Instructions to use meharuhanzz/malayalam-printed-ocr-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meharuhanzz/malayalam-printed-ocr-base with Transformers:

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

pipe = pipeline("image-text-to-text", model="meharuhanzz/malayalam-printed-ocr-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("meharuhanzz/malayalam-printed-ocr-base")
model = AutoModelForMultimodalLM.from_pretrained("meharuhanzz/malayalam-printed-ocr-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meharuhanzz/malayalam-printed-ocr-base with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/meharuhanzz/malayalam-printed-ocr-base

SGLang

How to use meharuhanzz/malayalam-printed-ocr-base 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 "meharuhanzz/malayalam-printed-ocr-base" \
    --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": "meharuhanzz/malayalam-printed-ocr-base",
		"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 "meharuhanzz/malayalam-printed-ocr-base" \
        --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": "meharuhanzz/malayalam-printed-ocr-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use meharuhanzz/malayalam-printed-ocr-base with Docker Model Runner:
```
docker model run hf.co/meharuhanzz/malayalam-printed-ocr-base
```

malayalam-printed-ocr-base

This model is a fine-tuned version of microsoft/trocr-base-handwritten on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0090
Model Preparation Time: 0.0142
Cer: 0.0214

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: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 16
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 100
num_epochs: 3

Training results

Framework versions

Transformers 4.57.1
Pytorch 2.9.0+cu128
Datasets 4.2.0
Tokenizers 0.22.1

Downloads last month: 4

Safetensors

Model size

0.3B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 1 Ask for provider support

Model tree for meharuhanzz/malayalam-printed-ocr-base

Base model

microsoft/trocr-base-handwritten

Finetuned

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