Next Steps

Congratulations! You've learned how to fine-tune LLMs using LoRA, QLoRA, and modern tools like Unsloth and TRL. Let's recap and explore where to go next.

What You've Learned

Module 1: Understanding Fine-tuning

• Why and when to fine-tune vs use prompting
• Full fine-tuning vs PEFT methods
• LoRA, QLoRA, and DoRA comparisons

Module 2: Dataset Preparation

• Instruction tuning formats (Alpaca, ShareGPT, ChatML)
• Creating quality training data
• Data validation and cleaning

Module 3: LoRA & QLoRA in Practice

• Configuring LoRA parameters (r, alpha, target_modules)
• 4-bit quantization with QLoRA
• Complete SFTTrainer workflows

Module 4: Training with Unsloth

• 2x faster training with 70% less VRAM
• Optimized training configurations
• Exporting to GGUF format

Module 5: Alignment with DPO

• Direct Preference Optimization vs RLHF
• Creating preference datasets
• Two-stage SFT + DPO pipeline

Module 6: Evaluation & Deployment

• Measuring model quality
• Diagnosing common issues
• Deploying to Ollama

Your Fine-tuning Toolkit

Tool	Purpose
transformers	Base model loading and training
peft	LoRA/QLoRA implementation
trl	SFTTrainer and DPOTrainer
bitsandbytes	4-bit quantization
unsloth	2x faster training
datasets	Data loading and processing
ollama	Local model deployment

Recommended Learning Path

Based on what you've learned, here are your next steps:

Immediate: Practice What You've Learned

1. Fine-tune a model on your own data
   └── Pick a specific use case
   └── Prepare 1,000+ examples
   └── Train with Unsloth + SFT
   └── Evaluate and iterate

2. Deploy to Ollama
   └── Export to GGUF
   └── Create custom Modelfile
   └── Integrate into your application

Next Course: AI Evaluation Frameworks

After fine-tuning comes evaluation. Learn to systematically measure AI quality:

AI Evaluation Frameworks: RAGAS, LangSmith & Custom Metrics

What you'll learn:

• Automated evaluation pipelines
• RAG-specific metrics (RAGAS)
• Custom evaluation criteria
• A/B testing frameworks
• Production monitoring

This course connects directly to your fine-tuning work:

• Measure if fine-tuning actually improved your model
• Compare different training configurations
• Build evaluation into your ML pipeline
• Monitor model quality in production

Advanced Topics to Explore

Topic	Description
Multi-GPU Training	Scale to larger models and datasets
Continued Pre-training	Adapt base models to new domains
Mixture of Experts	Train efficient large models
Vision-Language Models	Fine-tune multimodal models
Model Merging	Combine multiple fine-tuned models

Building Your Portfolio

Project Ideas

1. Domain Expert Bot

   • Fine-tune on technical documentation
   • Deploy as Q&A assistant
   • Measure accuracy improvements

2. Style Transfer Model

   • Train on specific writing styles
   • Create consistent brand voice
   • A/B test against base model

3. Code Assistant

   • Fine-tune on your codebase
   • Learn team conventions
   • Integrate with IDE

4. Customer Support Agent

   • Train on support tickets
   • Handle domain-specific queries
   • Reduce response time

Sharing Your Work

# Push fine-tuned model to Hugging Face
huggingface-cli login
model.push_to_hub("your-username/model-name")

# Share GGUF via Ollama
ollama push your-username/model-name

# Document your process
# - Training configuration
# - Dataset description
# - Evaluation results
# - Deployment guide

Resources

Documentation

• Hugging Face PEFT
• TRL Library
• Unsloth GitHub
• Ollama Docs

Research Papers

• LoRA: Low-Rank Adaptation of Large Language Models
• QLoRA: Efficient Finetuning of Quantized LLMs
• DPO: Direct Preference Optimization

Community

• Hugging Face Discord
• r/LocalLLaMA
• Unsloth Discord

Final Thoughts

Fine-tuning is a powerful technique that bridges the gap between general-purpose LLMs and specialized AI assistants. With the tools you've learned:

• LoRA makes fine-tuning accessible on consumer hardware
• QLoRA enables training models that previously required enterprise GPUs
• Unsloth cuts training time and costs in half
• DPO aligns models without complex RL pipelines
• Ollama makes deployment as simple as a single command

The key to success is iteration:

1. Start with a small dataset
2. Train quickly with Unsloth
3. Evaluate rigorously
4. Improve your data
5. Repeat

Remember: Data quality beats quantity. A well-curated dataset of 1,000 examples will outperform 10,000 noisy ones. Focus on your specific use case and iterate based on evaluation results.

Good luck with your fine-tuning journey!

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Ready to continue learning? Check out our AI Evaluation Frameworks course to master the art of measuring AI quality. :::