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fblgit/una-cybertron-7b-v2-bf16
una-cybertron-7b-v2-bf16 is a text generation model from fblgit. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
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 Lea…
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From the Hugging Face model README
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.
| 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.
Adiestrated with UNA: Uniform Neural Alignment technique (paper going out soon).
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:
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
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!
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 69.67 |
| AI2 Reasoning Challenge (25-Shot) | 68.26 |
| HellaSwag (10-Shot) | 85.85 |
| MMLU (5-Shot) | 63.23 |
| TruthfulQA (0-shot) | 64.63 |
| Winogrande (5-shot) | 80.98 |
| GSM8k (5-shot) | 55.04 |