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--- |
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license: other |
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language: |
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- en |
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--- |
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## Model Details |
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This is an unofficial implementation of "[AlpaGasus: Training a better Alpaca with Fewer Data.](https://github.com/Lichang-Chen/AlpaGasus)" with [LLaMA2](https://huggingface.co/meta-llama/Llama-2-13b-hf) & QLoRA! Training code is available at our [repo](https://github.com/gauss5930/AlpaGasus2-QLoRA). |
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- **Developed by:** [Yunsang Yoo](https://huggingface.co/ryan0712) and [Hyunwoo Ko](https://huggingface.co/Cartinoe5930) |
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- **Model type:** Auto-regressive model |
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- **Language(s):** English |
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- **Base Model:** [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) |
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- **License**: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)) |
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### Training dataset |
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"StudentLLM/Alpagasus-2-13b-QLoRA-merged" used [gpt4life](https://github.com/gpt4life/alpagasus)'s gpt-3.5-turbo filtered dataset, 'alpaca_t45.json'. |
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Configuration of the dataset is as follows: |
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``` |
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{ |
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'instruction': Give the instruction describing the question. |
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'input': Occasionally present, detailed instructions accompany the question if available. |
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'output': Give answers to questions. |
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} |
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. |
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. |
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. |
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``` |
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### Prompt Template: Alpaca style prompt |
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``` |
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Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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<prompt> (without the <>) |
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### Input: |
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<prompt> (if input exists) |
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### Response: |
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``` |
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### Fine-tuning Procedure |
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Our model was finetuned using QLoRA on single A100 80GB GPU. Training details are described in [repo](https://github.com/gauss5930/AlpaGasus2-QLoRA). |
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### Benchmark Metrics |
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"StudentLLM/Alpagasus-2-13b-QLoRA-merged" model performance is uploaded on Huggingface's [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). Model was evaluated on the tasks specified in HF's Open LLM Leaderboard(ARC, HellaSwag, MMLU, TruthfulQA). |
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| Metric | Value | |
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|-----------------------|-------| |
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| Avg. | 59.34 | |
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| MMLU | 55.27 | |
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| ARC | 61.09 | |
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| HellaSwag | 82.46 | |
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| TruthfulQA | 38.53 | |
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### LLM Evaluation |
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We tried to follow the evaluation metric introduced by the AlpaGasus paper. During the process, we consulted the code by [gpt4life](https://github.com/gpt4life/alpagasus). We used OpenAI's gpt-3.5-turbo as the evaluator model, and Alpaca2-LoRA-13B(it doesn't exist now...) as the comparison model. For more detailed information, please refer to our Github [repo](https://github.com/gauss5930/AlpaGasus2-QLoRA). |
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The evaluation result of AlpaGasus2-QLoRA is as follows: |
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### How to use |
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To use "StudentLLM/Alpagasus-2-13b-QLoRA-merged", please follow the following code! The use of the 7B model is the same! |
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```python |
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from peft import PeftModel, PeftConfig |
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from transformers import AutoModelForCausalLM |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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config = PeftConfig.from_pretrained("StudentLLM/Alpagasus-2-13B-QLoRA") |
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model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf", use_auth_token="yotu_HuggingFace_token").to(device) |
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model = PeftModel.from_pretrained(model, "StudentLLM/Alpagasus-2-13B-QLoRA") |
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tokenizer = AutoTokenizer.from_pretrained("StudentLLM/Alpagasus-2-13B-QLoRA") |
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tokenizer.pad_token = tokenizer.eos_token |
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input_data = "Please tell me 3 ways to relieve stress." # You can enter any questions!! |
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model_inputs = tokenizer(input_data, return_tensors='pt').to(device) |
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model_output = model.generate(**model_inputs, max_length=256) |
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model_output = tokenizer.decode(model_output[0], skip_special_tokens=True) |
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print(model_output) |
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``` |
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### Citations |
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```bibtex |
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@article{chen2023alpagasus, |
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title={AlpaGasus: Training a Better Alpaca with Fewer Data}, |
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author={Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin}, |
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journal={arXiv preprint arXiv:2307.08701}, |
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year={2023} |
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} |
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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_StudentLLM__Alpagasus-2-13B-QLoRA-pipeline) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 45.55 | |
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| ARC (25-shot) | 58.28 | |
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| HellaSwag (10-shot) | 80.98 | |
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| MMLU (5-shot) | 54.14 | |
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| TruthfulQA (0-shot) | 34.21 | |
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| Winogrande (5-shot) | 75.93 | |
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| GSM8K (5-shot) | 9.25 | |
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| DROP (3-shot) | 6.07 | |
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