--- 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: (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|