---
language:
  - en
license: apache-2.0
library_name: peft
tags:
  - peft
  - lora
  - qwen2.5
  - compliance
  - fdcpa
  - text-classification
base_model: Qwen/Qwen2.5-3B-Instruct
pipeline_tag: text-classification
---

# FDCPA Rule Classifier — QLoRA Adapter

## Model Description

This is a QLoRA adapter fine-tuned on [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) for classifying debt collection call transcripts against 12 FDCPA (Fair Debt Collection Practices Act) compliance rules.

The model determines whether an agent **complied** ("pass") or **violated** ("fail") a specific FDCPA rule given a transcript chunk.

## Intended Use

- **Intended:** Research on fine-tuning small models for domain-specific compliance classification. Demonstrating cost/accuracy tradeoffs between fine-tuned models and API-based LLMs.
- **Out-of-scope:** Production compliance decisions. Legal advice. Processing real consumer data without proper authorization.

## How to Use

```python
import json
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = "Qwen/Qwen2.5-3B-Instruct"
adapter = "ree2raz/fdcpa-rule-classifier-qlora"

tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    base_model, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)
model = PeftModel.from_pretrained(model, adapter)
model.eval()

system_msg = "You are an FDCPA compliance evaluator. Given a debt collection rule and a transcript chunk from a collections call, determine whether the agent complied with the rule. Respond with a JSON object containing your verdict and reasoning."

rule_description = """## Rule
- **Rule ID:** FDCPA-001
- **Rule Name:** Mini-Miranda Disclosure
- **Description:** The debt collector must identify themselves as a debt collector and state that any information obtained will be used for that purpose within the first communication."""

transcript = "Agent: Hi, this is Alex from ABC Collections. I need to discuss your account."

user_msg = f"""{rule_description}

## Transcript Chunk
{transcript}

## Task
Determine whether the agent COMPLIED with the above rule in this transcript chunk. Respond with ONLY a JSON object:
{{"verdict": "pass" or "fail", "reasoning": "1-2 sentence explanation"}}"""

messages = [
    {"role": "system", "content": system_msg},
    {"role": "user", "content": user_msg},
]

input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.0, do_sample=False)

generated = outputs[0][inputs["input_ids"].shape[1]:]
result = json.loads(tokenizer.decode(generated, skip_special_tokens=True))
print(result)
```

## Training Data

- **Source:** ~300+ synthetic transcript chunks generated by GPT-4.1-mini
- **Rules:** 12 FDCPA compliance rules
- **Distribution:** ~28 examples per rule (15 easy, 8 medium, 5 hard difficulty)
- **Seeds:** 24 hand-curated examples anchoring the generation process
- **Split:** 80% train / 10% validation / 10% test (stratified by rule and verdict)
- **Test set:** Hand-reviewed for label accuracy

## Training Procedure

### Hyperparameters

| Parameter | Value |
|-----------|-------|
| Base model | Qwen/Qwen2.5-3B-Instruct |
| Quantization | NF4 (4-bit), double quant |
| LoRA rank | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Epochs | 3 |
| Batch size | 4 (effective 16 with grad accumulation) |
| Learning rate | 2e-4 |
| Schedule | Cosine with 10% warmup |
| Max sequence length | 2048 |
| Precision | bf16 mixed |

### Training Infrastructure

- Kaggle T4 GPU (16GB VRAM)
- ~60-90 minutes training time
- Peak VRAM: ~12-13 GB

## Evaluation Results

Evaluated on 39 hand-reviewed test examples across 12 FDCPA rules (23 pass / 16 fail).

| Model | Accuracy | F1 (macro) | Parse Rate |
|-------|----------|------------|------------|
| o3-mini (ceiling) | **100.0%** | **1.000** | 46.2% |
| Qwen Base (zero-shot) | 76.9% | 0.769 | **100.0%** |
| **Qwen QLoRA (this model)** | **84.6%** | **0.846** | **100.0%** |

The fine-tuned model closes ~32% of the gap between the base model (76.9%) and the API ceiling (100%). All 6 errors are false negatives — the model over-predicts violations on transcripts that contain surface-level non-compliance signals (incomplete disclosure, pending verification) despite the agent ultimately complying.

## Limitations and Biases

1. Trained entirely on synthetic data — distribution shift from real transcripts is unknown
2. Small training set (~300 examples) limits generalization
3. Only tested on Qwen2.5-3B architecture — results may not transfer
4. Binary pass/fail classification oversimplifies nuanced compliance questions
5. Subjective rules (harassment, tone) have inherently lower agreement
6. The model inherits biases present in the teacher model (GPT-4.1-mini)

## Citation

```bibtex
@misc{fdcpa-rule-classifier,
  title={FDCPA Rule Classifier: QLoRA Fine-tuning for Compliance Classification},
  author={Rituraj},
  year={2025},
  url={https://github.com/ree2raz/fdcpa-rule-classifier}
}
```

## Links

- [GitHub Repository](https://github.com/ree2raz/fdcpa-rule-classifier)
- [Scrutiny — Upstream Compliance System](https://github.com/ree2raz/scrutiny)
- [Base Model](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)