Deita

🤗 HF Repo    📃 Paper

Welcome to Deita (Data-Efficient Instruction Tuning for Alignment) Project!

This is the preview version of Deita. We will continue to update, please stay tuned!

What is Deita?

Deita is an open-sourced project designed to facilitate Automatic Data Selection for instruction tuning in Large Language Models (LLMs).

It includes:

• Open-sourced Toolkits for automatic data selection in instruction tuning
• Deita Datasets: A series of extremely lightweight, high-quality alignment SFT data. We release 6k-sized and 10k-sized datasets in the first release
• Deita Models: A series of powerful models on par with SOTA chat LLMs with an extremely efficient instruction tuning Process. Deita models can be obained by training with 10x less instruction tuning data compared with other SOTA LLMs

News

• 🔥 [12/2023] We release the first collection of the Deita resources here, which include a series of extremely lightweight, effective sft datasets, the data complexity/quality scorer models, as well as the resulted deita chat models.

Performance

🔔 Still curious about how far a small amount of high-quality data can lead LLMs?

Deita may provide an answer for you:

🔦 Highlights

Model	Align	Data Size	MT-Bench	AlpacaEval(%)
Zephyr-7B-sft	SFT	200K	5.32	75.12
$\text{Zephyr-7B-}\beta$	SFT + DPO	200K SFT + 60K DPO	7.34	90.60
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
Tulu-2-13B	SFT	326K	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	7.09	89.17
Vicuna-13B-v1.5	SFT	>125K	6.57	78.80
DEITA-7B-v1.0 (6K)	SFT	6K	7.22	80.78
DEITA-7B-v1.0-sft	SFT	10K	7.32	81.67
DEITA-7B-v1.0	SFT + DPO	6K SFT + 10K DPO	7.55	90.06

DEITA models are based on Mistral-7B-v0.1. 🔥

Please refer to this table for full evaluations including Open LLM Leaderboard as well, which includes DEITA models with LLaMA base models and comparisons with other data selection approaches.

📈 Full Evaluations

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

🚀 Deita Resources

Resource	Link	License
Deita Datasets
deita-6k-v0	🤗 HF Repo	Apache-2.0
deita-10k-v0	🤗 HF Repo	Apache-2.0
Scorers
deita-complexity-scorer	🤗 HF Repo	LLaMA License
deita-quality-scorer	🤗 HF Repo	LLaMA License
Deita Models
DEITA-7B-v1.0-sft	🤗 HF Repo	Apache-2.0
DEITA-7B-v1.0	🤗 HF Repo	Apache-2.0
DEITA-LLaMA2-13B-v1.0-sft	🤗 HF Repo	LLaMA 2 License
DEITA-LLaMA1-13B-v1.0-sft	🤗 HF Repo	LLaMA License

🏃‍♂️ How to start?

Installation

  git clone https://github.com/hkust-nlp/deita.git
  cd deita
  pip install -e .

Data Sample Scoring

If you wish to assess the quality of a response for a single sample, you can follow these steps:

from deita.selection.scorer import Llama_Scorer

model_name_or_path = "hkust-nlp/deita-quality-scorer"

scorer = Llama_Scorer(model_name_or_path)

# example input
input_text = "word to describe UI with helpful tooltips" # Example Input
output_text = "User-friendly or intuitive UI" # Example Output
quality_score = scorer.infer_quality(input_text, output_text)

print(quality_score)
# 2.0230105920381902

Deita also supports VLLM for faster inference. If you want to use VLLM for inference,

pip install vllm

And set is_vllm = True when initilizing scorer

scorer = Llama_Scorer(model_name_or_path, is_vllm = True)

To assess other dimensions of data samples, please refer to the examples/scoring

SFT Training

Please refer to examples/train/sft.sh

deepspeed --include localhost:${DEVICES} --master_port 29501 src/deita/alignment/train.py \
    --model_name_or_path ${MODELPATH} \
    --data_path ${DATAPATH} \
    --output_dir ${OUTPUTPATH}/${RUNNAME} \
    --num_train_epochs 6 \
    --per_device_train_batch_size ${BSZPERDEV} \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps ${GRADACC} \
    --eval_steps 50 \
    --save_strategy "no" \
    --save_steps 100 \
    --save_total_limit 10 \
    --learning_rate 2e-5 \
    --warmup_ratio 0.1 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --do_eval False \
    --evaluation_strategy "no" \
    --model_max_length 2048 \
    --lazy_preprocess True \
    --conv_template "vicuna_v1.1" \
    --mask_user True \
    --report_to "wandb" \
    --run_name ${RUNNAME} \
    --bf16 True \
    --deepspeed src/deita/ds_configs/deepspeed_config_zero2_no_offload.json

DPO Training

Please refer to examples/train/dpo.sh

deepspeed --include localhost:${DEVICES} --master_port 29502 src/deita/alignment/dpo_train.py \
    --model_name_or_path ${MODELPATH} \
    --json_path ${JSONPATH} \
    --data_split ${DATASPLIT} \
    --output_dir ${OUTPUTPATH}/${RUNNAME} \
    --num_train_epochs ${DPOEPOCH} \
    --beta 0.1 \
    --per_device_train_batch_size ${BSZPERDEV} \
    --per_device_eval_batch_size 1 \
    --gradient_accumulation_steps ${GRADACC} \
    --save_global_steps False \
    --eval_steps 50 \
    --save_strategy "no" \
    --save_steps 500 \
    --save_total_limit 1 \
    --learning_rate 5e-7 \
    --warmup_ratio 0.1 \
    --lr_scheduler_type "linear" \
    --logging_steps 1 \
    --do_eval False \
    --evaluation_strategy "no" \
    --model_max_length 2048 \
    --conv_template "vicuna_v1.1" \
    --report_to "wandb" \
    --run_name ${RUNNAME} \
    --bf16 True \
    --gradient_checkpointing True \
    --deepspeed src/deita/ds_configs/stage3_no_offloading_accelerate.json

Evaluation

• For MT-Bench, please refer to MT-Bench
• For AlpacaEval, please refer to alpaca_eval
• For Open LLM Benchmark, please refer to lm-evaluation-harness and follow settings on HuggingFaceH4/open_llm_leaderboard

💪 What's more?

This is the preview version of Deita project. We will continue to update including

• Release data selection pipeline with efficient implementation
• More automatic data selection strategies
• CLI-Interface Supported
• Online Demo

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}
}

Acknowledgement

For training code, we use the code template of fastchat.