Instructions to use Kim-Rass/InternVL3-2B-KDL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kim-Rass/InternVL3-2B-KDL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Kim-Rass/InternVL3-2B-KDL", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Kim-Rass/InternVL3-2B-KDL", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Kim-Rass/InternVL3-2B-KDL: direct link, hf CLI and curl.
- Browser
- Download file 11.5 MB
-
https://huggingface.co/Kim-Rass/InternVL3-2B-KDL/resolve/main/tokenizer.json
- Command line
-
hf download hf://Kim-Rass/InternVL3-2B-KDL/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Kim-Rass/InternVL3-2B-KDL/resolve/main/tokenizer.json
11.5 MB
- Xet hash:
- a6e5daa6a029515fe8a21ec3128833758094766b393eddc5179e4756294713e8
- Size of remote file:
- 11.5 MB
- SHA256:
- ee4e8db53f790ae78c34b5b7463becd51494b2d04f5eee63b295a23f0d1c7fe3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.