Instructions to use LLMsForHepth/s6-L-3.1-8B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use LLMsForHepth/s6-L-3.1-8B-base with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "LLMsForHepth/s6-L-3.1-8B-base") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from LLMsForHepth/s6-L-3.1-8B-base: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/LLMsForHepth/s6-L-3.1-8B-base/resolve/main/tokenizer.json
- Command line
-
hf download hf://LLMsForHepth/s6-L-3.1-8B-base/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/LLMsForHepth/s6-L-3.1-8B-base/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- b93f7fdfe593b3cb84d293461b62167ef914eda1332af5d2dbc8c5929e52fe02
- Size of remote file:
- 17.2 MB
- SHA256:
- c8897d80949ba1d66d7ad21dcb95665bd9bb09b5e2df17229a0cd6cacf22a276
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.