Fine-Tuning

Benched.ai Editorial Team

Fine-tuning adapts a pre-trained model to a specific domain or task by continuing gradient updates on a smaller dataset.

Fine-Tuning Flavors

Flavor	Trainable Params	Typical Dataset	Example Use
Full weight	100 %	50 k–1 M examples	Domain experts with GPUs
Parameter-efficient (LoRA)	<1 %	5 k–100 k	SaaS adapters
Prompt-tuning	<0.01 %	<5 k	Mobile on-device
Delta weights	Changed tensors only	5 k–100 k	Community patches

Resource Comparison (7 B model, 1 epoch)

Method	GPU Hours	VRAM	Quality Δ vs full
Full	120	32 GB	—
LoRA (rank 16)	8	8 GB	−1.5 BLEU
Prompt-tune	1	6 GB	−4 BLEU

Design Trade-offs

Full fine-tune maximizes quality but risks catastrophic forgetting.
LoRA lowers cost but adds runtime adapters.
Prompt-tuning keeps base weights frozen; ideal for on-device personas.

Current Trends (2025)

FP8 optimizer states cut memory 40 %.
Alignment-aware fine-tuning adds reward-model logits as auxiliary loss.
LoRA v3 introduces merged adapters that remove additional matmuls at inference.

Implementation Tips

Start with LoRA rank 8; increase if eval drops >2 points.
Evaluate on out-of-domain safety prompts to catch regressions.
Distribute only diff checkpoints to respect restrictive open-weight licenses.