pokutuna/llm2025-basic-chat-template-only is a 4 billion parameter language model based on the Qwen3-4B-Instruct-2507 architecture. It features a custom, GEPA-optimized chat template designed for structured output generation, specifically for formats like JSON, YAML, XML, TOML, and CSV. This model excels at converting and generating data in specified structured formats by dynamically appending format-specific instructions based on the user's initial query.
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Overview
pokutuna/llm2025-basic-chat-template-only is a 4 billion parameter model derived from Qwen/Qwen3-4B-Instruct-2507. Its core distinction lies in a highly optimized chat template engineered for precise structured output generation. The model's weights are essentially unchanged from its base, with only a minimal modification to a single lm_head weight to meet a competition requirement.
Key Capabilities
- Structured Output Optimization: Features a GEPA-optimized chat template that intelligently detects target output formats (TOML, XML, YAML, JSON, CSV) from the first line of a user query.
- Dynamic Instruction Appending: Automatically appends format-specific instructions to the prompt, guiding the model to produce accurate and well-formed structured data.
- Format-Specific Rules: Includes detailed, strict rules for XML and YAML generation, covering syntax, structure preservation, special character handling, and validation checks.
- Base Model Performance: Inherits the capabilities of the Qwen3-4B-Instruct-2507 base model for general language understanding and generation, enhanced for structured tasks.
Good For
- Developers requiring reliable conversion between structured data formats (e.g., JSON to YAML, CSV to XML).
- Applications needing to generate structured data (JSON objects, XML documents, YAML configurations) with high fidelity to specific schemas or formatting rules.
- Use cases where precise control over the output format and adherence to strict syntax are critical, such as API integrations or configuration file generation.