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Model Card for una-xaberius-34b-v1-beta (UNA: Uniform Neural Alignment)

Introducing THE MODEL: XABERIUS 34B v1-BETA an experimental 34B LLaMa-Yi-34B based model, best on it's series. Trained on SFT, DPO and UNA (Unified Neural Alignment) on multiple datasets.

Timeline:

  • 05-Dec-2023 v1-beta released
Model Average ARC (25-s) HellaSwag (10-s) MMLU (5-s) TruthfulQA (MC) (0-s) Winogrande (5-s) GSM8K (5-s)
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
.. xaberius results will come out soon.

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

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 4.35.2-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{unaxaberius34b,
  title={Xaberius 34B: Uniform Neural Alignment}, 
  author={Xavier Murias},
  year={2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://huggingface.co/fblgit/una-xaberius-34b-v1beta}},
}

Special thanks to @TheBloke & @bartowski for converting the models and their support to the community. Thank you!

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Datasets used to train LoneStriker/una-xaberius-34b-v1beta-4.65bpw-h6-exl2