README.md

# Green Patent Detection: Advanced Agentic Workflow with QLoRA

## Project Summary

This is the final assignment it synthesizes Assignments 2 and 3 into a data labelling pipeline. A Generative LLM is fine-tuned via QLoRA to understand patent language, then integrated as the "jduge" of a Multi-Agent System (MAS) to debate and label complex patent claims. Finally, a targeted Human-in-the-Loop (HITL) review step produces a gold dataset for a final PatentSBERTa fine-tuning.

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## Pipeline Architecture

patents_50k_green.parquet

[Part A & B] Baseline PatentSBERTa + Uncertainty Sampling
- Top 100 high-risk claims (u ≈ 1.0)

[Part C – Step 1] QLoRA Fine-tuning on Colab (Qwen3-8B, 4-bit, 3 epochs)
- qlora_green_patent_adapter  (LoRA weights)
- Qwen3-8B.Q4_K_M.gguf      (served via LM Studio)

[Part C – Step 2] Multi-Agent System (CrewAI)
- Agent 1 – Advocate  (Qwen3-4B, argues for green: Advocator)
- Agent 2 – Skeptic   (Qwen3-4B, argues against green: Skeptic)
-  Agent 3 – Judge     (QLoRA Qwen3-8B, final verdict: Judge)

[Part D] Exception-Based HITL (only deadlocks / low-confidence)
- 26 claims reviewed with deadlock, 3 human overrides
- hitl_green_100_final.csv  (gold labels)

[Part D] Final PatentSBERTa Fine-tuning on gold dataset
- patentsberta_finetuned_final/


## Part C – Step 1: QLoRA Domain Adaptation

The generative LLM fine-tuning was performed on Google Colab (T4, 15 GB VRAM) using Unsloth's QLoRA implementation.

| Parameter | Value |
|---|---|
| Base model | `unsloth/Qwen3-8B-bnb-4bit` |
| LoRA rank (r) | 16 |
| LoRA alpha | 16 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| Training examples | 2,000 (train_silver, Alpaca format) |
| Epochs | 3 (375 total steps) |
| Batch size | 4 × 4 gradient accumulation = effective 16 |
| Learning rate | 2e-4 (AdamW 8-bit, linear schedule) |
| Max sequence length | 2,048 tokens |
| Training loss | 0.8899 |
| Training time | ~105 minutes on T4 |
| VRAM usage | ~5 GB (4-bit quantization) |

The fine-tuned adapter was exported to GGUF Q4_K_M format (4.682 GB) and served locally via LM Studio for use in the MAS.


## Part C – Step 2: Multi-Agent System

Three agents collaborate to label each of the 100 high-risk patent claims using CrewAI as the orchestration framework. The QLoRA fine-tuned model serves as the Judge's brain.

| Agent | Model | Temperature | Role |
|---|---|---|---|
| Advocate | Qwen3-4B (LM Studio) | 0.1 | Argues FOR Y02 green classification |
| Skeptic | Qwen3-4B (LM Studio) | 0.1 | Argues AGAINST (identifies greenwashing) |
| Judge | QLoRA Qwen3-8B (LM Studio) | 0.1 | Weighs debate and produces final JSON label |

Each claim produces a structured JSON output: `classification` (0/1), `confidence` (Low/Medium/High), and `rationale`.


## Part D: Targeted HITL & Final Fine-tuning

**Exception-Based HITL** was applied — only intervening when agents reached a deadlock or produced low-confidence outputs.

| Metric | Value |
|---|---|
| Total claims reviewed by MAS | 100 |
| Auto-accepted (high confidence) | 74 |
| Escalated to human review | 26 |
| Human overrides | 3 |
| Human agreement rate with Judge | 88.5% |

The gold-labelled dataset (`hitl_green_100_final.csv`) was used to fine-tune PatentSBERTa for 3 epochs using CosineSimilarityLoss on an AMD Radeon RX 9070 XT via DirectML (fell back to CPU, completed in ~31 minutes).


## Performance Results

| Model Version | Training Data Source | F1 Score (Test Set) |
|---|---|---|
| 1. Baseline | Frozen Embeddings (No Fine-tuning) | 0.7494 |
| 2. Assignment 2 Model | Silver + Gold (Simple Generic LLM) | 0.7465 |
| 3. Assignment 3 Model | Silver + Gold (Advanced Techniques / MAS) | 0.7467 |
| 4. Final Model | Silver + Gold (QLoRA-Powered MAS + Targeted HITL) | 0.7530 |

The Final Model achieves the highest F1 score across all iterations, demonstrating that QLoRA domain adaptation combined with structured agent debate and targeted human review produces measurable improvements.


## Key Findings

**QLoRA advantages:**
- Adapts a generative LLM to patent language with only 0.53% of parameters trained
- Enables a domain-aware Judge that understands Y02 classification logic
- 4-bit quantization fits 8B model on a free 15 GB T4 GPU

**MAS + HITL advantages:**
- Debate structure surfaces disagreements that single-model approaches miss
- Exception-based HITL reduces human effort by 74% (26 vs 100 reviews)
- Gold labels are higher-quality than silver LLM labels alone

**Limitations:**
- DirectML (AMD GPU) not fully supported by sentence-transformers training — fell back to CPU
- torchao 0.16.0 conflicts with transformers lazy loader in certain environments


## Repository Contents

| File | Description |
|---|---|
| `Final_Assignment.ipynb` | Main notebook (Parts A–D) |
| `patentsberta_finetuned_final/` | Final fine-tuned PatentSBERTa model |
| `hitl_green_100_final.csv` | Gold dataset — 100 claims with HITL labels and debate rationales |
| `final_classifier.joblib` | Serialised final Logistic Regression classifier |
| `qlora_outputs.zip` | QLoRA adapter weights (`qlora_green_patent_adapter/`) |
| `Part C Step 1.ipynb` | Colab notebook for QLoRA fine-tuning |
| `Debate transcripts` | All debate transcrips for MAS |


## Related Repositories

- [Assignment 1 – ](https://github.com/Ory999/Portfolio-Assignment-1-M4-SGD-Mechanics-Attention-Context)
- [Assignment 2 – ](https://huggingface.co/Ory999/Assignment_2)
- [Assignment 3 – ](https://huggingface.co/Ory999/Assignment_3)

- Video link: https://aaudk-my.sharepoint.com/:v:/g/personal/dl02af_student_aau_dk/IQCPwPoohhSGQr_1T0-tmw7-AZHTGk2JIY01ZNHmYpGHiNQ?email=hamidb%40business.aau.dk&e=GYbwCw&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D