Feature Extraction
sentence-transformers
Safetensors
Transformers
nanovdr_doc
visual-document-retrieval
document-ai
nanovdr
knowledge-distillation
retrieval
custom_code
Instructions to use nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096", 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 nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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