Instructions to use jealk/llm2vec-da-mntp-sheared with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jealk/llm2vec-da-mntp-sheared with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, LlamaBiForMNTP tokenizer = AutoTokenizer.from_pretrained("jealk/llm2vec-da-mntp-sheared") model = LlamaBiForMNTP.from_pretrained("jealk/llm2vec-da-mntp-sheared", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from jealk/llm2vec-da-mntp-sheared: direct link, hf CLI and curl.
- Browser
- Download file 3.62 MB
-
https://huggingface.co/jealk/llm2vec-da-mntp-sheared/resolve/main/tokenizer.json
- Command line
-
hf download hf://jealk/llm2vec-da-mntp-sheared/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jealk/llm2vec-da-mntp-sheared/resolve/main/tokenizer.json
3.62 MB
File too large to display, you can check the raw version instead.