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RichardErkhov/
fblgit_-_una-cybertron-7b-v2-bf16-gguf

Name: fblgit_-_una-cybertron-7b-v2-bf16-gguf
Availability: InStock

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una-cybertron-7b-v2-bf16 - GGUF

Model creator: https://huggingface.co/fblgit/
Original model: https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16/

Name	Quant method	Size
una-cybertron-7b-v2-bf16.Q2_K.gguf	Q2_K	2.53GB
una-cybertron-7b-v2-bf16.IQ3_XS.gguf	IQ3_XS	0.58GB
una-cybertron-7b-v2-bf16.IQ3_S.gguf	IQ3_S	2.96GB
una-cybertron-7b-v2-bf16.Q3_K_S.gguf	Q3_K_S	2.95GB
una-cybertron-7b-v2-bf16.IQ3_M.gguf	IQ3_M	3.06GB
una-cybertron-7b-v2-bf16.Q3_K.gguf	Q3_K	3.28GB
una-cybertron-7b-v2-bf16.Q3_K_M.gguf	Q3_K_M	3.28GB
una-cybertron-7b-v2-bf16.Q3_K_L.gguf	Q3_K_L	3.56GB
una-cybertron-7b-v2-bf16.IQ4_XS.gguf	IQ4_XS	3.67GB
una-cybertron-7b-v2-bf16.Q4_0.gguf	Q4_0	3.83GB
una-cybertron-7b-v2-bf16.IQ4_NL.gguf	IQ4_NL	3.87GB
una-cybertron-7b-v2-bf16.Q4_K_S.gguf	Q4_K_S	3.86GB
una-cybertron-7b-v2-bf16.Q4_K.gguf	Q4_K	4.07GB
una-cybertron-7b-v2-bf16.Q4_K_M.gguf	Q4_K_M	4.07GB
una-cybertron-7b-v2-bf16.Q4_1.gguf	Q4_1	4.24GB
una-cybertron-7b-v2-bf16.Q5_0.gguf	Q5_0	3.68GB
una-cybertron-7b-v2-bf16.Q5_K_S.gguf	Q5_K_S	4.65GB
una-cybertron-7b-v2-bf16.Q5_K.gguf	Q5_K	4.78GB
una-cybertron-7b-v2-bf16.Q5_K_M.gguf	Q5_K_M	4.78GB
una-cybertron-7b-v2-bf16.Q5_1.gguf	Q5_1	5.07GB
una-cybertron-7b-v2-bf16.Q6_K.gguf	Q6_K	5.53GB
una-cybertron-7b-v2-bf16.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 library_name: transformers tags:

juanako
UNA
cybertron
fbl datasets:
fblgit/tree-of-knowledge
Open-Orca/SlimOrca-Dedup
allenai/ultrafeedback_binarized_cleaned model-index:
name: una-cybertron-7b-v2-bf16 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: 68.26 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 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: 85.85 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 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: 63.23 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 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: 64.63 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 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: 80.98 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 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: 55.04 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/una-cybertron-7b-v2-bf16 name: Open LLM Leaderboard

Model Card for una-cybertron-7b-v2-bf16 (UNA: Uniform Neural Alignment)

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.

v1 Scoring #1 at 2 December 2023 with 69.43 ..few models were releasse .. but only 1 can survive: CYBERTRON!
v2 Scoring #1 at 5 December 2023 with 69.67

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.

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: MistralAI 7B
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：

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

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.. 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!

Open LLM Leaderboard Evaluation Results

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

RichardErkhov/fblgit_-_una-cybertron-7b-v2-bf16-gguf