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Model: umd-zhou-lab/recycled-wizardlm-7b-v2.0 Source: Original Platform
2026-08-30 03:41:16 +08:00
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+---
+license: llama2
+language:
+- en
+---
+# Model Card for umd-zhou-lab/recycled-wizardlm-7b-v2.0
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is trained by fine-tuning llama-2 with recycled WizardLM(70k) data V2.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+- **Developed by:** UMD Tianyi Zhou Lab
+- **Model type:** An auto-regressive language model based on the transformer architecture
+- **License:** Llama 2 Community License Agreement
+- **Finetuned from model:** [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b)
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **GitHub:** [Reflection-Tuning](https://github.com/tianyi-lab/Reflection_Tuning)
+- **Paper:** [Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning](https://arxiv.org/abs/2310.11716)
+- **Data:** Coming soon
+
+## Uses
+
+The primary use of this model is research on large language models and chatbots. 
+The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
+
+## Training
+
+We use the prompt from [FastChat](https://github.com/lm-sys/FastChat):
+
+```
+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>......
+```
+
+| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay | Warmup Rate |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| Recycled Models (7B) | 128 | 2e-5 | 3 | 2048 | 0 | 0.03 |
+
+## Performance
+
+The following table provides a comparison between our recycled models (V2) and baseline models on the AlpacaEval Leaderboard and Huggingface Open LLM Leaderboard. <br>
+
+The V2 Recycled Alpaca Data and WizardLM data, and the corresponding paper will be released soon. 
+
+|                          | **AlpacaEval** || **Avg** | **ARC** | **HellaSwag** | **MMLU** | **TruthfulQA** || **Model**|
+|--------------------------|:--------------:|:-:|:-----------:|:-------:|:-------------:|:-------:|:--------------:|:-:|:-:|
+| **Alpaca 7B**            | 26.46          || 50.21       | 42.65   | 76.91         | 41.73   | 39.55          ||/|
+| **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)|
+|||||||||||
+| **WizardLM 7B**          | 67.64          || 54.18       | 51.60   | 77.70         | 42.70   | 44.70          ||/|
+| **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)|
+|||||||||
+
+
+## Citation
+
+Please consider citing our paper if you think our codes, data, or models are useful. Thank you!
+```
+@misc{li2023reflectiontuning,
+      title={Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning}, 
+      author={Ming Li and Lichang Chen and Jiuhai Chen and Shwai He and Heng Huang and Jiuxiang Gu and Tianyi Zhou},
+      year={2023},
+      eprint={2310.11716},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```