--- license: apache-2.0 --- Deita banner # Model Card for Deita Complexity Scorer [GitHub](https://github.com/hkust-nlp/deita) | [Paper](https://arxiv.org/abs/2312.15685) Deita is an open-sourced project designed to facilitate **Automatic Data Selection** for instruction tuning in Large Language Models (LLMs). Deita Complexity Scorer is a tool for automatically annotating the Instruction Complexity of SFT data. ## Model description - **Model type:** Model fine tuned to automatically annotate the Instruction Complexity - **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](https://huggingface.co/collections/hkust-nlp/deita-6569c198c174808d94cf5bd4). ## 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 complexity of the Instruction: ```python from transformers import AutoTokenizer, AutoModelForCausalLM import numpy as np from scipy.special import softmax model_name = "hkust-nlp/deita-complexity-scorer" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) def infer_complexity(model, tokenizer, input_text): complexity_template = ("You are a helpful assistant. Please identify the complexity score of the following user query. \n##Query: {instruction} \n##Complexity: ") user_input = complexity_template.format(instruction=input_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 # example input input_text = "write a performance review for a junior data scientist" complexity_score = infer_complexity(model, tokenizer, input_text) print(complexity_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} } ```