ibivibiv/bubo-bubo-13b

Bubo Bubo 13B

Prompting

Prompt Template for alpaca style

### Instruction:

<prompt> (without the <>)

### Response:

Sample Code

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

torch.set_default_device("cuda")

model = AutoModelForCausalLM.from_pretrained("ibivibiv/bubo-bubo-13b", torch_dtype="auto", device_config='auto')
tokenizer = AutoTokenizer.from_pretrained("ibivibiv/bubo-bubo-13b")

inputs = tokenizer("### Instruction: Summarize this email chain :  <email chain stuff here>.\n### Response:\n", return_tensors="pt", return_attention_mask=False)

outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)

Model Details

• Trained by: ibivibiv
• Library: HuggingFace Transformers
• Model type: bubo-bubo-13b is an auto-regressive language model fine tuned on the Llama 2 transformer architecture.
• Language(s): English
• Purpose: Has specific training for summary tasks. This model is targeted towards summarizing communication chains specifically.

Benchmark Scores

I ran the benchmark harness, for curiousity, but this model is completely geared towards summarizing.

Test Name	Accuracy
all	0.579149139810157
arc:challenge	0.5631399317406144
hellaswag	0.6317466640111532
hendrycksTest-abstract_algebra	0.32
hendrycksTest-anatomy	0.5481481481481482
hendrycksTest-astronomy	0.5657894736842105
hendrycksTest-business_ethics	0.55
hendrycksTest-clinical_knowledge	0.6
hendrycksTest-college_biology	0.6388888888888888
hendrycksTest-college_chemistry	0.38
hendrycksTest-college_computer_science	0.43
hendrycksTest-college_mathematics	0.34
hendrycksTest-college_medicine	0.5260115606936416
hendrycksTest-college_physics	0.3431372549019608
hendrycksTest-computer_security	0.71
hendrycksTest-conceptual_physics	0.49361702127659574
hendrycksTest-econometrics	0.35964912280701755
hendrycksTest-electrical_engineering	0.5586206896551724
hendrycksTest-elementary_mathematics	0.3439153439153439
hendrycksTest-formal_logic	0.3333333333333333
hendrycksTest-global_facts	0.42
hendrycksTest-high_school_biology	0.6903225806451613
hendrycksTest-high_school_chemistry	0.45320197044334976
hendrycksTest-high_school_computer_science	0.58
hendrycksTest-high_school_european_history	0.6787878787878788
hendrycksTest-high_school_geography	0.7424242424242424
hendrycksTest-high_school_government_and_politics	0.8341968911917098
hendrycksTest-high_school_macroeconomics	0.558974358974359
hendrycksTest-high_school_mathematics	0.3
hendrycksTest-high_school_microeconomics	0.5672268907563025
hendrycksTest-high_school_physics	0.33112582781456956
hendrycksTest-high_school_psychology	0.7577981651376147
hendrycksTest-high_school_statistics	0.4212962962962963
hendrycksTest-high_school_us_history	0.8186274509803921
hendrycksTest-high_school_world_history	0.759493670886076
hendrycksTest-human_aging	0.6547085201793722
hendrycksTest-human_sexuality	0.6412213740458015
hendrycksTest-international_law	0.6776859504132231
hendrycksTest-jurisprudence	0.75
hendrycksTest-logical_fallacies	0.6993865030674846
hendrycksTest-machine_learning	0.41964285714285715
hendrycksTest-management	0.7281553398058253
hendrycksTest-marketing	0.8504273504273504
hendrycksTest-medical_genetics	0.6
hendrycksTest-miscellaneous	0.7624521072796935
hendrycksTest-moral_disputes	0.6560693641618497
hendrycksTest-moral_scenarios	0.4346368715083799
hendrycksTest-nutrition	0.673202614379085
hendrycksTest-philosophy	0.7009646302250804
hendrycksTest-prehistory	0.7067901234567902
hendrycksTest-professional_accounting	0.4645390070921986
hendrycksTest-professional_law	0.45697522816166886
hendrycksTest-professional_medicine	0.5514705882352942
hendrycksTest-professional_psychology	0.6013071895424836
hendrycksTest-public_relations	0.6636363636363637
hendrycksTest-security_studies	0.6448979591836734
hendrycksTest-sociology	0.7611940298507462
hendrycksTest-us_foreign_policy	0.84
hendrycksTest-virology	0.4819277108433735
hendrycksTest-world_religions	0.7894736842105263
truthfulqa:mc	0.4762440289139372
winogrande	0.7616416732438832
gsm8k	0.20621683093252463

Citations

@misc{open-llm-leaderboard,
  author = {Edward Beeching and Clémentine Fourrier and Nathan Habib and Sheon Han and Nathan Lambert and Nazneen Rajani and Omar Sanseviero and Lewis Tunstall and Thomas Wolf},
  title = {Open LLM Leaderboard},
  year = {2023},
  publisher = {Hugging Face},
  howpublished = "\url{https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard}"
}

@software{eval-harness,
  author       = {Gao, Leo and
                  Tow, Jonathan and
                  Biderman, Stella and
                  Black, Sid and
                  DiPofi, Anthony and
                  Foster, Charles and
                  Golding, Laurence and
                  Hsu, Jeffrey and
                  McDonell, Kyle and
                  Muennighoff, Niklas and
                  Phang, Jason and
                  Reynolds, Laria and
                  Tang, Eric and
                  Thite, Anish and
                  Wang, Ben and
                  Wang, Kevin and
                  Zou, Andy},
  title        = {A framework for few-shot language model evaluation},
  month        = sep,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v0.0.1},
  doi          = {10.5281/zenodo.5371628},
  url          = {https://doi.org/10.5281/zenodo.5371628}
}

@misc{clark2018think,
      title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
      author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
      year={2018},
      eprint={1803.05457},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

@misc{zellers2019hellaswag,
      title={HellaSwag: Can a Machine Really Finish Your Sentence?},
      author={Rowan Zellers and Ari Holtzman and Yonatan Bisk and Ali Farhadi and Yejin Choi},
      year={2019},
      eprint={1905.07830},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{hendrycks2021measuring,
      title={Measuring Massive Multitask Language Understanding},
      author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
      year={2021},
      eprint={2009.03300},
      archivePrefix={arXiv},
      primaryClass={cs.CY}
}

@misc{lin2022truthfulqa,
      title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
      author={Stephanie Lin and Jacob Hilton and Owain Evans},
      year={2022},
      eprint={2109.07958},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{DBLP:journals/corr/abs-1907-10641,
      title={{WINOGRANDE:} An Adversarial Winograd Schema Challenge at Scale},
      author={Keisuke Sakaguchi and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi},
      year={2019},
      eprint={1907.10641},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{DBLP:journals/corr/abs-2110-14168,
      title={Training Verifiers to Solve Math Word Problems},
      author={Karl Cobbe and
                  Vineet Kosaraju and
                  Mohammad Bavarian and
                  Mark Chen and
                  Heewoo Jun and
                  Lukasz Kaiser and
                  Matthias Plappert and
                  Jerry Tworek and
                  Jacob Hilton and
                  Reiichiro Nakano and
                  Christopher Hesse and
                  John Schulman},
      year={2021},
      eprint={2110.14168},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}