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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+aegolius-acadicus-v1-30b - GGUF
+- Model creator: https://huggingface.co/ibivibiv/
+- Original model: https://huggingface.co/ibivibiv/aegolius-acadicus-v1-30b/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [aegolius-acadicus-v1-30b.Q2_K.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q2_K.gguf) | Q2_K | 10.14GB |
+| [aegolius-acadicus-v1-30b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.IQ3_XS.gguf) | IQ3_XS | 11.35GB |
+| [aegolius-acadicus-v1-30b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.IQ3_S.gguf) | IQ3_S | 11.99GB |
+| [aegolius-acadicus-v1-30b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q3_K_S.gguf) | Q3_K_S | 11.97GB |
+| [aegolius-acadicus-v1-30b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.IQ3_M.gguf) | IQ3_M | 12.2GB |
+| [aegolius-acadicus-v1-30b.Q3_K.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q3_K.gguf) | Q3_K | 13.29GB |
+| [aegolius-acadicus-v1-30b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q3_K_M.gguf) | Q3_K_M | 13.29GB |
+| [aegolius-acadicus-v1-30b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q3_K_L.gguf) | Q3_K_L | 14.39GB |
+| [aegolius-acadicus-v1-30b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.IQ4_XS.gguf) | IQ4_XS | 14.97GB |
+| [aegolius-acadicus-v1-30b.Q4_0.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q4_0.gguf) | Q4_0 | 15.64GB |
+| [aegolius-acadicus-v1-30b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.IQ4_NL.gguf) | IQ4_NL | 15.79GB |
+| [aegolius-acadicus-v1-30b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q4_K_S.gguf) | Q4_K_S | 15.78GB |
+| [aegolius-acadicus-v1-30b.Q4_K.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q4_K.gguf) | Q4_K | 16.79GB |
+| [aegolius-acadicus-v1-30b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q4_K_M.gguf) | Q4_K_M | 16.79GB |
+| [aegolius-acadicus-v1-30b.Q4_1.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q4_1.gguf) | Q4_1 | 17.37GB |
+| [aegolius-acadicus-v1-30b.Q5_0.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q5_0.gguf) | Q5_0 | 19.09GB |
+| [aegolius-acadicus-v1-30b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q5_K_S.gguf) | Q5_K_S | 19.09GB |
+| [aegolius-acadicus-v1-30b.Q5_K.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q5_K.gguf) | Q5_K | 19.68GB |
+| [aegolius-acadicus-v1-30b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q5_K_M.gguf) | Q5_K_M | 19.68GB |
+| [aegolius-acadicus-v1-30b.Q5_1.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q5_1.gguf) | Q5_1 | 20.82GB |
+| [aegolius-acadicus-v1-30b.Q6_K.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q6_K.gguf) | Q6_K | 22.76GB |
+| [aegolius-acadicus-v1-30b.Q8_0.gguf](https://huggingface.co/RichardErkhov/ibivibiv_-_aegolius-acadicus-v1-30b-gguf/blob/main/aegolius-acadicus-v1-30b.Q8_0.gguf) | Q8_0 | 29.48GB |
+
+
+
+
+Original model description:
+---
+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|
+
+
+