Instructions to use Lillyr/pretrain_v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Lillyr/pretrain_v0 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/lustre/fs11/portfolios/llmservice/users/zhidingy/wsh-ws/playground/region/checkpoint/VideoLLaMA3-2B-local") model = PeftModel.from_pretrained(base_model, "Lillyr/pretrain_v0") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Lillyr/pretrain_v0: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Lillyr/pretrain_v0/resolve/main/tokenizer.json
- Command line
-
hf download hf://Lillyr/pretrain_v0/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Lillyr/pretrain_v0/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- c091853e775f50706afc69a57ea20538bd46d1ff3738ac404005327e002a21c0
- Size of remote file:
- 11.4 MB
- SHA256:
- 0b1831b3b06250d6d3ace338859f66def746787954133eef77d10ddef53eb794
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.