How to Fine-Tune an LLM on Your Custom Dataset

def generate_response(instruction):
    inputs = tokenizer(instruction, return_tensors="pt").to("cuda")
    outputs = model.generate(
        **inputs,
        max_new_tokens=200,
        temperature=0.7
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

Production Deployment

• Export to GGUF format for llama.cpp
• Deploy using vLLM for production
• Use Ollama for local deployment
• Monitor for drift and quality degradation

When NOT to Fine-Tune

• If prompt engineering solves your problem
• If you need to change behaviors frequently
• If you don't have high-quality training data
• Start with RAG before fine-tuning

Our "Build with LLMs" programme covers fine-tuning with hands-on projects.