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athene-noctua-13b - GGUF
- Model creator: https://huggingface.co/ibivibiv/
- Original model: https://huggingface.co/ibivibiv/athene-noctua-13b/
| Name | Quant method | Size |
|---|---|---|
| athene-noctua-13b.Q2_K.gguf | Q2_K | 4.52GB |
| athene-noctua-13b.IQ3_XS.gguf | IQ3_XS | 4.99GB |
| athene-noctua-13b.IQ3_S.gguf | IQ3_S | 5.27GB |
| athene-noctua-13b.Q3_K_S.gguf | Q3_K_S | 5.27GB |
| athene-noctua-13b.IQ3_M.gguf | IQ3_M | 5.57GB |
| athene-noctua-13b.Q3_K.gguf | Q3_K | 5.9GB |
| athene-noctua-13b.Q3_K_M.gguf | Q3_K_M | 5.9GB |
| athene-noctua-13b.Q3_K_L.gguf | Q3_K_L | 6.45GB |
| athene-noctua-13b.IQ4_XS.gguf | IQ4_XS | 6.54GB |
| athene-noctua-13b.Q4_0.gguf | Q4_0 | 6.86GB |
| athene-noctua-13b.IQ4_NL.gguf | IQ4_NL | 6.9GB |
| athene-noctua-13b.Q4_K_S.gguf | Q4_K_S | 6.91GB |
| athene-noctua-13b.Q4_K.gguf | Q4_K | 7.33GB |
| athene-noctua-13b.Q4_K_M.gguf | Q4_K_M | 7.33GB |
| athene-noctua-13b.Q4_1.gguf | Q4_1 | 7.61GB |
| athene-noctua-13b.Q5_0.gguf | Q5_0 | 8.36GB |
| athene-noctua-13b.Q5_K_S.gguf | Q5_K_S | 8.36GB |
| athene-noctua-13b.Q5_K.gguf | Q5_K | 8.6GB |
| athene-noctua-13b.Q5_K_M.gguf | Q5_K_M | 8.6GB |
| athene-noctua-13b.Q5_1.gguf | Q5_1 | 9.1GB |
| athene-noctua-13b.Q6_K.gguf | Q6_K | 9.95GB |
| athene-noctua-13b.Q8_0.gguf | Q8_0 | 12.88GB |
Original model description:
language: - en license: llama2 tags: - logic - reasoning model-index: - name: athene-noctua-13b 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: 57.17 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b 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: 81.52 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b 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: 55.91 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b 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: 47.49 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b 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: 73.4 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b 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: 15.31 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b name: Open LLM Leaderboard
Athene Noctua 13B
Model Details
- Trained by: ibivibiv
- Library: HuggingFace Transformers
- Model type: athene-noctua-13b is an auto-regressive language model fine tuned on the Llama 2 transformer architecture.
- Language(s): English
- Purpose: Has specific training for logic enforcement, will do well in ARC or other logic testing as well as critical thinking tasks. This model is targeted towards planning exercises.
- Comments: This little guy does pretty well in my logic puzzle testing for a 13B model. I've been using it for test runs to prime for larger models, but it is worth uploading now as it is doing very well on the tests. Again, this a 13B model so tricky logic does still trip it up but for its size it is doing well.
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/athene-noctua-13b", torch_dtype="auto", device_config='auto')
tokenizer = AutoTokenizer.from_pretrained("ibivibiv/athene-noctua-13b")
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)
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. | 55.13 |
| AI2 Reasoning Challenge (25-Shot) | 57.17 |
| HellaSwag (10-Shot) | 81.52 |
| MMLU (5-Shot) | 55.91 |
| TruthfulQA (0-shot) | 47.49 |
| Winogrande (5-shot) | 73.40 |
| GSM8k (5-shot) | 15.31 |
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