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 config.json from nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096: direct link, hf CLI and curl.
- Browser
- Download file 644 Bytes
-
https://huggingface.co/nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096/resolve/main/config.json
- Command line
-
hf download hf://nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096/config.json
-
curl -L -o config.json https://huggingface.co/nanovdr/NanoVDR-D-HiRes-Qwen3VL8B-4096/resolve/main/config.json
644 Bytes
| { | |
| "architectures": [ | |
| "NanoVDRDocModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "modeling_nanovdr_doc.NanoVDRDocConfig", | |
| "AutoModel": "modeling_nanovdr_doc.NanoVDRDocModel" | |
| }, | |
| "dtype": "float32", | |
| "embed_dim": 4096, | |
| "image_size": 448, | |
| "model_type": "nanovdr_doc", | |
| "pixel_shuffle_r": 1, | |
| "pool_type": "mean", | |
| "text_attn_implementation": "sdpa", | |
| "text_backbone_name": "answerdotai/ModernBERT-base", | |
| "tile_max_num": 6, | |
| "tile_max_total": 7, | |
| "tile_min_num": 1, | |
| "tile_use_thumbnail": true, | |
| "transformers_version": "4.57.6", | |
| "use_flash_attn": false, | |
| "visual_encoder_name": "OpenGVLab/InternViT-300M-448px-V2_5" | |
| } | |