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---
language:
- en
license: llama2
tags:
- moe
- moerge
model-index:
- name: aegolius-acadicus-30b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 72.61
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-30b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 88.01
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-30b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.07
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-30b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 67.07
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-30b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 84.93
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-30b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 70.51
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/aegolius-acadicus-30b
name: Open LLM Leaderboard
---
# Aegolius Acadicus 30B
MOE 4x7b model using the Mixtral branch of the mergekit. NOT A MERGE. It is tagged as an moe and is an moe.
I like to call this model "The little professor". It is simply a MOE merge of lora merged models across Llama2 and Mistral. I am using this as a test case to move to larger models and get my gate discrimination set correctly. This model is best suited for knowledge related use cases, I did not give it a specific workload target as I did with some of the other models in the "Owl Series".
This model is merged from the following sources:
[Westlake-7B](https://huggingface.co/senseable/Westlake-7B)
[WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2)
[openchat-nectar-0.5](https://huggingface.co/andysalerno/openchat-nectar-0.5)
[WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2)
[WestSeverus-7B-DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO)
Unless those models are "contaminated" this one is not. This is a proof of concept version of this series and you can find others where I am tuning my own models and using moe mergekit to combine them to make moe models that I can run on lower tier hardware with better results.
The goal here is to create specialized models that can collaborate and run as one model.
# 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/aegolius-acadicus-30b", torch_dtype="auto", device_config='auto')
tokenizer = AutoTokenizer.from_pretrained("ibivibiv/aegolius-acadicus-30b")
inputs = tokenizer("### Instruction: Who would when in an arm wrestling match between Abraham Lincoln and Chuck Norris?\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:** **aegolius-acadicus-30b** is an auto-regressive language model moe from Llama 2 transformer architecture models and mistral models.
* **Language(s)**: English
* **Purpose**: This model is an attempt at an moe model to cover multiple disciplines using finetuned llama 2 and mistral models as base models.
# Benchmark Scores
| Test Name | Accuracy |
|------------------------------------------------------|----------------------|
| all | 0.6566791267920726 |
|arc:challenge | 0.7005119453924915 |
|hellaswag | 0.7103166699860586 |
|hendrycksTest-abstract_algebra | 0.34 |
|hendrycksTest-anatomy | 0.6666666666666666 |
|hendrycksTest-astronomy | 0.6907894736842105 |
|hendrycksTest-business_ethics | 0.65 |
|hendrycksTest-clinical_knowledge | 0.7132075471698113 |
|hendrycksTest-college_biology | 0.7708333333333334 |
|hendrycksTest-college_chemistry | 0.48 |
|hendrycksTest-college_computer_science | 0.53 |
|hendrycksTest-college_mathematics | 0.33 |
|hendrycksTest-college_medicine | 0.6705202312138728 |
|hendrycksTest-college_physics | 0.4019607843137255 |
|hendrycksTest-computer_security | 0.77 |
|hendrycksTest-conceptual_physics | 0.5787234042553191 |
|hendrycksTest-econometrics | 0.5 |
|hendrycksTest-electrical_engineering | 0.5517241379310345 |
|hendrycksTest-elementary_mathematics | 0.42592592592592593 |
|hendrycksTest-formal_logic | 0.48412698412698413 |
|hendrycksTest-global_facts | 0.37 |
|hendrycksTest-high_school_biology | 0.7806451612903226 |
|hendrycksTest-high_school_chemistry | 0.4975369458128079 |
|hendrycksTest-high_school_computer_science | 0.69 |
|hendrycksTest-high_school_european_history | 0.7757575757575758 |
|hendrycksTest-high_school_geography | 0.803030303030303 |
|hendrycksTest-high_school_government_and_politics | 0.8963730569948186 |
|hendrycksTest-high_school_macroeconomics | 0.6641025641025641 |
|hendrycksTest-high_school_mathematics | 0.36666666666666664 |
|hendrycksTest-high_school_microeconomics | 0.6890756302521008 |
|hendrycksTest-high_school_physics | 0.37748344370860926 |
|hendrycksTest-high_school_psychology | 0.8403669724770643 |
|hendrycksTest-high_school_statistics | 0.5 |
|hendrycksTest-high_school_us_history | 0.8480392156862745 |
|hendrycksTest-high_school_world_history | 0.8059071729957806 |
|hendrycksTest-human_aging | 0.6995515695067265 |
|hendrycksTest-human_sexuality | 0.7938931297709924 |
|hendrycksTest-international_law | 0.8099173553719008 |
|hendrycksTest-jurisprudence | 0.7870370370370371 |
|hendrycksTest-logical_fallacies | 0.7484662576687117 |
|hendrycksTest-machine_learning | 0.4375 |
|hendrycksTest-management | 0.7766990291262136 |
|hendrycksTest-marketing | 0.8888888888888888 |
|hendrycksTest-medical_genetics | 0.72 |
|hendrycksTest-miscellaneous | 0.8314176245210728 |
|hendrycksTest-moral_disputes | 0.7398843930635838 |
|hendrycksTest-moral_scenarios | 0.4324022346368715 |
|hendrycksTest-nutrition | 0.7189542483660131 |
|hendrycksTest-philosophy | 0.7041800643086816 |
|hendrycksTest-prehistory | 0.7469135802469136 |
|hendrycksTest-professional_accounting | 0.5035460992907801 |
|hendrycksTest-professional_law | 0.4758800521512386 |
|hendrycksTest-professional_medicine | 0.6727941176470589 |
|hendrycksTest-professional_psychology | 0.6666666666666666 |
|hendrycksTest-public_relations | 0.6727272727272727 |
|hendrycksTest-security_studies | 0.7183673469387755 |
|hendrycksTest-sociology | 0.8407960199004975 |
|hendrycksTest-us_foreign_policy | 0.85 |
|hendrycksTest-virology | 0.5542168674698795 |
|hendrycksTest-world_religions | 0.8421052631578947 |
|truthfulqa:mc | 0.6707176642401714 |
|winogrande | 0.8492501973164956 |
|gsm8k | 0.7050796057619408 |
## 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}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ibivibiv__aegolius-acadicus-30b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.70|
|AI2 Reasoning Challenge (25-Shot)|72.61|
|HellaSwag (10-Shot) |88.01|
|MMLU (5-Shot) |65.07|
|TruthfulQA (0-shot) |67.07|
|Winogrande (5-shot) |84.93|
|GSM8k (5-shot) |70.51|
|