metadata
language:
- en
license: llama2
tags:
- logic
- planning
model-index:
- name: strix-rufipes-70b
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: 71.33
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/strix-rufipes-70b
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: 87.86
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/strix-rufipes-70b
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: 69.13
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/strix-rufipes-70b
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: 56.72
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/strix-rufipes-70b
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: 84.77
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/strix-rufipes-70b
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: 53.83
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/strix-rufipes-70b
name: Open LLM Leaderboard
Strix Rufipes 70B
Prompting
Prompt Template for alpaca style
### Instruction:
<prompt> (without the <>)
### Response:
Sample Code
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("ibivibiv/strix-rufipes-70b", torch_dtype="auto", device_config='auto')
tokenizer = AutoTokenizer.from_pretrained("ibivibiv/strix-rufipes-70b")
inputs = tokenizer("### Instruction: Create a plan for developing the game of snake in python using pygame.\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
- Library: HuggingFace Transformers
- Model type: strix-rufipes-70b is an auto-regressive language model fine tuned on the Llama 2 transformer architecture.
- Language(s): English
- Purpose: Has specific training for logic enforcement. This model is targeted towards planning exercises.
Benchmark Scores
| Test Name | Accuracy |
|---|---|
| average of all | 0.6910894247381432 |
| arc:challenge | 0.674061433447099 |
| hellaswag | 0.6898028281218881 |
| hendrycksTest-abstract_algebra | 0.36 |
| hendrycksTest-anatomy | 0.6370370370370371 |
| hendrycksTest-astronomy | 0.7960526315789473 |
| hendrycksTest-business_ethics | 0.73 |
| hendrycksTest-clinical_knowledge | 0.7169811320754716 |
| hendrycksTest-college_biology | 0.8125 |
| hendrycksTest-college_chemistry | 0.47 |
| hendrycksTest-college_computer_science | 0.56 |
| hendrycksTest-college_mathematics | 0.36 |
| hendrycksTest-college_medicine | 0.6820809248554913 |
| hendrycksTest-college_physics | 0.43137254901960786 |
| hendrycksTest-computer_security | 0.75 |
| hendrycksTest-conceptual_physics | 0.6851063829787234 |
| hendrycksTest-econometrics | 0.4824561403508772 |
| hendrycksTest-electrical_engineering | 0.5793103448275863 |
| hendrycksTest-elementary_mathematics | 0.41534391534391535 |
| hendrycksTest-formal_logic | 0.48412698412698413 |
| hendrycksTest-global_facts | 0.5 |
| hendrycksTest-high_school_biology | 0.8064516129032258 |
| hendrycksTest-high_school_chemistry | 0.5073891625615764 |
| hendrycksTest-high_school_computer_science | 0.71 |
| hendrycksTest-high_school_european_history | 0.8424242424242424 |
| hendrycksTest-high_school_geography | 0.8787878787878788 |
| hendrycksTest-high_school_government_and_politics | 0.9326424870466321 |
| hendrycksTest-high_school_macroeconomics | 0.717948717948718 |
| hendrycksTest-high_school_mathematics | 0.2962962962962963 |
| hendrycksTest-high_school_microeconomics | 0.7521008403361344 |
| hendrycksTest-high_school_physics | 0.48344370860927155 |
| hendrycksTest-high_school_psychology | 0.8788990825688073 |
| hendrycksTest-high_school_statistics | 0.5277777777777778 |
| hendrycksTest-high_school_us_history | 0.9019607843137255 |
| hendrycksTest-high_school_world_history | 0.8776371308016878 |
| hendrycksTest-human_aging | 0.7802690582959642 |
| hendrycksTest-human_sexuality | 0.8244274809160306 |
| hendrycksTest-international_law | 0.8677685950413223 |
| hendrycksTest-jurisprudence | 0.8148148148148148 |
| hendrycksTest-logical_fallacies | 0.7914110429447853 |
| hendrycksTest-machine_learning | 0.5357142857142857 |
| hendrycksTest-management | 0.8543689320388349 |
| hendrycksTest-marketing | 0.8974358974358975 |
| hendrycksTest-medical_genetics | 0.73 |
| hendrycksTest-miscellaneous | 0.8569604086845466 |
| hendrycksTest-moral_disputes | 0.7687861271676301 |
| hendrycksTest-moral_scenarios | 0.5184357541899441 |
| hendrycksTest-nutrition | 0.7679738562091504 |
| hendrycksTest-philosophy | 0.7620578778135049 |
| hendrycksTest-prehistory | 0.8271604938271605 |
| hendrycksTest-professional_accounting | 0.5390070921985816 |
| hendrycksTest-professional_law | 0.5743155149934811 |
| hendrycksTest-professional_medicine | 0.6911764705882353 |
| hendrycksTest-professional_psychology | 0.7565359477124183 |
| hendrycksTest-public_relations | 0.7272727272727273 |
| hendrycksTest-security_studies | 0.8 |
| hendrycksTest-sociology | 0.8507462686567164 |
| hendrycksTest-us_foreign_policy | 0.89 |
| hendrycksTest-virology | 0.5542168674698795 |
| hendrycksTest-world_religions | 0.8596491228070176 |
| truthfulqa | 0.4712300987333333 |
| winogrande | 0.8476716653512234 |
| gsm8k | 0.5382865807429871 |
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
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 70.61 |
| AI2 Reasoning Challenge (25-Shot) | 71.33 |
| HellaSwag (10-Shot) | 87.86 |
| MMLU (5-Shot) | 69.13 |
| TruthfulQA (0-shot) | 56.72 |
| Winogrande (5-shot) | 84.77 |
| GSM8k (5-shot) | 53.83 |