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
Download sentence_bert_config.json from nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096: direct link, hf CLI and curl.
- Browser
- Download file 209 Bytes
-
https://huggingface.co/nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096/resolve/main/sentence_bert_config.json
209 Bytes
| { | |
| "transformer_task": "feature-extraction", | |
| "modality_config": { | |
| "image": { | |
| "method": "forward", | |
| "method_output_name": "embedding" | |
| } | |
| }, | |
| "module_output_name": "sentence_embedding" | |
| } | |