Instructions to use ramankrishna10/npc-fin-32b-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ramankrishna10/npc-fin-32b-sft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-32b-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "ramankrishna10/npc-fin-32b-sft") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c9001cfb1f580550c74708362d2c9b38cf2c70e5222e0f90f9eb19295bf68f82
- Size of remote file:
- 2.15 GB
- SHA256:
- 80957d9bb337c35016aae8b4afa1f1b7787e3454bbf2889eacaebb42e594a5d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.