README.md

---
license: llama3.2
---
---
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: []
---

# 🦫 T.H.E.T.A.

**Timber Harvesting Engine for Text Architecture**

The industrious Canadian beaver AI! 🇨🇦

---

> **📜 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 →](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE)

---

## 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

```python
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](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) for full terms.

## Citation

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

```bibtex
@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 🐦

---

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