Deita

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

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

🔥 [03/2024] Our datasets have been used by Huggingface to creat the Zephyr Gemma Model.
📄 [01/2024] Deita paper What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning has been accepted by ICLR2024!
🔥 [01/2024] Deita pipelines have been released! With one line code and configurations, a high-quality data subset for alignment can be selected.
📚 [01/2024] Our scorer datasets deita-complexity-scorer-data and deita-quality-scorer-data have been released.
🔥 [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	MIT License
deita-10k-v0	🤗 HF Repo	MIT License
deita-complexity-scorer-data	🤗 HF Repo	MIT License
deita-quality-scorer-data	🤗 HF Repo	MIT License
deita-redundant-pool (100K)	🤗 HF Repo	MIT License
deita-sota-pool (300K)	🤗 HF Repo	MIT License
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

Deita Pipelines

You can use deita pipelines to perform a variety of operations on the dataset with only one line code and configurations.

Dataset Scoring

from deita.pipeline import Pipeline

pipeline = Pipeline("score_pipeline", 
                    data_path = args.data_path,   # json file with sharegpt format
                    scorer = args.scorer,   # [mistral, llama]
                    scorer_name_or_path = args.scorer_name_or_path,  # scorer name or path e.g. hkust-nlp/deita-complexity-scorer
                    is_vllm = args.is_vllm,  # launch with vllm [True, False]
                    score_type = args.score_type, # [complexity, quality]
                    output_path = args.output_path)  # output path (json format)

pipeline.run()

Get Embeddings

We use Huggingface Accelerate to enhance efficiency:

from deita.pipeline import Pipeline

embed_pipeline = Pipeline("embed_pipeline", 
                          data_path = args.data_path,   # json file with sharegpt format
                          output_path = args.output_path,  # output path (pickle format)
                          model_name_or_path = args.model_name_or_path,  # model name or path e.g. mistralai/Mistral-7B-v0.1
                          max_length = args.max_length,
                          use_flash_attention = args.use_flash_attention,  
                          batch_size_per_device = args.batch_size_per_device,
                          conv_template = args.conv_template,
                          only_answer = args.only_answer,
                          random_shuffle = args.random_shuffle,
                          bfloat16 = True
                          )

embed_pipeline.run()

CUDA_VISIBLE_DEVICES=$GPUIDX accelerate launch \
    --mixed_precision bf16 \
    --num_processes $NUMPROCESS \
    --num_machines 1 \
    examples/pipelines/embed_datasets.py \
    --use_flash_attention true \
    --data_path $DATAPATH \
    --output_path $OUTPUTPATH \
    --batch_size_per_device $BSZ

Score-first, Diversity-aware Selection

from deita.pipeline import Pipeline

filter_pipeline = Pipeline("filter_pipeline", 
                          data_path = args.data_path,  # json file with sharegpt format
                          other_data_path = args.other_data_path,  # embedding file path (pickle format)
                          threshold = args.threshold,  # filter threshold default: 0.9 
                          data_size = args.data_size,  # size of selected data
                          chunk_size = args.chunk_size,  # used for more efficient GPU computing  default: 100000
                          sort_key = args.sort_key,  # default: "complexity_scores,quality_scores"
                          output_path = args.output_path,  # json format output path
                          distance_metric = args.distance_metric,  # default: cosine
                          embedding_field = args.embedding_field,  # default: embedding
                          is_compression = args.is_compression,  # default: False
                          device = args.device  # GPU IDX, default: 0
                          )

filter_pipeline.run()

You can refer to examples/pipelines for more details. A doc will also be coming soon.

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:

@inproceedings{
liu2024what,
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},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=BTKAeLqLMw}
}

Acknowledgement

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