SpyazWeb_AI_DeepMind_Project Open-Source Large Model - Supports Multilingual Text Generation and Complex Reasoning

Spyazweb AI DeepMind Project

Developed by LeroyDyer

A multi-functional large language model based on the Mixtral architecture, integrating deep mind and self-optimizing capabilities, supporting multi-language text generation and complex reasoning tasks

Large Language Model

Transformers

EnglishOpen Source License:Apache-2.0 #Multi-task text generation #Self-optimizing learning #Medical-programming dual domains

Downloads 97

Release Time : 5/7/2024

Model Overview

This model is the core result of the Deep Mind project, possessing advanced cognitive abilities such as self-reflection, agent generation, and chain-of-thought, suitable for various scenarios such as open-domain dialogue, code generation, and mathematical reasoning

Model Features

Multi-modal expansion ability

It has integrated CLIP training ability and can be extended to vision-language multi-modal tasks

Self-teaching optimization

Supports a self-optimizing mechanism similar to the learning process of children

Complex reasoning ability

Achieved a standardized accuracy of 61.86% in the AI2 Reasoning Challenge (ARC)

Multi-language support

In addition to English, it also contains Swahili training data

Model Capabilities

Text generation

Code generation

Mathematical problem solving

Open-domain dialogue

Multi-language processing

Complex reasoning

Self-optimizing learning

Use Cases

Educational research

Mathematical problem solving

Solve the GSM8K math problem set

51.86% accuracy (5 times few-shot)

Business intelligence

SQL generation

Generate SQL queries based on natural language

Trained on the synthetic_text_to_sql dataset

Multi-language applications

Swahili processing

Handle English-Swahili bilingual tasks

Trained on specialized datasets such as SwahiliPlatypus

🚀 Mixtral_AI_CyberTron_DeepMind_III_UFT

This project presents a powerful text - generation model, Mixtral_AI_CyberTron_DeepMind_III_UFT, which surpasses ChatGPT in certain aspects. It can perform various tasks without special programming and has unique features like self - learning and optimization.

✨ Features

Surpassing ChatGPT: Achieved better performance than ChatGPT without using langchain.
Functionality Addition: There is a methodology to add any functionality to the model.
Self - Learning and Optimization: The model can teach itself like a child and optimize.
Multiple Cognitive Abilities: It has self - RAG, agent generation, chain of thoughts, deep thinking, and memory recall capabilities.
Personality and Friendliness: Developed a personality through specific datasets, acting as a friendly personal assistant.
Task Execution: Can perform various tasks such as entity recognition, writing pseudo - code, and performing calculations without special programming.

📦 Installation

No installation steps are provided in the original document.

💻 Usage Examples

No code examples are provided in the original document.

📚 Documentation

Model Information

Property	Details
Model Type	Mistral - based text - generation model
Base Model	LeroyDyer/Mixtral_AI_CyberTron_DeepMind_III
Training Datasets	gretelai/synthetic_text_to_sql, HuggingFaceTB/cosmopedia, teknium/OpenHermes - 2.5, etc.
Metrics	accuracy, bertscore, bleu, brier_score, cer, etc.

Training and Methodology

Training Prompt Version: Works great. It can check itself when discussing complex questions, generate mini - agents for small tasks, and has various thinking modes.
Methodology: Added many functions via datasets and complex data structures and prompts. Removed these prompts after training and applied standard alpaca training. Adjusted Lora configuration for embedding layers and fine - tuned on previous training. Parameters like topK, topP, and temp were carefully set to ensure good performance and response diversity.

Evaluation Results

Detailed evaluation results can be found [here](https://huggingface.co/datasets/open - llm - leaderboard/details_LeroyDyer__Mixtral_AI_CyberTron_DeepMind_III_UFT)

Metric	Value
Avg.	64.37
AI2 Reasoning Challenge (25 - Shot)	61.86
HellaSwag (10 - Shot)	83.15
MMLU (5 - Shot)	61.95
TruthfulQA (0 - shot)	49.41
Winogrande (5 - shot)	77.98
GSM8k (5 - shot)	51.86

🔧 Technical Details

The model is based on the Mistral architecture. During training, a series of complex operations were carried out, including adding functions through datasets and prompts, adjusting Lora configuration, and fine - tuning. These operations ensure that the model can perform various tasks and has good generalization ability.

📄 License

This project is licensed under the apache - 2.0 license.

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