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

# 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

→ Start with Basic LoRA Fine-tuning

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

→ Use 4-bit Quantized LoRA

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

→ Try DPO Training

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

→ Use Vision Model Fine-tuning

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

→ Try Multi-LoRA Methods

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

→ Use Task-Specific Fine-tuning

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

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

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

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"

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

❌ Model not following instructions?
→ Try DPO Training

❌ Training too slow? → Check Performance Optimization Tips

1. Week 1: Basic Fine-tuning Concepts
2. Week 2: LoRA and PEFT Techniques
3. Week 3: Vision-Language Models
4. Week 4: Human Preference Alignment
5. Week 5: Advanced Multi-LoRA Methods
6. Week 6: Task-Specific Applications

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

✅ 99% cost reduction while maintaining excellent performance!

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

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

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

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

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

Explore advanced techniques like DPO training and model optimization.

Master vision-language models and human preference alignment.

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

MIT License - see LICENSE file for details.

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

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

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

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

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

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

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)

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

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

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

Input → Layer Norm → Adapter → Residual Connection → Output

This repository contains six specialized folders:

Foundational fine-tuning techniques and inference examples for beginners.

Advanced model-specific fine-tuning using UnSloth, LoRA, and quantization techniques.

Vision-language model fine-tuning for multimodal AI applications.

Cutting-edge Multi-LoRA and LoRA composition techniques for multiple domain expertise.

Specialized fine-tuning for domain-specific tasks like code generation.

Human preference alignment using Direct Preference Optimization (DPO) and RLHF.

# Clone repository
git clone https://github.com/Abeshith/FineTuning_LanguageModels.git
cd FineTuning_LanguageModels

# Install dependencies
pip install transformers datasets torch torchvision
pip install unsloth peft bitsandbytes trl accelerate