Text Generation
PEFT
Safetensors
English
relation-extraction
information-extraction
qlora
lora
llama
nlp
conversational
Instructions to use Despina/Llama-3.2-3B-Instruct-re_gentune-2-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Despina/Llama-3.2-3B-Instruct-re_gentune-2-shot with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct") model = PeftModel.from_pretrained(base_model, "Despina/Llama-3.2-3B-Instruct-re_gentune-2-shot") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08237b040dbc9a8448a3503c661d60e044901645841c0d92e9072a325f0fc131
- Size of remote file:
- 389 MB
- SHA256:
- a6b85b070c832ba20a32936c8636cb7050c83ba90fb7e7b9b893099675d7e67d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.