LoRA / Parameter-Efficient Fine-Tuning
Our model-specialization layer supports parameter-efficient fine-tuning for workloads where general-purpose inference does not provide sufficient consistency, specialization, or production economics.
Used selectively
We use LoRA and related PEFT methods selectively when a recurring workload has stable task boundaries, representative training data, measurable evaluation criteria, and a demonstrated advantage over simpler approaches such as prompting, retrieval, few-shot conditioning, or deterministic processing.
Change the minimum amount of model behavior necessary to produce a measurable production improvement.
Architecture
For a base transformation W0, LoRA introduces a learned low-rank update:
W = W0 + ΔW
ΔW = BA
where A and B are trainable low-rank matrices while the underlying base weights remain frozen.
Our adaptation path can control base-model selection, target modules, adapter rank and scaling, dataset composition, training configuration, quantization state, evaluation thresholds, and deployment runtime. Rank and target-module selection are workload-specific. We do not assume that larger adapters or broader adaptation produce better results.
Evaluation and lifecycle
Adapters are deployed only after workload-specific comparison against relevant alternatives:
- Prompt-only
- Few-shot
- Retrieval-augmented
- PEFT-adapted
- Alternative model
- Deterministic implementation
Evaluation can include accuracy, consistency, structured-output validity, latency, inference cost, and regression behavior.
Each production adapter remains linked to its workload, dataset version, base-model version, training configuration, evaluation result, and deployed artifact. A base-model change therefore requires compatibility and regression evaluation rather than assuming an existing adapter remains valid.
PEFT reduces training requirements without necessarily reducing the compute required to serve the underlying base model. Quantization, model size, routing, and inference infrastructure remain separate production decisions.
We do not use adaptation to solve changing company facts, retrieval deficiencies, poor data quality, deterministic problems, or workloads without adequate evaluation data.
Specialize only where specialization produces a measurable production advantage.
Adapter management
Parameter efficiency reduces the size of the trainable update; it does not remove lifecycle complexity.
Where multiple adapters share a base model, the serving layer must preserve compatibility between the adapter artifact, base-model version, tokenizer, quantization state, and runtime. A small adapter attached to the wrong base-model revision is not a valid deployment merely because the tensors load.
Adapters can also create routing decisions of their own. A narrow classification adapter may be appropriate for one task while another workload should bypass adaptation entirely. For this reason, we treat PEFT artifacts as workload-scoped components inside the model-routing layer rather than as generic upgrades to the underlying model.