Sentence Similarity
sentence-transformers
Safetensors
Transformers
multilingual
llama_nemotron_vl
feature-extraction
retrieval
visual document retrieval
vlm embedding
page image embedding
text embedding
semantic search
question-answering retrieval
rag
custom_code
Instructions to use novelcore/chimera-vdr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use novelcore/chimera-vdr with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("novelcore/chimera-vdr", trust_remote_code=True) 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] - Transformers
How to use novelcore/chimera-vdr with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("novelcore/chimera-vdr", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from novelcore/chimera-vdr: direct link, hf CLI and curl.
- Browser
- Download file 277 Bytes
-
https://huggingface.co/novelcore/chimera-vdr/resolve/main/modules.json
- Command line
-
hf download hf://novelcore/chimera-vdr/modules.json
-
curl -L -o modules.json https://huggingface.co/novelcore/chimera-vdr/resolve/main/modules.json
277 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling" | |
| } | |
| ] |