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Model: GenueAI/Geode-Onyx-2
Source: Original Platform

README.md

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+**Note:** Beryl (0.5B) was the original lightweight experimental model in the first generation and has been replaced by Pyrite, which focuses specifically on code generation tasks.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