--- license: apache-2.0 datasets: - hkust-nlp/deita-6k-v0 language: - en --- Deita banner # Model Card for Deita 7B V1.0 SFT Deita is an open-sourced project designed to facilitate **Automatic Data Selection** for instruction tuning in Large Language Models (LLMs). Deita 7B V1.0 SFT (6k) is a fine-tuned 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). ## Model description - **Model type:** Model fine tuned on automatically selected lightweight, high-quality alignment SFT 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 | 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 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 | | 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 | SFT | 6K SFT | 7.22 | 80.78 | 64.94 | | DEITA-7B-v1.0-sft | 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) ``` 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: ``` ### 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