Adding Evaluation Results (#2)

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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.

	![img](./aegolius-acadicus.png)

	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\|