allenai/open-instruct-stanford-alpaca-7b

Open-Instruct Stanford Alpaca 7B

This model is a 7B LLaMa model finetuned on the Stanford Alpaca dataset. Please note this is a model diff - see below for usage instructions.

This was trained as part of the paper How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources. The codebase used to train and evaluate this model can be found at https://github.com/allenai/open-instruct.

This model is licensed under the AI model license given in LICENSE.txt along with the original Llama license (llama_license.txt).

Usage

We assume you have access to a LLaMa model in HF format already. You can find details on getting access and converting the model here: https://huggingface.co/docs/transformers/main/model_doc/llama

Clone https://github.com/allenai/open-instruct and install the required dependencies, or just copy scripts/weight_diff.py and install the minimal requirements listed in weight-diff-requirements.txt. Then download or clone this model diff to the same machine.

Then, run:

python scripts/weight_diff.py recover --path_raw ${hf_llama_path} --path_tuned ${output_path} --path_diff ${diff_location}

And you will have a recovered model! Note this takes up a decent amount of RAM, especially for the larger models.

Input Format

The model is trained to use the following format (note the newlines):

<|user|>
Your message here!
<|assistant|>

For best results, format all inputs in this manner.

Performance

Here is the performance of this model across benchmarks explored in our paper How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources:

MMLU 0-shot	MMLU 5-shot	GSM Direct	GSM CoT	BBH Direct	BBH CoT	TydiQA Gold-Passage	TydiQA Closed-book	Codex-Eval Pass@1	Codex-Eval Pass@10	AlpacaFarm vs Davinci-003	Average
41.5	40.3	7.0	10.0	32.6	31.8	31.2	7.2	13.2	22.0	21.1	23.3

If you use this model, please cite our work, the llama paper, and the original dataset:

@misc{wang2023far,
      title={How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources}, 
      author={Yizhong Wang and Hamish Ivison and Pradeep Dasigi and Jack Hessel and Tushar Khot and Khyathi Raghavi Chandu and David Wadden and Kelsey MacMillan and Noah A. Smith and Iz Beltagy and Hannaneh Hajishirzi},
      year={2023},
      eprint={2306.04751},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{touvron2023llama,
      title={LLaMA: Open and Efficient Foundation Language Models}, 
      author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie-Anne Lachaux and Timothée Lacroix and Baptiste Rozière and Naman Goyal and Eric Hambro and Faisal Azhar and Aurelien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
      year={2023},
      eprint={2302.13971},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{alpaca,
  author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
  title = {Stanford Alpaca: An Instruction-following LLaMA model},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}