Instructions to use munhim/multimodal-rag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use munhim/multimodal-rag with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("munhim/multimodal-rag") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download config.json from munhim/multimodal-rag: direct link, hf CLI and curl.
- Browser
- Download file 300 Bytes
-
https://huggingface.co/munhim/multimodal-rag/resolve/main/config.json
- Command line
-
hf download hf://munhim/multimodal-rag/config.json
-
curl -L -o config.json https://huggingface.co/munhim/multimodal-rag/resolve/main/config.json
300 Bytes
| { | |
| "text_embedding_dim": 384, | |
| "image_embedding_dim": 512, | |
| "text_model": "sentence-transformers/all-MiniLM-L6-v2", | |
| "image_model": "openai/clip-vit-base-patch32", | |
| "llm_model": "mistralai/Mistral-7B-Instruct-v0.2", | |
| "num_text_vectors": 161, | |
| "num_image_vectors": 285, | |
| "total_vectors": 446 | |
| } |