dennisonb/qwen25-tax-3b

Qwen2.5 Tax 3B — IRS Tax Code Expert

A 3B parameter model fine-tuned on the Internal Revenue Code using a 3-stage RL pipeline:

1. SFT (Supervised Fine-Tuning) on 16,909 RAG-grounded Q&A pairs from all 2,113 IRC sections
2. DPO (Direct Preference Optimization) with 1,311 hard-negative pairs + on-policy error correction
3. GRPO (Group Relative Policy Optimization) with citation accuracy reward signal

Training Pipeline

Stage	Data	Iterations	Key Metric
SFT	16,909 grounded pairs	1,000	Val loss: 0.765
DPO	1,311 preference pairs	500	Loss: 0.005
GRPO	16,909 prompts	300	Avg reward: 0.978

Model Versions

• v1: Initial SFT + DPO + GRPO (basic reward)
• v2: Improved grounded data + hard-negative DPO (best factual accuracy)
• v3: + On-policy DPO + citation accuracy reward (best citation specificity)

Usage with Ollama

# Download the GGUF
wget https://huggingface.co/dennisonb/qwen25-tax-3b/resolve/main/qwen25-tax-3b-v3-q8_0.gguf

# Create Ollama model
ollama create qwen25-tax-3b -f Modelfile

# Run
ollama run qwen25-tax-3b "What is the standard deduction for a single filer?"

Evaluation Results

Model	5-Q Score	GRPO Reward	Notes
v1	2.5/5	0.605	Frequent hallucinations
v2	4.5/5	0.828	Best factual accuracy
v3	3.5/5	0.978	Best citation specificity

Limitations

• 3B model cannot reliably memorize all IRC section numbers and dollar thresholds
• May hallucinate specific amounts (e.g., Section 179 limits)
• Best used with RAG (retrieval-augmented generation) for production
• Not a substitute for professional tax advice

Training Data

All training data was generated using RAG from the actual IRC statutory text:

• Source: 2,113 IRC sections parsed from the US Code
• Generation: GPT-4o-mini with actual statute text in context
• Validation: Cross-reference checking, citation accuracy validation
• Cost: ~$9 total API cost for full dataset generation

Built With

• MLX - Apple Silicon native ML framework
• Qwen2.5-3B-Instruct - Base model
• Ollama - Local model deployment
• OpenAI Batch API - Training data generation