Visual Document Retrieval
PEFT
Safetensors
ColPali
vidore
multimodal_embedding
multilingual_embedding
Text-to-Visual Document (T→VD) retrieval
Instructions to use nomic-ai/nomic-embed-multimodal-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nomic-ai/nomic-embed-multimodal-3b with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct") model = PeftModel.from_pretrained(base_model, "nomic-ai/nomic-embed-multimodal-3b") - ColPali
How to use nomic-ai/nomic-embed-multimodal-3b with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from nomic-ai/nomic-embed-multimodal-3b: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/nomic-ai/nomic-embed-multimodal-3b/resolve/main/tokenizer.json
- Command line
-
hf download hf://nomic-ai/nomic-embed-multimodal-3b/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/nomic-ai/nomic-embed-multimodal-3b/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- b1386d70854cfefd60e569ee033399ce5db5f1e9b001eabb74e0ab417a39a1a9
- Size of remote file:
- 11.4 MB
- SHA256:
- 5eee858c5123a4279c3e1f7b81247343f356ac767940b2692a928ad929543214
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