77 lines
3.2 KiB
Markdown
77 lines
3.2 KiB
Markdown
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---
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license: llama2
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language:
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- en
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---
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# Model Card for umd-zhou-lab/recycled-wizardlm-7b-v2.0
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<!-- Provide a quick summary of what the model is/does. -->
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This model is trained by fine-tuning llama-2 with recycled WizardLM(70k) data V2.
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** UMD Tianyi Zhou Lab
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- **Model type:** An auto-regressive language model based on the transformer architecture
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- **License:** Llama 2 Community License Agreement
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- **Finetuned from model:** [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b)
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### Model Sources
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<!-- Provide the basic links for the model. -->
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- **GitHub:** [Reflection-Tuning](https://github.com/tianyi-lab/Reflection_Tuning)
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- **Paper:** [Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning](https://arxiv.org/abs/2310.11716)
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- **Data:** Coming soon
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## Uses
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The primary use of this model is research on large language models and chatbots.
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The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
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## Training
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We use the prompt from [FastChat](https://github.com/lm-sys/FastChat):
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```
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A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>USER: Who are you? ASSISTANT: I am ...</s>......
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```
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| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay | Warmup Rate |
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| --- | ---: | ---: | ---: | ---: | ---: | ---: |
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| Recycled Models (7B) | 128 | 2e-5 | 3 | 2048 | 0 | 0.03 |
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## Performance
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The following table provides a comparison between our recycled models (V2) and baseline models on the AlpacaEval Leaderboard and Huggingface Open LLM Leaderboard. <br>
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The V2 Recycled Alpaca Data and WizardLM data, and the corresponding paper will be released soon.
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| | **AlpacaEval** || **Avg** | **ARC** | **HellaSwag** | **MMLU** | **TruthfulQA** || **Model**|
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|--------------------------|:--------------:|:-:|:-----------:|:-------:|:-------------:|:-------:|:--------------:|:-:|:-:|
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| **Alpaca 7B** | 26.46 || 50.21 | 42.65 | 76.91 | 41.73 | 39.55 ||/|
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| **Recycled Alpaca 7B V2.0** | 79.58 || 56.05 | 54.01 | 78.07 | 46.69 | 45.41 ||[[hf-Link]](https://huggingface.co/umd-zhou-lab/recycled-alpaca-7b-v2.0)|
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| **WizardLM 7B** | 67.64 || 54.18 | 51.60 | 77.70 | 42.70 | 44.70 ||/|
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| **Recycled WizardLM 7B V2.0** | 83.48 || 56.79 | 54.78 | 77.86 | 45.63 | 48.91 ||[[hf-Link]](https://huggingface.co/umd-zhou-lab/recycled-wizardlm-7b-v2.0)|
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## Citation
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Please consider citing our paper if you think our codes, data, or models are useful. Thank you!
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```
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@misc{li2023reflectiontuning,
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title={Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning},
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author={Ming Li and Lichang Chen and Jiuhai Chen and Shwai He and Heng Huang and Jiuxiang Gu and Tianyi Zhou},
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year={2023},
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eprint={2310.11716},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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