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

File size: 2,690 Bytes

ef16c1d

---
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