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Model Card for una-cybertron-7b-v1 (UNA: Uniform Neural Alignment)

We strike back, introducing Cybertron 7B v1 a 7B MistralAI based model, best on it's series. Trained on SFT, DPO and UNA (Unified Neural Alignment) on multiple datasets. He scores 64.60+ on HF LeaderTests (without DROP for now).

Scoring #1 at 2 December 2023:

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
perlthoughts/Chupacabra-7B-v2 63.54 66.47 85.17 64.49 57.6 79.16 28.35
fblgit/una-cybertron-7b-v1 64.60 68.17 85.14 62.07 63.98 80.9 27.34

The model excels in mathematics, logic, reasoning, overall very smart.

Model Details

Adiestrated with UNA: Uniform Neural Alignment technique (paper going out soon).

Model Description

  • Developed by: juanako.ai
  • Author: Xavier M.
  • Model type: MistralAI 7B
  • Funded by Cybertron's H100's

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:

Evaluation

|    Tasks     |Version|Shots | Metric |Value |   |Stderr|
|--------------|-------|------|--------|-----:|---|-----:|
|arc_challenge |       | 25   |acc_norm|0.6817|±  |0.0136|
|truthfulqa_mc2|       | 0    |acc     |0.6398|±  |0.0151|
|hellaswag     |       | 10   |acc_norm|0.8492|±  |0.0036|
|winogrande    |       | 0    |acc     |0.809 |±  |0.011 |
|gsm8k         |       | 5    |acc     |0.2733|±  |0.0137|
|mmlu          |       | 5    |acc     |0.6207|±  |0.1230|
|              |average|      |acc     |0.6456|   |      |

|      Groups      |Version|Filter|n-shot|Metric|Value |   |Stderr|
|------------------|-------|------|-----:|------|-----:|---|-----:|
|mmlu              |N/A    |none  |     0|acc   |0.6207|_  |0.1230|
| - humanities     |N/A    |none  |     5|acc   |0.5675|_  |0.1125|
| - other          |N/A    |none  |     5|acc   |0.6933|_  |0.1108|
| - social_sciences|N/A    |none  |     5|acc   |0.7270|_  |0.0666|
| - stem           |N/A    |none  |     5|acc   |0.5249|_  |0.1311|

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.. cite please:

@misc{unacybertron7a,
  title={Cybertron: Uniform Neural Alignment}, 
  author={Xavier Murias},
  year={2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
  howpublished = {\url{https://huggingface.co/fblgit/una-cybertron-7b-v1}},
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 69.49
AI2 Reasoning Challenge (25-Shot) 68.43
HellaSwag (10-Shot) 85.42
MMLU (5-Shot) 63.34
TruthfulQA (0-shot) 63.28
Winogrande (5-shot) 81.37
GSM8k (5-shot) 55.12
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Datasets used to train fblgit/una-cybertron-7b-v1-fp16

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Collection including fblgit/una-cybertron-7b-v1-fp16

Evaluation results