chrissoria/catllm-json-formatter
chrissoria/catllm-json-formatter is a fine-tuned Qwen2.5-0.5B-Instruct model designed to convert messy LLM classification outputs into valid JSON format. This 0.5 billion parameter model specializes in cleaning and structuring raw classification results from other LLMs into a consistent {"1": "0", "2": "1", ...} JSON object. Its primary use case is to ensure reliable and parseable output for downstream applications, particularly within the cat-llm framework, by handling various malformed inputs.
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CatLLM JSON Formatter Overview
chrissoria/catllm-json-formatter is a specialized Qwen2.5-0.5B-Instruct model, fine-tuned to address the common problem of inconsistent or malformed JSON output from large language models, specifically for classification tasks. It acts as a robust post-processor, taking raw, potentially messy LLM classification results and converting them into a clean, standardized JSON format like {"1": "0", "2": "1", ...}.
Key Capabilities
- Standardized JSON Output: Ensures consistent and parseable JSON from varied, malformed LLM classification responses.
- Handles Diverse Inputs: Trained on 8,000 synthetic examples covering over 26 messy output formats.
- Scalable Category Support: Reliably processes category counts from N=2 up to N=50, producing large JSON objects with many keys.
- Integrated with cat-llm: Designed to be used automatically as a fallback formatter within the cat-llm library when
json_formatter=Trueis enabled.
Good for
- Improving LLM Reliability: Essential for applications requiring strict JSON adherence from LLM classification outputs.
- Automated Data Processing: Streamlining workflows where classification results need to be programmatically consumed.
- Robust LLM Integrations: Mitigating issues caused by LLMs occasionally failing to produce perfectly formatted JSON.
- Developers using cat-llm: Provides an out-of-the-box solution for ensuring clean classification results.