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
File size: 564 Bytes
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{
"idx": 0,
"name": "0",
"path": "",
"type": "sentence_transformers.base.modules.transformer.Transformer"
},
{
"idx": 1,
"name": "1",
"path": "1_Dense",
"type": "sentence_transformers.base.modules.dense.Dense"
},
{
"idx": 2,
"name": "2",
"path": "2_Normalize",
"type": "sentence_transformers.base.modules.normalize.Normalize"
},
{
"idx": 3,
"name": "3",
"path": "3_MultiVectorMask",
"type": "sentence_transformers.multi_vector_encoder.modules.multi_vector_mask.MultiVectorMask"
}
]
|