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Model: chrissoria/catllm-json-formatter
Source: Original Platform

README.md

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+---
+library_name: transformers
+base_model: Qwen/Qwen2.5-0.5B-Instruct
+tags:
+  - text-generation
+  - json
+  - classification
+  - catllm
+license: apache-2.0
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# CatLLM JSON Formatter
+
+A fine-tuned Qwen2.5-0.5B-Instruct model that converts messy LLM classification
+output into valid [cat-llm](https://github.com/chrissoria/cat-llm) JSON format.
+
+## Task
+
+Given a list of numbered categories and raw (possibly malformed) classification
+output from another LLM, this model produces clean JSON:
+
+```json
+{"1": "0", "2": "1", "3": "0", ...}
+```
+
+## Usage
+
+This model is used automatically by cat-llm when `json_formatter=True`:
+
+```python
+import catllm as cat
+
+results = cat.classify(
+    input_data=df["responses"],
+    categories=["Positive", "Negative", "Neutral"],
+    api_key="your-key",
+    json_formatter=True,  # enables the formatter fallback
+)
+```
+
+Install the formatter dependencies: `pip install cat-llm[formatter]`
+
+## Training
+
+- **Base model:** Qwen/Qwen2.5-0.5B-Instruct
+- **Method:** LoRA (r=16, alpha=32) merged into base weights
+- **Training data:** 8,000 synthetic examples covering 26+ messy output formats,
+  with the category count spanning N=2..50 so the formatter reliably emits
+  large (28- and 48-key) JSON objects, not just small ones.
+- **Epochs:** 2
+- **Metrics:** evaluated separately on low-N (<=12 categories) and high-N
+  (>=25 categories) buckets; see the repository's eval gate for current numbers.
+
+## Prompt Format
+
+The model uses the Qwen chat template with:
+
+**System:** JSON formatter instructions (built into cat-llm)
+
+**User:**
+```
+Categories:
+1. Category A
+2. Category B
+...
+
+Raw classification output:
+{messy output here}
+```
+
+**Assistant:** `{"1":"0","2":"1",...}`