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Apel-sin/
llama-3-8B-iterative-DPO-final-exl2

Name: llama-3-8B-iterative-DPO-final-exl2
Availability: InStock

2405.07863

2312.11456

llama3

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Exllama v2 RLHFlow/LLaMA3-iterative-DPO-final

Using turboderp's ExLlamaV2 v0.0.21 for quantization.

The "main" branch only contains the measurement.json, download one of the other branches for the model

Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.

Original model: RLHFlow/LLaMA3-iterative-DPO-final
Calibration dataset: toxic-qna

Prompt format

<|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|>
<|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>

Available sizes

Branch	Bits	lm_head bits	VRAM (4k)	VRAM (8K)	VRAM (16k)	VRAM (32k)	Description
8_0	8.0	8.0	10.1 GB	10.5 GB	11.5 GB	13.6 GB	Maximum quality that ExLlamaV2 can produce, near unquantized performance.
6_5	6.5	8.0	8.9 GB	9.3 GB	10.3 GB	12.4 GB	Very similar to 8.0, good tradeoff of size vs performance, recommended.
5_0	5.0	8.0	7.7 GB	8.1 GB	9.1 GB	11.2 GB	Slightly lower quality vs 6.5, but usable on 8GB cards.

LLaMA3-iterative-DPO-final

Introduction

We release an unofficial checkpoint of a state-of-the-art instruct model of its class, LLaMA3-iterative-DPO-final. 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.

Even better, we provide a detailed recipe to reproduce the model. Enjoy!

Model Releases

See the collection of the training set, reward/preference model, SFT model.

Dataset

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

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" 

model = AutoModelForCausalLM.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final")
tokenizer = AutoTokenizer.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final")

messages = [
    {"role": "user", "content": "I'm trying to teach myself to have nicer handwriting. Can you help?"},
]

model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = model_inputs.to(device)
model.to(device)

output_tokens = model.generate(model_inputs, max_new_tokens=1024, do_sample=True)
model_outputs = tokenizer.batch_decode(output_tokens)
print(model_outputs[0])

Limitations

RLHFlow/LLaMA3-iterative-DPO-final is an unofficial checkpoint developed to illustrate the power of online iterative RLHF and is for research purpose. While safety and ethical considerations are integral to our alignment process, there remains the possibility that the model could generate offensive or unethical content, particularly under adversarial conditions. We are committed to continuous improvement in our models to minimize such risks and encourage responsible usage.

Citation

Please cite our techical report if you find our model is useful for your research or product.

@misc{dong2024rlhf,
      title={RLHF Workflow: From Reward Modeling to Online RLHF}, 
      author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang},
      year={2024},
      eprint={2405.07863},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{xiong2024iterative,
      title={Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint}, 
      author={Wei Xiong and Hanze Dong and Chenlu Ye and Ziqi Wang and Han Zhong and Heng Ji and Nan Jiang and Tong Zhang},
      year={2024},
      eprint={2312.11456},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Apel-sin/llama-3-8B-iterative-DPO-final-exl2