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
File size: 549 Bytes
2ae3a65 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"auto_map": {
"AutoProcessor": "processing_llama_nemotron_vl.LlamaNemotronVLProcessor"
},
"dynamic_image_size": true,
"image_size": 512,
"max_input_tiles": 6,
"norm_type": "siglip",
"num_channels": 3,
"num_image_token": 256,
"p_max_length": 4096,
"pad_to_multiple_of": null,
"padding": true,
"passage_prefix": "passage:",
"processor_class": "LlamaNemotronVLProcessor",
"q_max_length": 512,
"query_prefix": "query:",
"system_message": "",
"template": "bidirectional-llama-retriever",
"use_thumbnail": true
}
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