Instructions to use SriSanthM/LLAMA-3.2-1B-MathInstrcu-LORA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SriSanthM/LLAMA-3.2-1B-MathInstrcu-LORA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B") model = PeftModel.from_pretrained(base_model, "SriSanthM/LLAMA-3.2-1B-MathInstrcu-LORA") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-800/optimizer.pt from SriSanthM/LLAMA-3.2-1B-MathInstrcu-LORA: direct link, hf CLI and curl.
- Browser
- Download file 45.3 MB
-
https://huggingface.co/SriSanthM/LLAMA-3.2-1B-MathInstrcu-LORA/resolve/main/checkpoint-800/optimizer.pt
- Command line
-
hf download hf://SriSanthM/LLAMA-3.2-1B-MathInstrcu-LORA/checkpoint-800/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/SriSanthM/LLAMA-3.2-1B-MathInstrcu-LORA/resolve/main/checkpoint-800/optimizer.pt
45.3 MB
- Xet hash:
- dd728f9231c5a8a31f18df87a7960f2702a8af881fb3563b4d4b94c0234841bf
- Size of remote file:
- 45.3 MB
- SHA256:
- ad3cf72a3adc57837cbe4c2b468f6c4339eb1c93554a66c3b1ff5c745f293174
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.