Instructions to use jfang/glv4-1v-all-linear-500sample-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jfang/glv4-1v-all-linear-500sample-random with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jfang/glv4-1v-all-linear-500sample-random", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from jfang/glv4-1v-all-linear-500sample-random: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/jfang/glv4-1v-all-linear-500sample-random/resolve/main/tokenizer.json
- Command line
-
hf download hf://jfang/glv4-1v-all-linear-500sample-random/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jfang/glv4-1v-all-linear-500sample-random/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 2e297923c1d0fc148c109028551abef5198a7e5eb21c5c90beb2308d7053c2e0
- Size of remote file:
- 20 MB
- SHA256:
- d802c61882d225c2038222543d647b34120c74486437c98031edfdf7611e1c5f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.