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 tokenizer_config.json from Cognitive-Lab/NetraEmbed: direct link, hf CLI and curl.
- Browser
- Download file 1.16 MB
-
https://huggingface.co/Cognitive-Lab/NetraEmbed/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Cognitive-Lab/NetraEmbed/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Cognitive-Lab/NetraEmbed/resolve/main/tokenizer_config.json
1.16 MB
File too large to display, you can check the raw version instead.