:
```
docker model run hf.co/cyttic/trocr-noise-bigram2
```

trocr-noise-bigram2 / README.md

cyttic

End of training

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	---
	library_name: transformers
	base_model: cyttic/exp2-frozen-benyehuda-cont
	tags:
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: trocr-noise-bigram2
	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-noise-bigram2

	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: 0.5353
	- Cer: 0.0311
	- Wer: 0.0865

	## 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: 0.1
	- num_epochs: 2

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| Wer \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\|:------:\|
	\| 4.4701 \| 0.1290 \| 2000 \| 2.0749 \| 0.1892 \| 0.3917 \|
	\| 3.4282 \| 0.2581 \| 4000 \| 1.5678 \| 0.1288 \| 0.2889 \|
	\| 2.6796 \| 0.3871 \| 6000 \| 1.2115 \| 0.0937 \| 0.2210 \|
	\| 2.3398 \| 0.5161 \| 8000 \| 1.0724 \| 0.0778 \| 0.1845 \|
	\| 1.9402 \| 0.6452 \| 10000 \| 0.9204 \| 0.0661 \| 0.1640 \|
	\| 1.8243 \| 0.7742 \| 12000 \| 0.8303 \| 0.0570 \| 0.1429 \|
	\| 1.6875 \| 0.9032 \| 14000 \| 0.7434 \| 0.0509 \| 0.1294 \|
	\| 1.2807 \| 1.0323 \| 16000 \| 0.6928 \| 0.0457 \| 0.1188 \|
	\| 1.2310 \| 1.1613 \| 18000 \| 0.6552 \| 0.0419 \| 0.1105 \|
	\| 1.1186 \| 1.2903 \| 20000 \| 0.6258 \| 0.0380 \| 0.1035 \|
	\| 1.0514 \| 1.4194 \| 22000 \| 0.6097 \| 0.0380 \| 0.1000 \|
	\| 1.1760 \| 1.5484 \| 24000 \| 0.5739 \| 0.0346 \| 0.0934 \|
	\| 1.0901 \| 1.6774 \| 26000 \| 0.5598 \| 0.0339 \| 0.0922 \|
	\| 1.0063 \| 1.8065 \| 28000 \| 0.5434 \| 0.0328 \| 0.0888 \|
	\| 0.9992 \| 1.9355 \| 30000 \| 0.5368 \| 0.0309 \| 0.0861 \|
	\| 1.0274 \| 2.0 \| 31000 \| 0.5353 \| 0.0311 \| 0.0865 \|


	### Framework versions

	- Transformers 5.9.0
	- Pytorch 2.11.0+cu128
	- Datasets 5.0.1
	- Tokenizers 0.22.2