Instructions to use Chrome540/qwen_f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chrome540/qwen_f with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Chrome540/qwen_f")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Chrome540/qwen_f") model = AutoModel.from_pretrained("Chrome540/qwen_f", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Download tokenizer.json from Chrome540/qwen_f: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Chrome540/qwen_f/resolve/main/tokenizer.json
- Command line
-
hf download hf://Chrome540/qwen_f/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Chrome540/qwen_f/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- a7030cf2e58dead38199a68a8cd6f6f1a609a6072d7fb38ba5f85b3bb7e21557
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.