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---
license: llama2
language:
- en
tags:
- summary
---
# Bubo Bubo 13B
# Prompting
## Prompt Template for alpaca style
```
### Instruction:
<prompt> (without the <>)
### Response:
```
## Sample Code
```python
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](https://huggingface.co/ibivibiv)
* **Library**: [HuggingFace Transformers](https://github.com/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}
}
```
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