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base_model: Qwen/Qwen2.5-3B-Instruct
library_name: transformers
license: apache-2.0
tags:
- qwen
- conversational
- reasoning
- math
- code-generation
- css
- javascript
- html
- physics
---
# 💎 Geode Onyx 2 (3B)
Onyx 2 is a 3-billion parameter conversational AI model, fine-tuned as part of the second generation of the Geode model family.
## Model Details
- **Base Model:** Qwen 2.5 3B Instruct
- **Parameters:** 3 Billion
- **Fine-Tuning:** LoRA (r=32, alpha=64)
- **Training Loss:** 0.40
- **Precision:** FP16
- **License:** Apache 2.0
## The Geode Family (Second Generation)
The Geode family is Genue AI's lineup of locally-runnable conversational models. In the second generation, Beryl has been retired and replaced by Pyrite, a specialized coding model:
| Model | Parameters | Role |
|-------|------------|------|
| Pyrite | 7B | Coding specialist |
| Onyx | 3B | Balanced logic & personality |
| Thaumite | 8B | Flagship, highest capability |
**Note:** Beryl (0.5B) was the original lightweight experimental model in the first generation and has been replaced by Pyrite, which focuses specifically on code generation tasks.
## Usage
Onyx 2 uses the Qwen Instruct prompt format:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"GenueAI/Geode-Onyx-2",
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("GenueAI/Geode-Onyx-2")
prompt = "<|im_start|>user\nWhat is your name?<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Data
Fine-tuned on a curated dataset of 1,013 examples covering:
- **Identity & self-awareness** - AI assistant identity and capabilities
- **Mathematical reasoning** - Arithmetic, algebra, word problems
- **General knowledge** - Broad factual knowledge
- **HTML/CSS/JavaScript code generation** - Web development tasks
- **Physics problems** - Falling objects, thermodynamics
- **Genue AI ecosystem knowledge** - Company information, model family details
- **Conversational generalization** - Natural dialogue patterns
- **Anti-hallucination training** - Proper handling of unknown information (time, location, preferences)
## Model Architecture
- Base: Qwen 2.5 3B Instruct
- Adapter: LoRA with r=32, alpha=64
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Trainable parameters: 59.9M (1.9% of total)
## Training Details
- **Training regime:** FP16 mixed precision
- **Epochs:** 2
- **Batch size:** 8
- **Learning rate:** 2e-4
- **Training time:** ~8 minutes on RTX 3090
## Developed By
Genue AI — Founded by Brybod123 (Bradar)
## Model Card Contact
For questions or issues, contact Genue AI through the HuggingFace repository.
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