🤗 HF Repo 📄 Paper 📚 6K Data 📚 10K Data
Welcome to Deita (Data-Efficient Instruction Tuning for Alignment) Project!
We will continue to update, please stay tuned!
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
- 🔥 [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.
🔔 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 |
| 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.
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 |
| 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 |
| 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 |
git clone https://github.com/hkust-nlp/deita.git
cd deita
pip install -e .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)
# 2.0230105920381902Deita also supports VLLM for faster inference. If you want to use VLLM for inference,
pip install vllmAnd 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
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 $BSZ- Score-first, Diversity-aware Selection
from deita.pipeline import Pipeline
filter_pipeline = Pipeline("filter_pipeline",
data_path = args.data_path, # json file with sharegpt format
other_data_path = args.other_data_path, # embedding file path (pickle format)
threshold = args.threshold, # filter threshold default: 0.9
data_size = args.data_size, # size of selected data
chunk_size = args.chunk_size, # used for more efficient GPU computing default: 100000
sort_key = args.sort_key, # default: "complexity_scores,quality_scores"
output_path = args.output_path, # json format output path
distance_metric = args.distance_metric, # default: cosine
embedding_field = args.embedding_field, # default: embedding
is_compression = args.is_compression, # default: False
device = args.device # GPU IDX, default: 0
)
filter_pipeline.run()You can refer to examples/pipelines for more details. A doc will also be coming soon.
Please refer to examples/train/sft.sh
deepspeed --include localhost:${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 ${RUNNAME} \
--bf16 True \
--deepspeed src/deita/ds_configs/deepspeed_config_zero2_no_offload.jsonPlease refer to examples/train/dpo.sh
deepspeed --include localhost:${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- 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
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
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}
}
For training code, we use the code template of fastchat.