LLM Fine-tuning

Understands the concept of LLM fine-tuning: full fine-tuning vs parameter-efficient methods. Uses Hugging Face API for fine-tuning small models on custom data. Prepares training data in the correct format for various LLM platforms.

Knows LLM fine-tuning basics: full fine-tuning vs LoRA, instruction-tuning data format. Runs basic fine-tuning of a small model via Hugging Face Trainer under mentor guidance.

Independently conducts LLM fine-tuning using LoRA, QLoRA, and prefix-tuning. Configures training hyperparameters, monitors loss curves. Evaluates fine-tuned model quality through domain-specific benchmarks and human evaluation.

Independently conducts LLM fine-tuning: LoRA/QLoRA, instruction dataset preparation, hyperparameter tuning. Monitors training via W&B, evaluates results on held-out datasets.

Designs fine-tuning pipelines for production LLM systems. Applies RLHF, DPO for model alignment. Optimizes training through DeepSpeed, FSDP. Conducts systematic evaluation via automated benchmarks and red-teaming.

Designs production fine-tuning pipelines: data curation, multi-stage training (SFT → DPO), distributed fine-tuning. Optimizes LoRA rank, learning rate, and batch size for maximum quality.

Defines LLM fine-tuning strategy for the organization. Establishes data preparation, training, and evaluation standards for custom LLMs. Coordinates GPU infrastructure and budgets for LLM experiments.

Defines fine-tuning strategy for the LLM team. Establishes best practices for data preparation, training configuration, evaluation. Coordinates fine-tuning experiments and model selection process.

Shapes custom LLM development strategy at organizational level. Defines buy vs build for LLM, evaluates open-source vs proprietary models. Influences industry through publications and open-source contributions.

Shapes enterprise fine-tuning platform. Defines approaches to automated fine-tuning, model versioning, and A/B testing of fine-tuned models. Optimizes cost and speed of fine-tuning at scale.