--- license: apache-2.0 datasets: - fblgit/tree-of-knowledge - Open-Orca/SlimOrca-Dedup - allenai/ultrafeedback_binarized_cleaned library_name: transformers tags: - juanako - UNA - cybertron - fbl --- # Model Card for una-cybertron-14b-v2-bf16 (UNA: Uniform Neural Alignment) We strike back, introducing **Cybertron 7B v2** a 7B MistralAI based model, best on it's series. Trained on SFT, DPO and UNA (Unified Neural Alignment) on multiple datasets. He scores [EXACTLY](https://huggingface.co./datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v2-bf16) **#1** with **69.67**+ score on HF LeaderBoard board, **#8** ALL SIZES top score. * v1 Scoring **#1** at 2 December 2023 with 69.43 ..few models were releasse .. but only 1 can survive: CYBERTRON! * v2 Scoring **#1** at 5 December 2023 with 69.67 | Model | Average | ARC (25-s) | HellaSwag (10-s) | MMLU (5-s) | TruthfulQA (MC) (0-s) | Winogrande (5-s) | GSM8K (5-s) | | --- | --- | --- | --- | --- | --- | --- | --- | | [mistralai/Mistral-7B-v0.1](https://huggingface.co./mistralai/Mistral-7B-v0.1) | 60.97 | 59.98 | 83.31 | 64.16 | 42.15 | 78.37 | 37.83 | | [Intel/neural-chat-7b-v3-2](https://huggingface.co./Intel/neural-chat-7b-v3-2) | 68.29 | 67.49 | 83.92 | 63.55 | 59.68 | 79.95 | 55.12 | | [perlthoughts/Chupacabra-7B-v2](https://huggingface.co./perlthoughts/Chupacabra-7B-v2) | 63.54 | 66.47 | 85.17 | 64.49 | 57.6 | 79.16 | 28.35 | | [fblgit/una-cybertron-7b-v1-fp16](https://huggingface.co./fblgit/una-cybertron-7b-v1-fp16) | **69.49** | **68.43** | **85.85** | 63.34 | **63.28** | **80.90** | **55.12** | | [fblgit/una-cybertron-7b-v2-bf16](https://huggingface.co./fblgit/una-cybertron-7b-v2-bf16) | **69.67** | **68.26** | **85.?4** | 63.23 | **64.63** | **81.37** | **55.04** | The model excels in mathematics, logic, reasoning, overall very smart. He can make a deep reasoning over the context and prompt, it gives the impression of not missing details around. ## Model Details Adiestrated with UNA: Uniform Neural Alignment technique (paper going out soon). * What is **NOT** UNA? Its not a merged layers model. Is not SLERP or SLURP or similar. * What **is** UNA? A formula & A technique to *TAME* models * When will be released the code and paper? When have time, contribute and it'll be faster. ### Model Description - **Developed by:** [juanako.ai](https://juanako.ai) - **Author:** [Xavier M.](xavi@juanako.ai) - **Investors** [CONTACT HERE](billing@juanako.ai) - **Model type:** MistralAI 7B - **Funded by Cybertron's H100's** with few hours training. ### Prompt The model is very good, works well on almost any prompt but ChatML format and Alpaca System gets the best ``` <|im_start|>system - You are a helpful assistant chatbot trained by MosaicML. - You answer questions. - You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user. - You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|> <|im_start|>user Explain QKV<|im_end|> <|im_start|>assistant ``` ``` ### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat! ### Human: Explain QKV ### Assistant: ``` ``` [Round <|round|>] 问:Explain QKV 答: ``` ``` [Round <|round|>] Question:Explain QKV Answer: ``` ``` Question:Explain QKV Answer: ``` ### Framework versions - Transformers 4.35.0-UNA - Pytorch 2.1.0 - Datasets 2.14.6 - Tokenizers 0.14.1 ### Citations If you find Cybertron, Juanako or any of our models useful, specially if you use it for your big brand.. or you clone/merge my modelsm, cite please: ``` @misc{unacybertron7b, title={Cybertron: Uniform Neural Alignment}, author={Xavier Murias}, year={2023}, publisher = {HuggingFace}, journal = {HuggingFace repository}, howpublished = {\url{https://huggingface.co./fblgit/una-cybertron-7b-v2-bf16}}, } ``` Special thanks to @TheBloke & @bartowski for converting the models and their support to the community. Thank you!