Samuel Iso

Understand how large language models work — tokens, context windows, and inference.

• Explain how transformers generate text
• Understand tokenisation and context limits
• Compare GPT-4, Claude, Gemini, Llama capabilities

Fine-tune open-source LLMs with LoRA/QLoRA for custom tasks and domain adaptation.

• Prepare fine-tuning datasets
• Run LoRA/QLoRA training with Unsloth or HF Trainer
• Evaluate and merge adapters
• Push models to Hugging Face Hub

Understand the ReAct loop, agent memory, and how tool-using agents work.

• Explain the ReAct pattern
• Build a simple tool-calling agent
• Implement short-term memory with conversation history

Understand and operate vector databases — indexing, metadata filtering, ANN.

• Set up Pinecone, Weaviate, or pgvector
• Implement metadata filtering
• Benchmark ANN recall vs latency

Generate images with Midjourney, DALL-E 3, and Stable Diffusion.

• Generate photorealistic and stylised images with prompts
• Use negative prompts and style parameters
• Understand model differences (Midjourney vs SD vs DALL-E)