Model Card for Deita Quality Scorer
Deita is an open-sourced project designed to facilitate Automatic Data Selection for instruction tuning in Large Language Models (LLMs).
Deita Quality Scorer is a tool for automatically annotating the Instruction Quality of SFT data.
Model description
- Model type: Model fine tuned to automatically annotate the Instruction-Response Pair Quality
- Language(s) (NLP): Primarily English
- Finetuned from model: Llama-1-13b-hf
Model Sources
- Repository: https://github.com/hkust-nlp/deita
- Model Family: Other models and the dataset are found in the Deita collection.
Performance
| Model | Align | Data Size | MT-Bench | AlpacaEval(%) | OpenLLM (Avg.) |
|---|---|---|---|---|---|
| Proprietary Models | |||||
| GPT-4-Turbo | ? | -- | 9.32 | 97.70 | -- |
| GPT-4 | SFT + PPO | -- | 8.99 | 95.03 | -- |
| Claude-2 | SFT + PPO | -- | 8.06 | 91.36 | -- |
| GPT-3.5-turbo | SFT + PPO | -- | 7.94 | 89.37 | -- |
| Open-sourced Models based on LLaMA-1-13B | |||||
| LIMA | SFT | 1K SFT | 4.29 | 41.98 | 59.82 |
| WizardLM-13B | SFT | 70K SFT | 6.35 | 75.31 | 58.96 |
| Vicuna-13B-v1.3 | SFT | 125K SFT | 6.39 | 82.11 | 60.01 |
| Random | SFT | 10K SFT | 6.03 | 71.52 | 60.14 |
| DEITA-LLaMA1-13B-v1.0-sft | SFT | 10K SFT | 6.60 | 78.01 | 64.27 |
| Open-sourced Models based on LLaMA-2-13B | |||||
| Tulu-2-13B | SFT | 326K SFT | 6.70 | 78.90 | -- |
| Tulu-2-13B+DPO | SFT + DPO | 326K SFT + 60K DPO | 7.00 | 89.50 | -- |
| LLaMA2-13B-Chat | SFT + PPO | -- | 6.65 | 81.09 | -- |
| WizardLM-13B-v1.2 | SFT | >70K SFT | 7.09 | 89.17 | -- |
| Vicuna-13B-v1.5 | SFT | 125K SFT | 6.57 | 78.80 | 61.63 |
| Random | SFT | 10K SFT | 5.78 | 65.19 | 61.32 |
| DEITA-LLaMA2-13B-v1.0-sft | SFT | 10K SFT | 6.79 | 81.09 | 62.71 |
| Open-sourced Models based on Mistral-7B | |||||
| Mistral-7B-Instruct-v0.1 | -- | -- | 6.84 | 69.65 | 60.45 |
| Zephyr-7B-sft | SFT | 200K SFT | 5.32 | 75.12 | 60.93 |
| $\text{Zephyr-7B-}\beta$ | SFT + DPO | 200K SFT + 60K DPO | 7.34 | 90.60 | 66.36 |
| OpenChat-3.5 | C-RLFT | >> 70K C-RLFT | 7.81 | 88.51 | -- |
| Starling-7B | C-RLFT + APA | >>70K C-RLFT + 183K APA | 8.09 | 91.99 | -- |
| Random | SFT | 10K SFT | 5.89 | 56.90 | 61.72 |
| DEITA-7B-v1.0-sft (6K) | SFT | 6K SFT | 7.22 | 80.78 | 64.94 |
| DEITA-7B-v1.0-sft (10K) | SFT | 10K SFT | 7.32 | 81.67 | 64.00 |
| DEITA-7B-v1.0 | SFT + DPO | 6K SFT + 10K DPO | 7.55 | 90.06 | 69.86 |
Usage
Please use the following format to score the quality of Instruction-Response Pair
from transformers import AutoTokenizer, AutoModelForCausalLM
import numpy as np
from scipy.special import softmax
model_name = "hkust-nlp/deita-quality-scorer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def infer_Quality(model, tokenizer, input_text, resp_text):
quality_template = ("You are a helpful assistant. Please identify the quality score of the Response corresponding to the Question. \n #Question#:\n{instruction}\n#Response#:\n{output} \n##Quality: ")
user_input = quality_template.format(instruction=input_text, output=resp_text)
input_ids = tokenizer.encode(user_input, return_tensors="pt")
max_length = 512
outputs = model.generate(input_ids, max_length=512, num_return_sequences=1, return_dict_in_generate=True, output_scores=True)
logprobs_list = outputs.scores[0][0]
score_logits = []
id2score = {
29896: "1",
29906: "2",
29941: "3",
29946: "4",
29945: "5",
29953: "6"
}
score_template = np.array([1,2,3,4,5,6])
for k in id2score:
score_logits.append(logprobs_list[k])
score_logits = np.array(score_logits)
score_npy = softmax(score_logits, axis=0)
score_npy = score_npy * score_template
score_npy = np.sum(score_npy, axis=0)
return score_npy
input_text = "word to describe UI with helpful tooltips" # Example Input
output_text = "User-friendly or intuitive UI" # Example Output
quality_score = infer_quality(model, tokenizer, input_text)
print(quality_score)
Citation
If you find the content of this project helpful, please cite our paper as follows:
@misc{liu2023what,
title={What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning},
author={Wei Liu and Weihao Zeng and Keqing He and Yong Jiang and Junxian He},
year={2023},
eprint={2312.15685},
archivePrefix={arXiv},
primaryClass={cs.CL}
}