Instructions to use renyiyu/llama-2-7b-sft-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use renyiyu/llama-2-7b-sft-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "renyiyu/llama-2-7b-sft-lora") - Notebooks
- Google Colab
- Kaggle
Model Details
- Supervised fine-tuning (sft) based on meta-llama/Llama-2-7b-hf on yahma/alpaca-cleaned
- Trained with Deepspeed ZeRO-1 + TRL + QLoRA + Flash-Attntion 2 within 1h with 3090x4
- The LoRa adapter is uploaded
Model and Training Details
Finetuned from model: meta-llama/Llama-2-7b-hf
Dataset: yahma/alpaca-cleaned
Preprocessing
- preprocessed and packed the sft dataset with trl.trainer.ConstantLengthDataset
Results
Compute Infrastructure
The model is trained using 4 * RTX 3090 - 24GB
Model Card Authors
Yiyu (Michael) Ren
Model Card Contact
Email: renyiyuap@gmail.com
Framework versions
- PEFT 0.8.2
- Downloads last month
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Base model
meta-llama/Llama-2-7b-hf