Instructions to use yrhong/llama-3.1-8b-instruct-sft-seed44 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use yrhong/llama-3.1-8b-instruct-sft-seed44 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "yrhong/llama-3.1-8b-instruct-sft-seed44") - Notebooks
- Google Colab
- Kaggle
yrhong/llama-3.1-8b-instruct-sft-seed44
LoRA adapter for meta-llama/Llama-3.1-8B-Instruct.
- Training setting: SFT
- Seed: 44
- Adapter format: PEFT LoRA
Usage (Transformers + PEFT)
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = "meta-llama/Llama-3.1-8B-Instruct"
adapter_repo = "yrhong/llama-3.1-8b-instruct-sft-seed44"
model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, adapter_repo)
tokenizer = AutoTokenizer.from_pretrained(base_model)
Notes
- This repository contains adapter weights only, not full base model weights.
- Use only for research and evaluation purposes.
- Downloads last month
- 4
Model tree for yrhong/llama-3.1-8b-instruct-sft-seed44
Base model
meta-llama/Llama-3.1-8B Finetuned
meta-llama/Llama-3.1-8B-Instruct