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--- |
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language: |
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- en |
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license: llama2 |
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tags: |
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- logic |
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- reasoning |
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model-index: |
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- name: athene-noctua-13b |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 57.17 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 81.52 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 55.91 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 47.49 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 73.4 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 15.31 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ibivibiv/athene-noctua-13b |
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name: Open LLM Leaderboard |
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--- |
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# Athene Noctua 13B |
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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:** **athene-noctua-13b** is an auto-regressive language model fine tuned on the Llama 2 transformer architecture. |
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* **Language(s)**: English |
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* **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. |
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* **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. |
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|
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# Prompting |
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|
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## Prompt Template for alpaca style |
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|
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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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|
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## Sample Code |
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|
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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/athene-noctua-13b", torch_dtype="auto", device_config='auto') |
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tokenizer = AutoTokenizer.from_pretrained("ibivibiv/athene-noctua-13b") |
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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) |
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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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|
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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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@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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@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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@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}, |
|
year={2019}, |
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eprint={1907.10641}, |
|
archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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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 |
|
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} |
|
} |
|
``` |
|
|
|
|
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ibivibiv__athene-noctua-13b) |
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|
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |55.13| |
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|AI2 Reasoning Challenge (25-Shot)|57.17| |
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|HellaSwag (10-Shot) |81.52| |
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|MMLU (5-Shot) |55.91| |
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|TruthfulQA (0-shot) |47.49| |
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|Winogrande (5-shot) |73.40| |
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|GSM8k (5-shot) |15.31| |
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|
