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geode-thaumite

Text Generation
Safetensors
English
qwen2
conversational
fine-tuned
geode
genue-ai
flagship
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geode-thaumite
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---
license: apache-2.0
language:
- en
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- conversational
- fine-tuned
- geode
- genue-ai
- flagship
pipeline_tag: text-generation
---

# 🔮 Genue Geode Thaumite (7B) — Flagship

**Thaumite** is Genue AI's **flagship** 7-billion parameter conversational AI model. It is the most capable model in the **Geode** family, fine-tuned for deep reasoning, code generation, and rich conversational ability.

## Model Details

| Property | Value |
|---|---|
| **Base Model** | Qwen 2.5 7B Instruct |
| **Parameters** | 7 Billion |
| **Fine-Tuning** | LoRA (r=32, alpha=32) |
| **Training Loss** | 0.646 |
| **Precision** | FP16 |
| **License** | Apache 2.0 |

## The Geode Family

The **Geode** family is Genue AI's lineup of locally-runnable conversational models:

| Model | Parameters | Role |
|---|---|---|
| [Beryl](https://huggingface.co/GenueAI/geode-beryl) | 0.5B | Lightweight, experimental |
| [Onyx](https://huggingface.co/GenueAI/geode-onyx) | 3B | Balanced logic & personality |
| **Thaumite** | 7B | 👑 Flagship, highest capability |

## Why Thaumite?

Thaumite is named after a rare crystalline mineral, representing the pinnacle of the Geode family. It offers:

- **Superior Reasoning**: Handles multi-step math, logic puzzles, and word problems
- **Code Generation**: Produces full HTML/CSS/JS applications on request
- **Rich Personality**: Maintains a consistent, friendly identity across conversations
- **Deep Knowledge**: Trained on curated facts about history, gaming, science, and more

## Usage

Thaumite uses a simple **Direct Mode** prompt format:

```
User: What is your name?
Bot:
```

### With Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("GenueAI/geode-thaumite", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained("GenueAI/geode-thaumite")

prompt = "User: Make a clicker game in HTML.\nBot:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.4, repetition_penalty=1.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Data

Fine-tuned on 64 curated conversational examples covering:
- Identity & self-awareness
- Mathematical reasoning (arithmetic, algebra, word problems)
- General & historical knowledge
- HTML/CSS/JS code generation
- Gaming knowledge (Geometry Dash, Omori)
- Genue AI ecosystem knowledge

## Developed By

**Genue AI** — Founded by Brybod123 (Bradar)

> *"Me, Thaumite of course!"* — When asked which model is best