Overview
Brigham-Young-University/Qwen2.5-Coder-3B-Ilograph-Instruct is a 3.1 billion parameter causal language model, fine-tuned by Chris Mijangos at BYU from the Qwen2.5-Coder-3B-Instruct base model. This model is specifically designed to generate Ilograph Diagram Language (IDL) specifications based on natural language prompts. It leverages a 32768-token context length and is provided as a standalone Transformers causal language model, having been trained with LoRA and then merged.
Key Capabilities
- IDL Generation: Translates natural language instructions into valid Ilograph Diagram Language (IDL) specifications.
- Specialized Output: Focuses exclusively on generating IDL in YAML format, explicitly avoiding JSON or Mermaid syntax.
- System Prompt & Schema: Comes with a recommended system prompt and an IDL schema (JSON) to guide output formatting and ensure adherence to IDL standards.
Use Cases
- Automated Diagramming: Ideal for developers and architects needing to quickly generate Ilograph diagrams for resources, relationships, and sequences from textual descriptions.
- Simplified Diagram Creation: Streamlines the process of creating technical diagrams by converting high-level instructions into structured IDL code.
Limitations
- Primarily suited for relatively simple Ilograph diagrams due to its size and focused training data.
- May not perform optimally for highly complex, large-scale, or extensively customized diagram structures.
- Users are encouraged to evaluate its performance for their specific use cases, especially for more intricate diagramming needs.