Model Card for una-xaberius-34b-v1-beta (UNA: Uniform Neural Alignment)
This is another King-Breed from Juanako.AI
We have Identified some Problems with regular Quants use these models to play with Xaberius-34B and harness its power in full. Unfortunately we were not able to use any of TheBloke models, seems there is some undesired results out of it.
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
- 08-Dec-2023 Evaluation been "RUNNING" for 2 days.. no results yet
- 09-Dec-2023 Evaluation been "FINISHED", confirming #1 spot outperforming the contaminated-disqualified tigerbot :)
Sidenote: Tests took 19H to run, wonder what happened in the 48H that HF held this one.. interim releasing manually other results??..
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 |
fblgit/una-xaberius-34b-v1beta | 74.18 | 70.39 | 86.77 | 78.15 | 61.45 | 84.93 | 63.38 |
Evaluations
- Scores 74.21 Outperforming former leader tigerbot-70b-chat and landing on #1 position of HuggingFace LeaderBoard: 08 December 2023.
- Scores 79.13 in MMLU, setting a new record not just for 34B but also for all OpenSource LLM's :)
SideNote: MMLU was a very solid 79+ .. weird, we'll dive further on this for irregularities :)
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
- Author: Xavier M.
- Investors CONTACT HERE
- Model type: LLaMa YI-34B
- 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 4.35.2-UNA
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
Citations
If you find Xaberius, Cybertron, Juanako or any of our models useful, specially if you use it for your big brand or you cloning/merge/SLERP 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}},
}
Thanks to LoneStriker for his ExLLama2 models of high quality that works properly. Enormous Ku2 to Yi-34b Team for the outstanding model, UNA is only as good as its pre-trained model THANK YOU!
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 74.18 |
AI2 Reasoning Challenge (25-Shot) | 70.39 |
HellaSwag (10-Shot) | 86.77 |
MMLU (5-Shot) | 78.15 |
TruthfulQA (0-shot) | 61.45 |
Winogrande (5-shot) | 84.93 |
GSM8k (5-shot) | 63.38 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard70.390
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard86.770
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard78.150
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard61.450
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard84.930
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard63.380