File size: 10,505 Bytes
1260e0b 1d44358 58f9604 5c2c39a 58f9604 1d44358 dc19756 58f9604 a6f8c47 58f9604 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 |
---
license: llama2
language:
- en
tags:
- moe
---
# Aegolius Acadicus 30B
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}
}
``` |