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Model: RLHFlow/LLaMA3-iterative-DPO-final Source: Original Platform
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---
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license: llama3
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---
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# LLaMA3-iterative-DPO-final
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* **Paper**: [RLHF Workflow: From Reward Modeling to Online RLHF](https://arxiv.org/pdf/2405.07863) (Published in TMLR, 2024)
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* **Authors**: Hanze Dong*, Wei Xiong*, Bo Pang*, Haoxiang Wang*, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang
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* **Code**: https://github.com/RLHFlow/Online-RLHF
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## Introduction
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We release an unofficial checkpoint of a state-of-the-art instruct model of its class, **LLaMA3-iterative-DPO-final**.
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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),
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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.
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Even better, we provide a [detailed recipe](https://github.com/RLHFlow/Online-RLHF) to reproduce the model. Enjoy!
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## Model Releases
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See the [collection](https://huggingface.co/collections/RLHFlow/online-rlhf-663ae95fade1a39663dab218) of the training set, reward/preference model, SFT model.
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- [SFT model](https://huggingface.co/RLHFlow/LLaMA3-SFT)
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- [Reward model](https://huggingface.co/sfairXC/FsfairX-LLaMA3-RM-v0.1)
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- This model is more like the concise version in the report. We are still working on the model realeasing due to some license issue....
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## Dataset
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- [Preference data mix](https://huggingface.co/datasets/hendrydong/preference_700K)
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- [Prompt collection for RLHF training](https://huggingface.co/datasets/RLHFlow/prompt-collection-v0.1)
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## Training methods
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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.
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Unlike widely-used offline DPO, the online component of our approach effectively mitigates distribution shifts during policy optimization.
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For a detailed exposition, please refer to our accompanying technical report.
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## Chat Benchmarks
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| **Model** | **Size** | **Method** | **LC Alpaca-Eval-V2** | **MT-Bench** | **Chat-Arena-Hard** |
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|-------------------------|----------|-------------------|-----------------------|--------------|---------------------|
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| **Small Open-Sourced Models** | | | | | |
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| Gemma-7B-it | 7B | SFT | 10.4 | 6.38 | 7.5 |
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| Zephyr-7B-beta | 7B | Vanilla DPO | 13.1 | 7.34 | - |
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| Mistral-7B-v0.2-it | 7B | SFT | 17.1 | 7.51 | 12.6 |
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| Open-Chat-0106 | 7B | SFT | 15.6 | 7.8 | - |
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| Starling-7B-beta | 7B | PPO | 25.8 | 8.12 | 23.0 |
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| LLaMA-3-8B-it | 8B | RS+DPO+PPO | 22.9 | 8.16 | 20.6 |
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| **Ours** | | | | | |
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| Ours (SFT baseline) | 8B | SFT | 10.2 | 7.69 | 5.6 |
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| Ours (DPO baseline) | 8B | Vanilla DPO | 22.5 | 8.17 | 22.4 |
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| Ours (Online RLHF) | 8B | Iterative DPO | **37.2** | **8.46** | **29.1** |
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| **Large Open-Sourced Models** | | | | | |
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| Vicuna-33b-v1.3 | 33B | SFT | 17.6 | 7.12 | 8.6 |
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| Yi-34B-Chat | 34B | SFT | 27.2 | - | 23.1 |
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| Mixtral-8x7B-it | 45B* | SFT | 23.7 | 8.30 | 23.4 |
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| Tulu-2-DPO-70B | 70B | Vanilla DPO | 21.2 | 7.89 | 15.0 |
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| LLaMA-3-70B-it | 70B | RS+DPO+PPO | 34.4 | 8.95 | 41.1 |
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| Mixtral-8x22B-it | 141B* | SFT | 30.9 | 8.66 | 36.4 |
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| **Proprietary Models** | | | | | |
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| GPT-3.5-turbo-1106 | - | - | 19.3 | 8.35 | 18.9 |
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| GPT-3.5-turbo-0613 | - | - | 22.7 | 8.39 | 24.8 |
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| GPT-4-0613 | - | - | 30.2 | 9.18 | 37.9 |
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| Claude-3-Opus | - | - | 40.5 | 9.00 | 60.4 |
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| GPT-4 Turbo (04/09) | - | - | 55.0 | - | 82.6 |
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## Academic Benchmarks
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| **Model** | **Size** | **Method** | **GSM-8K** | **MMLU** | **HumanEval** | **TruthfulQA** | **ARC** | **MBPP** |
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|----------------------------|----------|-----------------|------------|----------|---------------|----------------|---------|----------|
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| LLaMA-3-8B-it | 8B | RS+DPO+PPO | 79.6 | 66.0 | 61.6 | 43.9 | 59.5 | 61.1 |
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| Ours (SFT baseline) | 8B | SFT | 74.2 | 64.7 | 65.2 | 53.4 | 61.4 | 62.3 |
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| Ours (DPO baseline) | 8B | Vanilla DPO | 79.8 | 64.5 | 63.4 | 61.8 | 65.2 | 60.3 |
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| Ours (Iterative RLHF) | 8B | Iterative DPO | 80.7 | 65.3 | 64.6 | 60.4 | 64.3 | 60.8 |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda"
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model = AutoModelForCausalLM.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final")
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tokenizer = AutoTokenizer.from_pretrained("RLHFlow/LLaMA3-iterative-DPO-final")
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messages = [
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{"role": "user", "content": "I'm trying to teach myself to have nicer handwriting. Can you help?"},
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]
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model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
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model_inputs = model_inputs.to(device)
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model.to(device)
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output_tokens = model.generate(model_inputs, max_new_tokens=1024, do_sample=True)
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model_outputs = tokenizer.batch_decode(output_tokens)
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print(model_outputs[0])
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```
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## Limitations
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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,
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there remains the possibility that the model could generate offensive or unethical content, particularly under adversarial conditions.
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We are committed to continuous improvement in our models to minimize such risks and encourage responsible usage.
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## Citation
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Please cite our techical report if you find our model is useful for your research or product.
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```
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@misc{dong2024rlhf,
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title={RLHF Workflow: From Reward Modeling to Online RLHF},
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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},
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year={2024},
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eprint={2405.07863},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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@misc{xiong2024iterative,
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title={Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint},
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author={Wei Xiong and Hanze Dong and Chenlu Ye and Ziqi Wang and Han Zhong and Heng Ji and Nan Jiang and Tong Zhang},
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year={2024},
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eprint={2312.11456},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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```
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config.json
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"architectures": [
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"LlamaForCausalLM"
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128009,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 8192,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.40.0.dev0",
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"use_cache": false,
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"vocab_size": 128256
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}
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||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
410562
tokenizer.json
Normal file
410562
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user