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Model: fblgit/una-cybertron-7b-v2-bf16 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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library_name: transformers
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tags:
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- juanako
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- UNA
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- cybertron
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- fbl
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datasets:
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- fblgit/tree-of-knowledge
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- Open-Orca/SlimOrca-Dedup
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- allenai/ultrafeedback_binarized_cleaned
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model-index:
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- name: una-cybertron-7b-v2-bf16
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 68.26
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 85.85
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 63.23
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 64.63
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 80.98
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 55.04
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16
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name: Open LLM Leaderboard
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---
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# Model Card for una-cybertron-7b-v2-bf16 (UNA: Uniform Neural Alignment)
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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.
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He scores [EXACTLY](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v2-bf16) **#1** with **69.67**+ score on HF LeaderBoard board, **#8** ALL SIZES top score.
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* v1 Scoring **#1** at 2 December 2023 with 69.43 ..few models were releasse .. but only 1 can survive: CYBERTRON!
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* v2 Scoring **#1** at 5 December 2023 with 69.67
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| Model | Average | ARC (25-s) | HellaSwag (10-s) | MMLU (5-s) | TruthfulQA (MC) (0-s) | Winogrande (5-s) | GSM8K (5-s) |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 60.97 | 59.98 | 83.31 | 64.16 | 42.15 | 78.37 | 37.83 |
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| [Intel/neural-chat-7b-v3-2](https://huggingface.co/Intel/neural-chat-7b-v3-2) | 68.29 | 67.49 | 83.92 | 63.55 | 59.68 | 79.95 | 55.12 |
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| [perlthoughts/Chupacabra-7B-v2](https://huggingface.co/perlthoughts/Chupacabra-7B-v2) | 63.54 | 66.47 | 85.17 | 64.49 | 57.6 | 79.16 | 28.35 |
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| [fblgit/una-cybertron-7b-v1-fp16](https://huggingface.co/fblgit/una-cybertron-7b-v1-fp16) | **69.49** | **68.43** | **85.85** | 63.34 | **63.28** | **80.90** | **55.12** |
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| [fblgit/una-cybertron-7b-v2-bf16](https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16) | **69.67** | **68.26** | **85.?4** | 63.23 | **64.63** | **81.37** | **55.04** |
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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.
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## Model Details
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Adiestrated with UNA: Uniform Neural Alignment technique (paper going out soon).
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* What is **NOT** UNA? Its not a merged layers model. Is not SLERP or SLURP or similar.
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* What **is** UNA? A formula & A technique to *TAME* models
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* When will be released the code and paper? When have time, contribute and it'll be faster.
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### Model Description
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- **Developed by:** [juanako.ai](https://juanako.ai)
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- **Author:** [Xavier M.](xavi@juanako.ai)
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- **Investors** [CONTACT HERE](billing@juanako.ai)
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- **Model type:** MistralAI 7B
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- **Funded by Cybertron's H100's** with few hours training.
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### Prompt
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The model is very good, works well on almost any prompt but ChatML format and Alpaca System gets the best
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```
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<|im_start|>system
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- You are a helpful assistant chatbot trained by MosaicML.
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- You answer questions.
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- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
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- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
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<|im_start|>user
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Explain QKV<|im_end|>
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<|im_start|>assistant
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```
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```
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### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat!
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### Human: Explain QKV
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### Assistant:
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```
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```
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[Round <|round|>]
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问：Explain QKV
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答：
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```
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```
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[Round <|round|>]
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Question：Explain QKV
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Answer：
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```
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```
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Question：Explain QKV
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Answer：
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```
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Using Exllamav2_HF set alpha=2.5 for 16K Context
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**Users also report that exllamav2_HF loader, 8bpw-h8 exl2 quant, simple-1 preset provides good results**
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### Framework versions
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- Transformers 4.35.0-UNA
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- Pytorch 2.1.0
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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### Citations
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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:
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```
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@misc{unacybertron7b,
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title={Cybertron: Uniform Neural Alignment},
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author={Xavier Murias},
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year={2023},
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publisher = {HuggingFace},
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journal = {HuggingFace repository},
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howpublished = {\url{https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16}},
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}
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```
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Special thanks to @TheBloke & @bartowski for converting the models and their support to the community. Thank you!
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v2-bf16)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |69.67|
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|AI2 Reasoning Challenge (25-Shot)|68.26|
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|HellaSwag (10-Shot) |85.85|
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|MMLU (5-Shot) |63.23|
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|TruthfulQA (0-shot) |64.63|
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|Winogrande (5-shot) |80.98|
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|GSM8k (5-shot) |55.04|
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