Image-Text-to-Text
Safetensors
ColPali
sentence-transformers
sauerkrautlm-colpali
collfm2
document-retrieval
vision-language-model
multi-vector
late-interaction
visual-retrieval
lfm2
small-model
efficient
curriculum-learning
hierarchical-merge
mteb
vidore
Instructions to use VAGOsolutions/SauerkrautLM-ColLFM2-450M-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ColPali
How to use VAGOsolutions/SauerkrautLM-ColLFM2-450M-v0.1 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
- sentence-transformers
How to use VAGOsolutions/SauerkrautLM-ColLFM2-450M-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("VAGOsolutions/SauerkrautLM-ColLFM2-450M-v0.1") 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
| { | |
| "architectures": [ | |
| "ColLFM2" | |
| ], | |
| "model_type": "collfm2", | |
| "base_model": "LiquidAI/LFM2-VL-450M", | |
| "dim": 128, | |
| "mask_non_image_embeddings": false, | |
| "_name_or_path": "VAGOsolutions/SauerkrautLM-ColLFM2-450M-v0.1", | |
| "hidden_size": 1024 | |
| } |