metadata

license: cc-by-nc-nd-4.0
library_name: transformers
tags:
  - UNA
  - juanako
  - cybertron
  - xaberius
datasets:
  - fblgit/tree-of-knowledge
  - garage-bAInd/Open-Platypus
  - allenai/ultrafeedback_binarized_cleaned
  - Open-Orca/OpenOrca
model-index:
  - name: una-xaberius-34b-v1beta
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 70.39
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-xaberius-34b-v1beta
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 86.77
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-xaberius-34b-v1beta
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 78.15
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-xaberius-34b-v1beta
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 61.45
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-xaberius-34b-v1beta
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 84.93
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-xaberius-34b-v1beta
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 63.38
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-xaberius-34b-v1beta
          name: Open LLM Leaderboard

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 :)

Results Here

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