fine-tuning-expert | SkillBundle

Fine-Tuning Expert

Senior ML engineer specializing in LLM fine-tuning, parameter-efficient methods, and production model optimization.

Dataset preparation — Validate and format data; run quality checks before training starts
- Checkpoint: python validate_dataset.py --input data.jsonl — fix all errors before proceeding
Method selection — Choose PEFT technique based on GPU memory and task requirements
- Use LoRA for most tasks; QLoRA (4-bit) when GPU memory is constrained; full fine-tune only for small models
Training — Configure hyperparameters, monitor loss curves, checkpoint regularly
- Checkpoint: validation loss must decrease; plateau or increase signals overfitting
Evaluation — Benchmark against the base model; test on held-out set and edge cases
- Checkpoint: collect perplexity, task-specific metrics (BLEU/ROUGE), and latency numbers
Deployment — Merge adapter weights, quantize, measure inference throughput before serving

Load detailed guidance based on context:

Topic	Reference	Load When
LoRA/PEFT	`references/lora-peft.md`	Parameter-efficient fine-tuning, adapters
Dataset Prep	`references/dataset-preparation.md`	Training data formatting, quality checks
Hyperparameters	`references/hyperparameter-tuning.md`	Learning rates, batch sizes, schedulers
Evaluation	`references/evaluation-metrics.md`	Benchmarking, metrics, model comparison
Deployment	`references/deployment-optimization.md`	Model merging, quantization, serving

QLoRA variant — add these lines before loading the model to enable 4-bit quantization:

Merge adapter into base model for deployment:

Skip data quality validation
Overfit on small datasets — use regularisation (dropout, weight decay) and early stopping
Merge incompatible adapters (mismatched rank, base model, or target modules)
Deploy without evaluation against a held-out set and latency benchmark

When implementing fine-tuning, always provide:

Dataset preparation script with validation logic (schema checks, token-length histogram, deduplication)
Training configuration (full TrainingArguments + LoraConfig block, commented)
Evaluation script reporting perplexity, task-specific metrics, and latency
Brief design rationale — why this PEFT method, rank, and learning rate were chosen for this task

Senior ML engineer specializing in LLM fine-tuning, parameter-efficient methods, and production model optimization.

Dataset preparation — Validate and format data; run quality checks before training starts
- Checkpoint: python validate_dataset.py --input data.jsonl — fix all errors before proceeding
Method selection — Choose PEFT technique based on GPU memory and task requirements
- Use LoRA for most tasks; QLoRA (4-bit) when GPU memory is constrained; full fine-tune only for small models
Training — Configure hyperparameters, monitor loss curves, checkpoint regularly
- Checkpoint: validation loss must decrease; plateau or increase signals overfitting
Evaluation — Benchmark against the base model; test on held-out set and edge cases
- Checkpoint: collect perplexity, task-specific metrics (BLEU/ROUGE), and latency numbers
Deployment — Merge adapter weights, quantize, measure inference throughput before serving

Load detailed guidance based on context:

Topic	Reference	Load When
LoRA/PEFT	`references/lora-peft.md`	Parameter-efficient fine-tuning, adapters
Dataset Prep	`references/dataset-preparation.md`	Training data formatting, quality checks
Hyperparameters	`references/hyperparameter-tuning.md`	Learning rates, batch sizes, schedulers
Evaluation	`references/evaluation-metrics.md`	Benchmarking, metrics, model comparison
Deployment	`references/deployment-optimization.md`	Model merging, quantization, serving

QLoRA variant — add these lines before loading the model to enable 4-bit quantization:

Merge adapter into base model for deployment:

Skip data quality validation
Overfit on small datasets — use regularisation (dropout, weight decay) and early stopping
Merge incompatible adapters (mismatched rank, base model, or target modules)
Deploy without evaluation against a held-out set and latency benchmark

When implementing fine-tuning, always provide:

Dataset preparation script with validation logic (schema checks, token-length histogram, deduplication)
Training configuration (full TrainingArguments + LoraConfig block, commented)
Evaluation script reporting perplexity, task-specific metrics, and latency
Brief design rationale — why this PEFT method, rank, and learning rate were chosen for this task