metadata
license: apache-2.0
datasets:
- hkust-nlp/deita-10k-v0
language:
- en
base_model: meta-llama/Llama-2-13b-hf
Model Card for Deita Llama2 13B V1.0 SFT
Deita is an open-sourced project designed to facilitate Automatic Data Selection for instruction tuning in Large Language Models (LLMs). Deita Llama2 13B V1.0 SFT is a fine-tuned version of Llama 2 that was trained on 10k automatically selected lightweight, high-quality alignment SFT data: Deita 10K 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: meta-llama/Llama-2-13b-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-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 |
Input Format
The model is trained using the vicuna_v1.1 template
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!</s>USER: How are you? ASSISTANT:
Training hyperparameters
The following hyperparameters were used during fine tuning:
- learning_rate: 2e-05
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0