A parameter-efficient fine-tune of Qwen2.5-3B-Instruct specialized for Graphviz DOT code generation.
This model was trained with LoRA adapters and exported in GGUF Q5_K_M quantization for lightweight, portable inference.
📌 Model Summary
Base Model: Qwen2.5-3B-Instruct (bnb-4bit)
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Export Format: GGUF (llama.cpp-compatible)
Quantization: Q5_K_M (5-bit, medium precision)
File Size: 2.22 GB
License: Apache 2.0
Task: Natural language → Graphviz DOT code
Hosting: Hugging Face Hub
🚀 Motivation
The base Qwen2.5-3B-Instruct model often produced:
❌ Hallucinated content (extra nodes/edges not in the prompt)
Fine-tuning with LoRA adapters on a curated dataset of 671 compiler-validated DOT examples resolved these issues, ensuring structurally valid DOT syntax generation.
