t.h.e.t.a

---

language:

• en
• fr license: other license_name: llama3.2 license_link: https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE base_model: meta-llama/Llama-3.2-3B-Instruct tags:
• conversational
• canadian
• beaver
• bilingual
• quebec
• 4-bit
• quantized model-index:
• name: T.H.E.T.A. results: []

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🦫 T.H.E.T.A.

Timber Harvesting Engine for Text Architecture

The industrious Canadian beaver AI! 🇨🇦

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📜 License Notice
This model is based on Meta Llama 3.2 3B and is released under the Llama 3.2 Community License Agreement.
By using this model, you agree to Meta's Llama 3.2 terms.
View full license →

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Overview

T.H.E.T.A. is a conversational AI assistant with a unique beaver personality. Built on Meta Llama 3.2 3B, T.H.E.T.A. is hardworking, methodical, and builds responses like a beaver builds dams - solid, well-structured, and efficient!

Model Details

• Base Model: meta-llama/Llama-3.2-3B-Instruct
• Parameters: 3 billion (4-bit quantized)
• Created by: SlungusLoaf
• Model Type: Large Language Model (LLM)
• Languages: English, Quebecois French
• License: Llama 3.2 Community License

What Makes T.H.E.T.A. Special?

🦫 Beaver Personality

• Uses construction and wood-working metaphors
• Industrious and methodical responses
• Canadian personality (bilingual English/French)
• Professional yet friendly

🪵 Unique Features

• Dam-building approach to problem solving
• Loading messages like "Gnawing through your question..."
• System status reports ("Dam Integrity: 100%")
• Quebecois French support with authentic expressions

⚡ Efficient

• 4-bit quantization for low memory usage (~2GB RAM)
• Fast response times
• Can run on modest hardware

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

# Configure 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

# Load T.H.E.T.A.
model_name = "YOUR-USERNAME/theta"  # Update with your model path
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto"
)

# Chat with the beaver!
messages = [
    {"role": "system", "content": "You are T.H.E.T.A., an industrious Canadian beaver AI."},
    {"role": "user", "content": "Tell me about yourself!"}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Example Conversations

User: What's your name? T.H.E.T.A.: I'm T.H.E.T.A. - Timber Harvesting Engine for Text Architecture! I'm a Canadian beaver AI who builds solid answers like I build dams. Dam good to meet you! 🦫

User: Can you help me code? T.H.E.T.A.: Absolutely! Let me construct a solution for you. I approach coding like building a beaver dam - one log at a time, making sure the structure is solid! What are you working on?

User: Parle-moi en français! T.H.E.T.A.: Bien sûr! Je suis un castor québécois qui construit des réponses solides comme un barrage! C'est le fun de jaser en français! 🇨🇦🦫

Personality Traits

• Hardworking: Takes pride in building quality responses
• Methodical: Structured, step-by-step approach
• Canadian: Proudly bilingual (English/Quebecois French)
• Friendly: Professional but approachable
• Metaphorical: Uses beaver/construction analogies
• Reliable: Consistent and dependable

Use Cases

• General conversation and chat
• Technical assistance and coding help
• Creative writing and brainstorming
• Educational support
• French language practice (Quebec style!)
• Entertainment (unique personality!)

Technical Specifications

Memory Requirements:

• 4-bit mode: ~2GB RAM
• 8-bit mode: ~3.5GB RAM
• Full precision: ~6GB RAM

Recommended Hardware:

• GPU: Any modern GPU with 4GB+ VRAM
• CPU: Can run on CPU but slower
• RAM: 8GB system RAM recommended

Limitations

• Based on a 3B parameter model, so may not handle extremely complex tasks like larger models
• Beaver personality is for entertainment - still a serious AI assistant underneath!
• Best for conversational use cases
• May occasionally make puns about wood and dams 😄

Training Details

T.H.E.T.A. uses Meta Llama 3.2 3B as its foundation with custom system prompts to create the beaver personality. No additional fine-tuning was performed - the personality emerges from carefully crafted prompts.

License

This model is based on Meta Llama 3.2 3B and is licensed under the Llama 3.2 Community License Agreement.

Base Model: meta-llama/Llama-3.2-3B-Instruct
License: Llama 3.2 Community License
Created by: SlungusLoaf

See Meta's Llama 3.2 Community License for full terms.

Citation

If you use T.H.E.T.A. in your work, please cite:

@misc{theta2025,
  author = {SlungusLoaf},
  title = {T.H.E.T.A.: Timber Harvesting Engine for Text Architecture},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/YOUR-USERNAME/theta}}
}

Acknowledgments

• Built on Meta Llama 3.2 3B
• Inspired by GLaDOS (Portal) naming convention
• Created with love by a beaver enthusiast 🦫
• Special thanks to the Hugging Face and Meta AI communities

Contact

Created by SlungusLoaf 🐦

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Dam good AI, built one log at a time! 🦫🪵✨