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Model: chrissoria/catllm-json-formatter
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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",...}`

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "float32",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 896,
"initializer_range": 0.02,
"intermediate_size": 4864,
"layer_types": [
"full_attention",
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],
"max_position_embeddings": 32768,
"max_window_layers": 21,
"model_type": "qwen2",
"num_attention_heads": 14,
"num_hidden_layers": 24,
"num_key_value_heads": 2,
"pad_token_id": null,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"bos_token_id": 151643,
"do_sample": true,
"eos_token_id": [
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"pad_token_id": 151643,
"repetition_penalty": 1.1,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.0.0"
}

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{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"extra_special_tokens": {},
"is_local": false,
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}