Instructions to use smangrul/tinyllama_lora_adcopy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use smangrul/tinyllama_lora_adcopy with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T") model = PeftModel.from_pretrained(base_model, "smangrul/tinyllama_lora_adcopy") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from smangrul/tinyllama_lora_adcopy: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/smangrul/tinyllama_lora_adcopy/resolve/main/training_args.bin
- Command line
-
hf download hf://smangrul/tinyllama_lora_adcopy/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/smangrul/tinyllama_lora_adcopy/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- a78015a094ac89bd0c4dfe8b7a034354c9b21ee4ce65fbc613a44b7cde650ae5
- Size of remote file:
- 4.73 kB
- SHA256:
- 2acbb0387c44bfc7c33361851a55602fa4440d5150c071c8e94144f53625f78e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.