Feature Extraction
Transformers
Safetensors
English
qwen2_vl
image-text-to-text
embedding
multimodal
Instructions to use naver-ai/MuCo-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use naver-ai/MuCo-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="naver-ai/MuCo-2B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("naver-ai/MuCo-2B") model = AutoModelForMultimodalLM.from_pretrained("naver-ai/MuCo-2B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from naver-ai/MuCo-2B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/naver-ai/MuCo-2B/resolve/main/tokenizer.json
- Command line
-
hf download hf://naver-ai/MuCo-2B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/naver-ai/MuCo-2B/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 5e60afffd20fb9c177b7f515cc2a54e2de0905586c370b71710dc58790510ca9
- Size of remote file:
- 11.4 MB
- SHA256:
- 662e94ef50a214a73c0cb319af42d5fc5cc554d3e6814e91cdbe8b3f86aeb1da
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.