Visual Document Retrieval
Transformers
Safetensors
gemma3
image-text-to-text
vision-language
retrieval
multimodal
multilingual
document-retrieval
matryoshka-embeddings
dense-retrieval
22-languages
Eval Results (legacy)
text-generation-inference
Instructions to use Cognitive-Lab/NetraEmbed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cognitive-Lab/NetraEmbed with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Cognitive-Lab/NetraEmbed") model = AutoModelForMultimodalLM.from_pretrained("Cognitive-Lab/NetraEmbed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download processor_config.json from Cognitive-Lab/NetraEmbed: direct link, hf CLI and curl.
- Browser
- Download file 70 Bytes
-
https://huggingface.co/Cognitive-Lab/NetraEmbed/resolve/41efa287ecbc3097c029f216c954d6500fbfedd5/processor_config.json
- Command line
-
hf download hf://Cognitive-Lab/NetraEmbed@41efa287ecbc3097c029f216c954d6500fbfedd5/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/Cognitive-Lab/NetraEmbed/resolve/41efa287ecbc3097c029f216c954d6500fbfedd5/processor_config.json
70 Bytes
| { | |
| "image_seq_length": 256, | |
| "processor_class": "Gemma3Processor" | |
| } | |