license: other
language:
- en
Model Details
This is an unofficial implementation of "AlpaGasus: Training a better Alpaca with Fewer Data." with LLaMA2 & QLoRA! Training code is available at our repo.
- Developed by: Yunsang Yoo and Hyunwoo Ko
- Model type: Auto-regressive model
- Language(s): English
- Base Model: meta-llama/Llama-2-13b-hf
- License: Non-Commercial Creative Commons license (CC BY-NC-4.0)
Training dataset
"StudentLLM/Alpagasus-2-13b-QLoRA-merged" used gpt4life's gpt-3.5-turbo filtered dataset, 'alpaca_t45.json'.
Configuration of the dataset is as follows:
{
'instruction': Give the instruction describing the question.
'input': Occasionally present, detailed instructions accompany the question if available.
'output': Give answers to questions.
}
.
.
.
Prompt Template: Alpaca style prompt
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
<prompt> (without the <>)
### Input:
<prompt> (if input exists)
### Response:
Fine-tuning Procedure
Our model was finetuned using QLoRA on single A100 80GB GPU. Training details are described in repo.
Benchmark Metrics
"StudentLLM/Alpagasus-2-13b-QLoRA-merged" model performance is uploaded on Huggingface's OpenLLM Leaderboard. Model was evaluated on the tasks specified in HF's Open LLM Leaderboard(ARC, HellaSwag, MMLU, TruthfulQA).
| Metric | Value |
|---|---|
| Avg. | 59.34 |
| MMLU | 55.27 |
| ARC | 61.09 |
| HellaSwag | 82.46 |
| TruthfulQA | 38.53 |
LLM Evaluation
We tried to follow the evaluation metric introduced by the AlpaGasus paper. During the process, we consulted the code by gpt4life. We used OpenAI's gpt-3.5-turbo as the evaluator model, and Alpaca2-LoRA-13B(it doesn't exist now...) as the comparison model. For more detailed information, please refer to our Github repo.
The evaluation result of AlpaGasus2-QLoRA is as follows:
How to use
To use "StudentLLM/Alpagasus-2-13b-QLoRA-merged", please follow the following code! The use of the 7B model is the same!
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config = PeftConfig.from_pretrained("StudentLLM/Alpagasus-2-13B-QLoRA")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf", use_auth_token="yotu_HuggingFace_token").to(device)
model = PeftModel.from_pretrained(model, "StudentLLM/Alpagasus-2-13B-QLoRA")
tokenizer = AutoTokenizer.from_pretrained("StudentLLM/Alpagasus-2-13B-QLoRA")
tokenizer.pad_token = tokenizer.eos_token
input_data = "Please tell me 3 ways to relieve stress." # You can enter any questions!!
model_inputs = tokenizer(input_data, return_tensors='pt').to(device)
model_output = model.generate(**model_inputs, max_length=256)
model_output = tokenizer.decode(model_output[0], skip_special_tokens=True)
print(model_output)
Citations
@article{chen2023alpagasus,
title={AlpaGasus: Training a Better Alpaca with Fewer Data},
author={Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin},
journal={arXiv preprint arXiv:2307.08701},
year={2023}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 45.55 |
| ARC (25-shot) | 58.28 |
| HellaSwag (10-shot) | 80.98 |
| MMLU (5-shot) | 54.14 |
| TruthfulQA (0-shot) | 34.21 |
| Winogrande (5-shot) | 75.93 |
| GSM8K (5-shot) | 9.25 |
| DROP (3-shot) | 6.07 |