Feature Extraction
Transformers
Safetensors
English
qwen2_vl
image-text-to-text
multimodal
video embedding
ncsoft
ncai
varco
Instructions to use OpenLLM-Korea/GME-VARCO-VISION-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenLLM-Korea/GME-VARCO-VISION-Embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="OpenLLM-Korea/GME-VARCO-VISION-Embedding")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("OpenLLM-Korea/GME-VARCO-VISION-Embedding") model = AutoModelForMultimodalLM.from_pretrained("OpenLLM-Korea/GME-VARCO-VISION-Embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from OpenLLM-Korea/GME-VARCO-VISION-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/OpenLLM-Korea/GME-VARCO-VISION-Embedding/resolve/main/tokenizer.json
- Command line
-
hf download hf://OpenLLM-Korea/GME-VARCO-VISION-Embedding/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/OpenLLM-Korea/GME-VARCO-VISION-Embedding/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- d8afa1f6f3ea3f16d98fce7895282a83c4eb38b30af190f43649a5188663f721
- Size of remote file:
- 11.4 MB
- SHA256:
- 091aa7594dc2fcfbfa06b9e3c22a5f0562ac14f30375c13af7309407a0e67b8a
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