Instructions to use TuringGame/Llama-3.2-1B-Instruct-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use TuringGame/Llama-3.2-1B-Instruct-tuned with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "TuringGame/Llama-3.2-1B-Instruct-tuned") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from TuringGame/Llama-3.2-1B-Instruct-tuned: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/TuringGame/Llama-3.2-1B-Instruct-tuned/resolve/main/tokenizer.json
- Command line
-
hf download hf://TuringGame/Llama-3.2-1B-Instruct-tuned/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/TuringGame/Llama-3.2-1B-Instruct-tuned/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- c2bc2742787c9a9cbc3f85f673422734612721fed4ae1dcccb462b9900e9f69f
- Size of remote file:
- 17.2 MB
- SHA256:
- 9170a9137d29f9085955a34c8ccc8aeafba9ebaae92f505835f6c6ff66653619
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.