---
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",...}`