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--- |
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license: llama2 |
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language: |
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- en |
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tags: |
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- moe |
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--- |
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# Aegolius Acadicus 30B |
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MOE 4x7b model using the Mixtral branch of the mergekit. NOT A MERGE. It is tagged as an moe and is an moe. |
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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". |
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This model is merged from the following sources: |
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[Westlake-7B](https://huggingface.co/senseable/Westlake-7B) |
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[WestLake-7B-v2](https://huggingface.co/senseable/WestLake-7B-v2) |
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[openchat-nectar-0.5](https://huggingface.co/andysalerno/openchat-nectar-0.5) |
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[WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2) |
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[WestSeverus-7B-DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO) |
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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. |
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The goal here is to create specialized models that can collaborate and run as one model. |
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# Prompting |
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## Prompt Template for alpaca style |
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``` |
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### Instruction: |
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<prompt> (without the <>) |
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### Response: |
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``` |
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## Sample Code |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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torch.set_default_device("cuda") |
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model = AutoModelForCausalLM.from_pretrained("ibivibiv/aegolius-acadicus-30b", torch_dtype="auto", device_config='auto') |
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tokenizer = AutoTokenizer.from_pretrained("ibivibiv/aegolius-acadicus-30b") |
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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) |
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outputs = model.generate(**inputs, max_length=200) |
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text = tokenizer.batch_decode(outputs)[0] |
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print(text) |
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``` |
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# Model Details |
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* **Trained by**: [ibivibiv](https://huggingface.co/ibivibiv) |
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* **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers) |
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* **Model type:** **aegolius-acadicus-30b** is an auto-regressive language model moe from Llama 2 transformer architecture models and mistral models. |
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* **Language(s)**: English |
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* **Purpose**: This model is an attempt at an moe model to cover multiple disciplines using finetuned llama 2 and mistral models as base models. |
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# Benchmark Scores |
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| Test Name | Accuracy | |
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|------------------------------------------------------|----------------------| |
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| all | 0.6566791267920726 | |
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|arc:challenge | 0.7005119453924915 | |
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|hellaswag | 0.7103166699860586 | |
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|hendrycksTest-abstract_algebra | 0.34 | |
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|hendrycksTest-anatomy | 0.6666666666666666 | |
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|hendrycksTest-astronomy | 0.6907894736842105 | |
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|hendrycksTest-business_ethics | 0.65 | |
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|hendrycksTest-clinical_knowledge | 0.7132075471698113 | |
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|hendrycksTest-college_biology | 0.7708333333333334 | |
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|hendrycksTest-college_chemistry | 0.48 | |
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|hendrycksTest-college_computer_science | 0.53 | |
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|hendrycksTest-college_mathematics | 0.33 | |
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|hendrycksTest-college_medicine | 0.6705202312138728 | |
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|hendrycksTest-college_physics | 0.4019607843137255 | |
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|hendrycksTest-computer_security | 0.77 | |
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|hendrycksTest-conceptual_physics | 0.5787234042553191 | |
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|hendrycksTest-econometrics | 0.5 | |
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|hendrycksTest-electrical_engineering | 0.5517241379310345 | |
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|hendrycksTest-elementary_mathematics | 0.42592592592592593 | |
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|hendrycksTest-formal_logic | 0.48412698412698413 | |
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|hendrycksTest-global_facts | 0.37 | |
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|hendrycksTest-high_school_biology | 0.7806451612903226 | |
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|hendrycksTest-high_school_chemistry | 0.4975369458128079 | |
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|hendrycksTest-high_school_computer_science | 0.69 | |
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|hendrycksTest-high_school_european_history | 0.7757575757575758 | |
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|hendrycksTest-high_school_geography | 0.803030303030303 | |
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|hendrycksTest-high_school_government_and_politics | 0.8963730569948186 | |
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|hendrycksTest-high_school_macroeconomics | 0.6641025641025641 | |
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|hendrycksTest-high_school_mathematics | 0.36666666666666664 | |
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|hendrycksTest-high_school_microeconomics | 0.6890756302521008 | |
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|hendrycksTest-high_school_physics | 0.37748344370860926 | |
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|hendrycksTest-high_school_psychology | 0.8403669724770643 | |
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|hendrycksTest-high_school_statistics | 0.5 | |
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|hendrycksTest-high_school_us_history | 0.8480392156862745 | |
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|hendrycksTest-high_school_world_history | 0.8059071729957806 | |
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|hendrycksTest-human_aging | 0.6995515695067265 | |
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|hendrycksTest-human_sexuality | 0.7938931297709924 | |
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|hendrycksTest-international_law | 0.8099173553719008 | |
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|hendrycksTest-jurisprudence | 0.7870370370370371 | |
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|hendrycksTest-logical_fallacies | 0.7484662576687117 | |
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|hendrycksTest-machine_learning | 0.4375 | |
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|hendrycksTest-management | 0.7766990291262136 | |
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|hendrycksTest-marketing | 0.8888888888888888 | |
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|hendrycksTest-medical_genetics | 0.72 | |
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|hendrycksTest-miscellaneous | 0.8314176245210728 | |
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|hendrycksTest-moral_disputes | 0.7398843930635838 | |
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|hendrycksTest-moral_scenarios | 0.4324022346368715 | |
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|hendrycksTest-nutrition | 0.7189542483660131 | |
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|hendrycksTest-philosophy | 0.7041800643086816 | |
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|hendrycksTest-prehistory | 0.7469135802469136 | |
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|hendrycksTest-professional_accounting | 0.5035460992907801 | |
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|hendrycksTest-professional_law | 0.4758800521512386 | |
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|hendrycksTest-professional_medicine | 0.6727941176470589 | |
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|hendrycksTest-professional_psychology | 0.6666666666666666 | |
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|hendrycksTest-public_relations | 0.6727272727272727 | |
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|hendrycksTest-security_studies | 0.7183673469387755 | |
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|hendrycksTest-sociology | 0.8407960199004975 | |
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|hendrycksTest-us_foreign_policy | 0.85 | |
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|hendrycksTest-virology | 0.5542168674698795 | |
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|hendrycksTest-world_religions | 0.8421052631578947 | |
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|truthfulqa:mc | 0.6707176642401714 | |
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|winogrande | 0.8492501973164956 | |
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|gsm8k | 0.7050796057619408 | |
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## Citations |
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|
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``` |
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@misc{open-llm-leaderboard, |
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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}, |
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title = {Open LLM Leaderboard}, |
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year = {2023}, |
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publisher = {Hugging Face}, |
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howpublished = "\url{https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard}" |
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} |
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``` |
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``` |
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@software{eval-harness, |
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author = {Gao, Leo and |
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Tow, Jonathan and |
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Biderman, Stella and |
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Black, Sid and |
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DiPofi, Anthony and |
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Foster, Charles and |
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Golding, Laurence and |
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Hsu, Jeffrey and |
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McDonell, Kyle and |
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Muennighoff, Niklas and |
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Phang, Jason and |
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Reynolds, Laria and |
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Tang, Eric and |
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Thite, Anish and |
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Wang, Ben and |
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Wang, Kevin and |
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Zou, Andy}, |
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title = {A framework for few-shot language model evaluation}, |
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month = sep, |
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year = 2021, |
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publisher = {Zenodo}, |
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version = {v0.0.1}, |
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doi = {10.5281/zenodo.5371628}, |
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url = {https://doi.org/10.5281/zenodo.5371628} |
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} |
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``` |
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``` |
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@misc{clark2018think, |
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title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge}, |
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author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord}, |
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year={2018}, |
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eprint={1803.05457}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.AI} |
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} |
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``` |
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``` |
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@misc{zellers2019hellaswag, |
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title={HellaSwag: Can a Machine Really Finish Your Sentence?}, |
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author={Rowan Zellers and Ari Holtzman and Yonatan Bisk and Ali Farhadi and Yejin Choi}, |
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year={2019}, |
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eprint={1905.07830}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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``` |
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@misc{hendrycks2021measuring, |
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title={Measuring Massive Multitask Language Understanding}, |
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author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt}, |
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year={2021}, |
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eprint={2009.03300}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CY} |
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} |
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``` |
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``` |
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@misc{lin2022truthfulqa, |
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title={TruthfulQA: Measuring How Models Mimic Human Falsehoods}, |
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author={Stephanie Lin and Jacob Hilton and Owain Evans}, |
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year={2022}, |
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eprint={2109.07958}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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``` |
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@misc{DBLP:journals/corr/abs-1907-10641, |
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title={{WINOGRANDE:} An Adversarial Winograd Schema Challenge at Scale}, |
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author={Keisuke Sakaguchi and Ronan Le Bras and Chandra Bhagavatula and Yejin Choi}, |
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year={2019}, |
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eprint={1907.10641}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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``` |
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@misc{DBLP:journals/corr/abs-2110-14168, |
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title={Training Verifiers to Solve Math Word Problems}, |
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author={Karl Cobbe and |
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Vineet Kosaraju and |
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Mohammad Bavarian and |
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Mark Chen and |
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Heewoo Jun and |
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Lukasz Kaiser and |
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Matthias Plappert and |
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Jerry Tworek and |
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Jacob Hilton and |
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Reiichiro Nakano and |
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Christopher Hesse and |
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John Schulman}, |
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year={2021}, |
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eprint={2110.14168}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
