🎯 Fine-Tuning Language Models

A comprehensive, beginner-friendly repository for fine-tuning large language models using modern techniques like LoRA, DPO, and Unsloth optimizations.

🚀 Quick Start (5 Minutes)

# Install dependencies
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

# Load and fine-tune in 3 lines
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained("unsloth/llama-3-8b-bnb-4bit")
model = FastLanguageModel.get_peft_model(model, r=16)

# Add your training code here

🧭 Navigation Guide

📁 Folder	🎯 Purpose	👥 Best For	⏱️ Time Needed
FineTuning LanguageModels	Basic concepts & simple examples	Beginners, students	1-2 hours
FineTuning LargeLanguageModels	Advanced techniques & model-specific guides	Practitioners, researchers	3-5 hours
FineTuning VisionModel	Vision-language model fine-tuning	Computer vision researchers	2-3 hours
Advanced FineTuning Methods	Multi-LoRA & LoRA composition techniques	Advanced researchers, ML engineers	3-4 hours
Task Specific FineTuning	Specialized task fine-tuning (Code generation)	Domain specialists, developers	2-3 hours
Reinforcement Learning Based FineTning	Human preference alignment (DPO, RLHF)	Advanced users, alignment researchers	2-4 hours
docs/	Comprehensive guides & troubleshooting	All skill levels	Reference

🤔 Which Technique Should I Use?

🔰 I'm New to Fine-Tuning

→ Start with Basic LoRA Fine-tuning

✅ Easy to understand
✅ Low memory requirements
✅ Good results for most tasks

💻 I Have Limited GPU Memory

→ Use 4-bit Quantized LoRA

✅ Works on Google Colab T4
✅ 70% less memory usage
✅ Minimal performance loss

🎯 I Want Better Instruction Following

→ Try DPO Training

✅ Improves response quality
✅ Better human alignment
✅ No reward model needed

🖼️ I Need Vision-Language Models

→ Use Vision Model Fine-tuning

✅ Image + text processing
✅ Mathematical formula recognition
✅ Multimodal AI applications

🧬 I Need Multiple Domain Expertise

→ Try Multi-LoRA Methods

✅ Multiple skills without forgetting
✅ Dynamic adapter switching
✅ Combine different expertises

💻 I Need Specialized Code Generation

→ Use Task-Specific Fine-tuning

✅ Advanced code generation
✅ Multi-language programming support
✅ Optimized with Qwen2.5-Coder

📊 Memory & Performance Guide

Model	Size	Min GPU Memory	Training Time (100 steps)	Best Use Case
Phi-3 Mini	3.8B	6GB	10 mins	Coding tasks, efficiency
Llama-3-8B	8B	12GB	20 mins	General conversation
Qwen2.5-7B	7.6B	10GB	18 mins	Multilingual, math
Qwen2-VL-7B	7B	8GB	25 mins	Vision-language tasks

💡 Tip: All memory requirements assume 4-bit quantization with LoRA

🛠️ Installation

Option 1: Google Colab (Recommended)

!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps "trl<0.9.0" transformers datasets

Option 2: Local Installation

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"

🔧 Troubleshooting

❌ Getting CUDA out of memory errors? → See Memory Optimization Guide

❌ Model not following instructions?
→ Try DPO Training

❌ Training too slow? → Check Performance Optimization Tips

📚 Learning Path

🛠️ Technologies

Core Technologies

Advanced Optimization

🤔 What is Fine-Tuning?

Fine-tuning is like teaching a smart student a new skill:

🧠 Pre-trained model = Student who already knows language
📚 Your dataset = Textbook for the new skill
⚙️ Fine-tuning = Practice sessions to master the skill
🎯 Fine-tuned model = Expert in your specific task

💡 Why Fine-Tuning?

Approach	Cost	Time	Data Needed	Results
Training from scratch	$1M+	Months	Billions of tokens	100%
Fine-tuning	$10-100	Hours	Thousands of examples	95%+

✅ 99% cost reduction while maintaining excellent performance!

🧠 Understanding the Process

Step 1: Choose Your Base Model

# Start with a pre-trained model
model = "unsloth/llama-3-8b-bnb-4bit"  # Already knows language

Step 2: Add Task-Specific Training

# Your specialized dataset
dataset = [
    {"instruction": "Explain photosynthesis", "output": "Photosynthesis is..."},
    {"instruction": "Write code", "output": "def function():..."}
]

Step 3: Efficient Training with LoRA

# Train only 1% of parameters
model = FastLanguageModel.get_peft_model(model, r=16)  # LoRA magic

🎯 Repository Overview

This repository contains everything you need to master fine-tuning, from beginner-friendly tutorials to advanced optimization techniques.

🔰 Beginner Level

Learn the fundamentals with hands-on examples and step-by-step guides.

🚀 Intermediate Level

Explore advanced techniques like DPO training and model optimization.

🔬 Expert Level

Master vision-language models and human preference alignment.

🆘 Need Help?

🐛 Bug reports: GitHub Issues
💬 Questions: GitHub Discussions
📚 Documentation: Check the docs/ folder
🤝 Contributing: See CONTRIBUTING.md

📄 License

MIT License - see LICENSE file for details.

⭐ Star this repo if it helped you fine-tune your models!

🔄 Full Fine-Tuning

Updates all model parameters during training. Requires high computational resources but achieves best task-specific performance.

⚡ Parameter-Efficient Fine-Tuning (PEFT)

Updates only a small subset of parameters with 90% reduction in computational cost while matching full fine-tuning performance.

📊 Quantization: Memory Optimization

Quantization reduces model weight precision from 32-bit to lower precision (8-bit, 4-bit), dramatically reducing memory usage while maintaining performance.

🎯 Precision Formats

FP32: 4 bytes per parameter (baseline)
INT8: 1 byte per parameter (75% memory reduction)
INT4: 0.5 bytes per parameter (87.5% memory reduction)

📈 Example: 7B Parameter Model

FP32: 7B × 4 bytes = 28 GB
INT8: 7B × 1 byte = 7 GB (75% reduction)
INT4: 7B × 0.5 bytes = 3.5 GB (87.5% reduction)

🎯 LoRA: Low-Rank Adaptation

LoRA decomposes weight updates into low-rank matrices, reducing trainable parameters by 99% while maintaining performance.

📐 Mathematical Foundation

W' = W + ΔW
ΔW = A × B

W: Original pre-trained weights (frozen)
A: Low-rank matrix (d × r)
B: Low-rank matrix (r × k)
r: Rank (much smaller than d or k)

🔢 Rank Selection Guidelines

r = 8-16: Standard choice, good balance
r = 32-64: Complex adaptations
r = 128+: Approaching full fine-tuning

📊 Parameter Reduction Example

4096 × 4096 layer with r=16:

Original: 4096 × 4096 = 16,777,216 parameters
LoRA: (4096 × 16) + (16 × 4096) = 131,072 parameters
Reduction: 99.2% fewer parameters

🔧 Adapters: Modular Fine-Tuning

Small neural network modules inserted between transformer layers for task-specific adaptation.

🏗️ Architecture

Input → Layer Norm → Adapter → Residual Connection → Output

⚖️ LoRA vs Adapters

Aspect	LoRA	Adapters
Parameter Count	0.1-1%	2-4%
Training Speed	Faster	Moderate
Modularity	Limited	High

📁 Repository Structure

This repository contains six specialized folders: