eramax / salesforce-iterative-llama3-8b-dpo-r

https://huggingface.co/bartowski/SFR-Iterative-DPO-LLaMA-3-8B-R-GGUF

8B

186 Pulls Updated 6 weeks ago

6 weeks ago

938a6e68ce87 · 5.7GB

model
llama
·
8.03B
·
Q5_K_M
params
{"stop":["<|start_header_id|>","<|end_header_id|>","<|eot_id|>"]}
template
{{ if .System }}<|start_header_id|>system<|end_header_id|> {{ .System }}<|eot_id|>{{ end }}{{ if .Prompt }}<|start_header_id|>user<|end_header_id|> {{ .Prompt }}<|eot_id|>{{ end }}<|start_header_id|>assistant<|end_header_id|> {{ .Response }}<|eot_id|>

Readme

SFR-Iterative-DPO-Llama-3-8B-R

Introduction

We release a state-of-the-art instruct model of its class, SFR-Iterative-DPO-LLaMA-3-8B-R.
On all three widely-used instruct model benchmarks: Alpaca-Eval-V2, MT-Bench, Chat-Arena-Hard, our model outperforms all models of similar size (e.g., LLaMA-3-8B-it), most large open-sourced models (e.g., Mixtral-8x7B-it),
and strong proprietary models (e.g., GPT-3.5-turbo-0613). The model is trained with open-sourced datasets without any additional human-/GPT4-labeling.

Model Releases

Training methods

We have developed a simple and efficient online RLHF recipe for LLM instruct training. Our recipe is DPO-based and thus much cheaper and simpler to train and tune compared to PPO-based approaches.
Unlike widely-used offline DPO, the online component of our approach effectively mitigates distribution shifts during policy optimization.
For a detailed exposition, please refer to our accompanying technical report.

Chat Benchmarks

Model Size Method LC Alpaca-Eval-V2 MT-Bench Chat-Arena-Hard
Small Open-Sourced Models
Gemma-7B-it 7B SFT 10.4 6.38 7.5
Zephyr-7B-beta 7B Vanilla DPO 13.1 7.34 -
Mistral-7B-v0.2-it 7B SFT 17.1 7.51 12.6
Open-Chat-0106 7B SFT 15.6 7.8 -
Starling-7B-beta 7B PPO 25.8 8.12 23.0
LLaMA-3-8B-it 8B RS+DPO+PPO 22.9 8.16 20.6
Ours
Ours (SFT baseline) 8B SFT 10.2 7.69 5.6
Ours (DPO baseline) 8B Vanilla DPO 22.5 8.17 22.4
Ours (Online RLHF) 8B Iterative DPO 37.2 8.46 29.1
Large Open-Sourced Models
Vicuna-33b-v1.3 33B SFT 17.6 7.12 8.6
Yi-34B-Chat 34B SFT 27.2 - 23.1
Mixtral-8x7B-it 45B* SFT 23.7 8.30 23.4
Tulu-2-DPO-70B 70B Vanilla DPO 21.2 7.89 15.0
LLaMA-3-70B-it 70B RS+DPO+PPO 34.4 8.95 41.1
Mixtral-8x22B-it 141B* SFT 30.9 8.66 36.4
Proprietary Models
GPT-3.5-turbo-1106 - - 19.3 8.35 18.9
GPT-3.5-turbo-0613 - - 22.7 8.39 24.8
GPT-4-0613 - - 30.2 9.18 37.9
Claude-3-Opus - - 40.5 9.00 60.4
GPT-4 Turbo (04/09) - - 55.0 - 82.6

Academic Benchmarks

Model Size Method GSM-8K MMLU HumanEval TruthfulQA ARC MBPP
LLaMA-3-8B-it 8B RS+DPO+PPO 79.6 66.0 61.6 43.9 59.5 61.1
Ours (SFT baseline) 8B SFT 74.2 64.7 65.2 53.4 61.4 62.3
Ours (DPO baseline) 8B Vanilla DPO 79.8 64.5 63.4 61.8 65.2 60.3
Ours (Iterative RLHF) 8B Iterative DPO 80.7 65.3 64.6 60.4 64.3 60.8