Image-Text-to-Text
Transformers
Safetensors
PEFT
vision-language-model
multimodal
document-understanding
document-ai
structured-extraction
json-generation
siglip
qwen2
lora
multilingual
indic-languages
english
hindi
bengali
tamil
synthetic-data
Instructions to use arikatokachi/Indic-Document-VLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arikatokachi/Indic-Document-VLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="arikatokachi/Indic-Document-VLM")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("arikatokachi/Indic-Document-VLM", device_map="auto") - PEFT
How to use arikatokachi/Indic-Document-VLM with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use arikatokachi/Indic-Document-VLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "arikatokachi/Indic-Document-VLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arikatokachi/Indic-Document-VLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/arikatokachi/Indic-Document-VLM
- SGLang
How to use arikatokachi/Indic-Document-VLM 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 "arikatokachi/Indic-Document-VLM" \ --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": "arikatokachi/Indic-Document-VLM", "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 "arikatokachi/Indic-Document-VLM" \ --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": "arikatokachi/Indic-Document-VLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use arikatokachi/Indic-Document-VLM with Docker Model Runner:
docker model run hf.co/arikatokachi/Indic-Document-VLM