--- license: apache-2.0 datasets: - hkust-nlp/deita-6k-v0 language: - en --- Deita banner # Model Card for Deita 7B V1.0 Deita is an open-sourced project designed to facilitate **Automatic Data Selection** for instruction tuning in Large Language Models (LLMs). Deita 7B V1.0 is a fine-tuned + DPO version of Mistral-7B-v0.1 that was trained on **6K** automatically selected lightweight, high-quality alignment SFT data: [Deita 6K V0](https://huggingface.co/datasets/hkust-nlp/deita-6k-v0) and **10K** randomly sampled alignment preference data from Ultrafeedback. ## Model description - **Model type:** Model trained on automatically selected lightweight, high-quality alignment SFT data and 10K randomly sampled alignment preference data. - **Language(s) (NLP):** Primarily English - **Finetuned from model:** Mistral-7B-v0.1 ### 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
See full evaluations | 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 |
## Input Format The model is trained using the [vicuna_v1.1 template](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py) ### SFT Format ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hello! ASSISTANT: Hi!USER: How are you? ASSISTANT: ``` ### DPO Format ``` A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: ASSISTANT: ``` where \ can be a chosen answer or a rejected answer. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 128 - total_train_batch_size: 512 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 6.0 ### Framework versions - Transformers 4.34.1 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1 ## 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} } ```