UNA-cybertron-7b-v3-OMA Open Source Large Language Model - Free Deployment to Boost Mathematical and Logical Reasoning

Una Cybertron 7b V3 OMA

Developed by fblgit

UNA-cybertron-7b-v3 is a 7B parameter large language model developed by the OMA team, trained using UNA (Unified Neural Alignment) technology, excelling in mathematics, logic, and reasoning.

Large Language Model

Transformers

Open Source License:Apache-2.0 #Deep Reasoning #Mathematical Logic Optimization #Multi-format Prompt Compatibility

Downloads 103

Release Time : 12/14/2023

Model Overview

This model is based on the MistralAI 7B architecture, trained with UNA technology, excelling in deep reasoning and contextual understanding, supporting multiple prompt formats, and suitable for various natural language processing tasks.

Model Features

Unified Neural Alignment Technology

Utilizes unique UNA training methods, differing from traditional layer merging or SLERP/SLURP techniques

Exceptional Reasoning Capabilities

Outstanding performance in mathematics, logic, and complex reasoning tasks

Flexible Prompt Format Support

Compatible with ChatML, Alpaca, and other prompt formats

Retrainability

Supports further supervised fine-tuning (SFT)

Digital Watermark

Includes digital watermarking technology

Model Capabilities

Text Generation

Logical Reasoning

Mathematical Computation

Question Answering Systems

Creative Writing

Use Cases

Education

Math Problem Solving

Helps students understand complex mathematical concepts

Accurately solves various math problems

Creative Industries

Content Creation

Generates poetry, short stories, and jokes

High-quality creative text output

Technical Fields

Technical Concept Explanation

Explains machine learning concepts like QKV

Clear and accurate technical explanations

🚀 Model Card for una-cybertron-7b-v3 (UNA: Uniform Neural Alignment)

OMA (One Man Army) proudly presents a new 7B Champion: cybertron-7b-v3 with our famous UNA algorithm. This model stands out in mathematics, logic, and reasoning, demonstrating high intelligence. It can conduct in - depth reasoning based on the context and prompts, seemingly not missing any details.

Key Features

UNA models can undergo Supervised Fine - Tuning (SFT) again.
UNA models are suitable as Merge Bases. You can place Cybertron in the fan - in and fan - out of the layering.
UNA models now include a digital watermark.

🚀 Quick Start

Model Details

The model is trained with the UNA: Uniform Neural Alignment technique (a related paper will be published soon).

What is NOT UNA? It is not a merged layers model, nor is it SLERP, SLURP, or similar techniques.
What is UNA? It is a formula and a technique to TAME models.

Model Description

Property	Details
Developed by	juanako.ai
Author	Xavier M.
Model Type	MistralAI 7B
Training	Funded by Cybertron's H100's with few hours training

Prompt

The model performs well on almost any prompt. However, the ChatML format and Alpaca System yield the best results.

<|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

Framework versions

Property	Details
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, especially if you use it for your big brand or clone/merge our models, please cite:

@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-v3-OMA}},
}

📄 License

This project is licensed under the Apache - 2.0 license.

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