luepow/thau-agi-v2

THAU AGI v2 - Proto-AGI System

THAU = THomas + AUrora

A Proto-AGI (Prototype Artificial General Intelligence) system fine-tuned from TinyLlama-1.1B with specialized training in reasoning, tool calling, and Spanish language support.

Features

• ReAct Cycle: THINK -> PLAN -> ACT -> OBSERVE -> REFLECT
• Experiential Learning: Learns from past interactions
• Metacognition: Self-evaluation for improvement
• Web Search: Internet search capabilities
• Multi-Agent: Collaboration between specialized agents (CODER, REVIEWER, RESEARCHER, PLANNER, TESTER)
• Knowledge Base: RAG (Retrieval Augmented Generation)
• Feedback Loop: Continuous improvement with user feedback
• Tool Calling: Integrated tools for calculations, file operations, code execution
• TTS Support: Text-to-Speech integration
• Image Generation: Stable Diffusion integration
• MCP Integration: Model Context Protocol support

Available Tools

Tool	Description
calculate	Mathematical calculations
read_file	Read files
write_file	Write files
list_directory	List directories
execute_python	Execute Python code
web_search	Search on internet
fetch_url	Get URL content
research	Deep research
text_to_speech	Convert text to speech
generate_image	Generate images

Operation Modes

1. CHAT: Casual conversation
2. TASK: Specific tasks with tools
3. RESEARCH: Deep information search
4. COLLABORATIVE: Multi-agent collaboration
5. LEARNING: Intensive learning mode

Usage

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("luepow/thau-agi-v2")
tokenizer = AutoTokenizer.from_pretrained("luepow/thau-agi-v2")

prompt = "<|system|>\nYou are THAU AGI v2, a helpful AI assistant.</s>\n<|user|>\nWhat is 25 * 4 + 100?</s>\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

With Ollama

ollama pull luepow/thau:agi-v2
ollama run luepow/thau:agi-v2

With Gradio Interface

git clone https://github.com/luepow/thau.git
cd thau
pip install -r requirements.txt
python scripts/gradio_thau_ollama.py

Training Data

The model was fine-tuned on:

• Programming tutorials (Python, JavaScript, Rust, Go, Java)
• Mathematical reasoning
• Tool calling patterns
• Spanish language content
• DevOps and cloud infrastructure
• Agile methodologies
• UX/CSS frameworks

Model Card

• Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
• Parameters: 1.1B
• Context Length: 4096 tokens
• Languages: English, Spanish
• License: MIT

Links

• GitHub: https://github.com/luepow/thau
• Ollama: https://ollama.com/luepow/thau
• Support: Buy Me a Coffee - luepowg

Credits

Developed with love for Thomas & Aurora.

THAU = THomas + AUrora