Truthseeker87/llama-tinker-lora-news-enhancer

🤖 LLM-Powered News Content Enhancer

Fine-tuning LLaMA 3.2-1B with LoRA for Semantic Newsletter Analysis

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🎯 Project Overview

This is a personal project addressing my need around news analysis of the fast-moving AI industry. I collect AI-related news content from various sources and needed a way to enhance raw news content with rich semantic metadata. This enables deeper, structured analysis and pattern-finding across trends and developments in the AI industry.

What This Project Does

• Enhance and add rich semantic metadata to AI-related news content as part of my news analyst application system
• Explore, test and compare Thinking Machines' Tinker's managed training & finetuning API service

Why This Matters

• 3-5x better content organization through semantic understanding
• 10x more metadata extracted from newsletter content
• 2-3x better cross-newsletter insights for knowledge synthesis
• 40-60% richer NotebookLM outputs for analysis and research

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🏗️ System Architecture

This fine-tuning project is part of a larger News Analyst MCP Agent system:

┌─────────────────────────────────────────────────────────────┐
│                   News Analyst MCP Agent                    │
│  (Production system for automated newsletter processing)    │
└────────────────────┬────────────────────────────────────────┘
                     │
                     ▼
         ┌───────────────────────┐
         │  LLM Enhancement Layer │ ← This Project
         │  (Fine-tuned LoRA)     │
         └───────────────────────┘
                     │
        ┌────────────┴────────────┐
        ▼                         ▼
┌───────────────┐         ┌──────────────┐
│ Tinker LoRA   │         │ Unsloth LoRA │
│ (Cloud-based) │         │ (Local)      │
└───────────────┘         └──────────────┘

Integration Context:

• Deployment: Local Windows Surface Pro (Intel Iris Xe, 16GB RAM)
• Base Model: LLaMA 3.2-1B (1 billion parameters, optimized for edge devices)
• Architecture: LoRA adapters for parameter-efficient fine-tuning
• Production System: See docs/NEWS_ANALYST_SYSTEM_ARCHITECTURE.md

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🔬 LoRA vs Full Fine-Tuning Comparison

This project uses LoRA (Low-Rank Adaptation) instead of full fine-tuning for several critical reasons:

Criterion	LoRA	Full Fine-Tuning	Winner
Memory (Colab T4)	2.5GB	17GB (exceeds 15GB limit)	✅ LoRA
Training Speed	30-45 min	60-90 min (if feasible)	✅ LoRA
Model Size	50-100MB adapter	1.2GB full model	✅ LoRA
Parameter Efficiency	0.5% trainable	100% trainable	✅ LoRA
Catastrophic Forgetting	Low risk	High risk	✅ LoRA
Data Requirements	50-200 examples	500-5000 examples	✅ LoRA
Performance	90-95% of full FT	100% (theoretical)	⚠️ Full FT

Verdict: LoRA achieves 9.2/10 weighted score vs 6.0/10 for full fine-tuning.

Key Advantages:

• ✅ Fits within Google Colab free tier (T4 GPU, 15GB VRAM)
• ✅ Deployable on limited hardware (Intel Iris Xe, 16GB RAM)
• ✅ Preserves general language capabilities
• ✅ 99.5% parameter reduction (1-5M trainable vs 1.2B total)

See docs/LORA_COMPARISON.md for detailed analysis.

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📊 Results

Model Comparison

Comprehensive Model Evaluation

Advanced Metrics Comparison

Performance Comparison Table

Model	Quality Score	JSON Valid	ROUGE-1	BERTScore	Consistency (CV)	Training Time
Tinker LoRA	0.8674	100%	0.7714	0.9649	0.0% ✅	2.65 min
Unsloth LoRA	0.2664	0%	0.0311	0.7721	156.3%	0.94 min
Base Model	0.3302	0%	0.0501	0.8003	45.2%	N/A

Winner: Tinker LoRA achieved best performance across all metrics.

Key Findings

1. Tinker LoRA: Perfect consistency, 100% valid JSON, highest semantic similarity
2. Unsloth LoRA: Fast training but inconsistent outputs (placeholder text issue)
3. Base Model: Verbose markdown output, doesn't follow JSON format

Performance Metrics

• Quality Score: Composite metric (ROUGE + BERTScore + JSON validation)
• JSON Validation: Schema compliance for structured output
• ROUGE-1: N-gram overlap with reference summaries
• BERTScore: Semantic similarity using contextual embeddings
• Consistency (CV): Coefficient of variation (lower is better)

See results/reports/evaluation_report.md for detailed analysis.

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🚀 Quick Start

Prerequisites

# Python 3.8+
python --version

# CUDA-capable GPU (for training) or CPU (for inference)
nvidia-smi  # Optional: Check GPU availability

Installation

# Clone repository
git clone https://github.com/youshen-lim/llama-tinker-lora-newsletter.git
cd llama-tinker-lora-newsletter

# Install dependencies
pip install -r requirements.txt

Training Data

• Training examples: 101 annotated newsletters
• Test examples: 20 newsletters
• Format: JSONL with user/assistant message pairs
• Annotation: Custom widget for manual annotation

# View training data
head -n 5 data/processed/newsletter_train_data.jsonl

Fine-Tuning

Option 1: Tinker API (Recommended)

# See notebooks/News_Analyst_1_Notebook.ipynb for complete workflow
# Training time: ~2.65 minutes for 3 epochs

Option 2: Unsloth (Local)

# See notebooks/News_Analyst_1_Notebook.ipynb for complete workflow
# Training time: ~0.94 minutes for 3 epochs

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "models/tinker/")
model = model.merge_and_unload()

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B")

# Run inference
newsletter = "Your newsletter text here..."
inputs = tokenizer(newsletter, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=500)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

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📁 Project Structure

newsletter-finetuning/
├── README.md                                    # This file
├── LICENSE                                      # MIT License
├── .gitignore                                   # Git ignore rules
├── requirements.txt                             # Python dependencies
│
├── notebooks/
│   ├── News_Analyst_1_Notebook.ipynb           # Main fine-tuning workflow
│   └── JSONL_Annotation_Notebook_Final.ipynb   # Annotation tool
│
├── scripts/
│   └── news_analyst_1_notebook.py              # Python script version
│
├── data/
│   └── processed/
│       ├── newsletter_train_data.jsonl         # Training data (101 examples)
│       └── newsletter_test_data.jsonl          # Test data (20 examples)
│
├── models/
│   ├── tinker/                                 # Tinker LoRA adapter
│   ├── unsloth/                                # Unsloth LoRA adapter
│   └── baseline/                               # Base model info
│
├── results/
│   ├── metrics/                                # Evaluation metrics
│   ├── visualizations/                         # Charts and graphs
│   └── reports/                                # Evaluation reports
│
└── docs/
    ├── NEWS_ANALYST_SYSTEM_ARCHITECTURE.md     # Production system overview
    ├── LORA_COMPARISON.md                      # LoRA vs full fine-tuning
    ├── FINE_TUNING_CONFIGURATION.md            # Model configuration
    ├── EVALUATION_METHODOLOGY.md               # Evaluation metrics
    ├── DATA_PREPARATION.md                     # Data annotation process
    ├── TINKER_TRAINING_GUIDE.md                # Tinker API guide
    ├── MODEL_DEPLOYMENT.md                     # Deployment instructions
    └── TROUBLESHOOTING.md                      # Common issues and fixes

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📚 Documentation

Core Documentation

• System Architecture - How this fits into the larger news analyst system
• LoRA Comparison - Why LoRA was chosen over full fine-tuning
• Fine-Tuning Configuration - Model and training parameters

Guides

• Data Preparation - Annotation process and data formatting
• Tinker Training - Using Tinker API for fine-tuning
• Model Deployment - Local deployment instructions
• Evaluation Methodology - Metrics and evaluation process
• Troubleshooting - Common issues and solutions

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🛠️ Technologies Used

• Base Model: LLaMA 3.2-1B by Meta AI
• Fine-Tuning Method: LoRA (Low-Rank Adaptation) via PEFT
• Training Platforms:
  • Tinker API - Managed fine-tuning service
  • Unsloth - Optimized local fine-tuning
• Evaluation: ROUGE, BERTScore, Sentence-BERT, JSON schema validation
• Deployment: Local inference with Transformers

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🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• Meta AI for LLaMA 3.2-1B base model
• Thinking Machines for Tinker API managed fine-tuning service
• Unsloth for optimized local fine-tuning library
• Hugging Face for Transformers and PEFT libraries

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📧 Contact

Aaron (Youshen) Lim - @youshen-lim

Project Link: https://github.com/youshen-lim/llama-tinker-lora-newsletter

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