Edit model card

Model Card for una-cybertron-7b-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 #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 60.97 59.98 83.31 64.16 42.15 78.37 37.83
Intel/neural-chat-7b-v3-2 68.29 67.49 83.92 63.55 59.68 79.95 55.12
perlthoughts/Chupacabra-7B-v2 63.54 66.47 85.17 64.49 57.6 79.16 28.35
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 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


The model is very good, works well on almost any prompt but ChatML format and Alpaca System gets the best

- 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|>
Explain QKV<|im_end|>
### 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
[Round <|round|>]
Question:Explain QKV
Question:Explain QKV

Using Exllamav2_HF set alpha=2.5 for 16K Context

Users also report that exllamav2_HF loader, 8bpw-h8 exl2 quant, simple-1 preset provides good results

Framework versions

  • Transformers 4.35.0-UNA
  • Pytorch 2.1.0
  • Datasets 2.14.6
  • Tokenizers 0.14.1


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:
  title={Cybertron: Uniform Neural Alignment}, 
  author={Xavier Murias},
  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!

Downloads last month
Model size
7.24B params
Tensor type

Datasets used to train fblgit/una-cybertron-7b-v2-bf16

Spaces using fblgit/una-cybertron-7b-v2-bf16 3

Collection including fblgit/una-cybertron-7b-v2-bf16