Instructions to use SkillScreen/SkillScreen-2B-SD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkillScreen/SkillScreen-2B-SD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SkillScreen/SkillScreen-2B-SD")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("SkillScreen/SkillScreen-2B-SD") model = AutoModelForSequenceClassification.from_pretrained("SkillScreen/SkillScreen-2B-SD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download adapter/tokenizer.json from SkillScreen/SkillScreen-2B-SD: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/SkillScreen/SkillScreen-2B-SD/resolve/main/adapter/tokenizer.json
- Command line
-
hf download hf://SkillScreen/SkillScreen-2B-SD/adapter/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SkillScreen/SkillScreen-2B-SD/resolve/main/adapter/tokenizer.json
20 MB
- Xet hash:
- 27d2eee8f623e7d97acc63d53705a9359643550d64fbfba9f1bc7cdc95a8e547
- Size of remote file:
- 20 MB
- SHA256:
- f399b3cd12fa270d51457bb749fb30863521e8359b8a27059c71b6c2f7d6dd6c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.