💎 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:

Note: Beryl (0.5B) was the original lightweight experimental model in the first generation and has been replaced by Pyrite, which focuses specifically on code generation tasks.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:

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.