Simplified Pathway to Model Finetuning

Month 1: Foundations + First Fine-Tune

Goal: Learn Hugging Face + PyTorch basics and fine-tune your first model.

Skills

Python (NumPy, Pandas, basic classes).

PyTorch basics (tensors, training loops).

Transformers (attention, embeddings, tokenization).

Hugging Face: transformers, datasets, peft.

Projects

Fine-tune DistilBERT on sentiment classification.

Fine-tune a small LLaMA/Mistral model for Q&A.

Milestone:

✅ You can load a model from Hugging Face, fine-tune it, evaluate it, and push it back to the Hugging Face Hub.

Month 2: Fine-Tuning Mastery

Goal: Practice multiple fine-tuning strategies + domain applications.

Skills

LoRA / QLoRA (parameter-efficient fine-tuning).

Adapters, prompt-tuning.

Vector databases (FAISS) + Retrieval-Augmented Generation (RAG).

Projects

Fine-tune LLaMA or Mistral with LoRA for domain-specific chatbot (e.g., customer support, medical, or legal).

Build a RAG pipeline (fine-tuned model + vector DB).

Fine-tune Stable Diffusion with DreamBooth for custom branding.

Milestone:
✅ You can adapt models to specific industries and optimize GPU cost with LoRA/QLoRA.

Month 3: Deployment + Portfolio

Goal: Learn to deploy fine-tuned models + build job-ready portfolio.

Skills

Model serving (Hugging Face Inference API, AWS Sagemaker, Docker).

Quantization for cheaper inference.

Experiment tracking (Weights & Biases).

Projects

Deploy your fine-tuned chatbot as an API or web app.

Optimize with quantization (run on CPU or small GPU).

Create a portfolio repo with 3–4 end-to-end fine-tuning demos.

Milestone:
✅ You have public projects + live demos proving you can fine-tune, optimize, and deploy models.

📊 Condensed Timeline

Month 1: Learn → fine-tune first models (DistilBERT + LLaMA).

Month 2: Master fine-tuning techniques (LoRA, QLoRA, RAG, multimodal).

Month 3: Deploy + portfolio (live API, Hugging Face Hub, blog posts).