DOMAIN LLM SYSTEMS
Building a continued-pretraining path for semiconductor language models
Training architecture, domain data preparation, parameter-efficient adaptation and functional evaluation planning for specialised RTL and hardware-design tasks.
- Continued pretraining
- LLM evaluation
- Verilog
- Training infrastructure
Context
Specialist semiconductor and hardware-design workflows depend on terminology, structure and correctness criteria that general-purpose language models handle inconsistently. The engagement focused on establishing a technically sound path for domain adaptation rather than a one-off demo.
Challenge
Define a practical continued-pretraining and adaptation route for domain language models used on RTL-related and hardware-design tasks, while keeping evaluation grounded in functional usefulness rather than training loss alone.
Constraints
- Domain data required careful preparation and quality controls
- Training and adaptation needed to fit practical compute budgets
- Evaluation had to reflect task behaviour, not only proxy metrics
- Sensitive implementation details could not be disclosed publicly
Approach
- Mapped domain corpus characteristics and task families
- Designed data-preparation and filtering stages for continued pretraining
- Selected base-model candidates with inference and adaptation trade-offs in mind
- Planned parameter-efficient adaptation after domain exposure
- Defined functional evaluation paths for specialised technical tasks
- Documented training architecture and iteration checkpoints
Domain corpus
Token prep
Base model
Continued pretraining
Adapter layer
RTL evaluation
Checkpoint
The system path moved from domain corpus preparation into base-model selection, continued pretraining, adapter-based specialisation and functional evaluation. Deployment planning stayed downstream of evidence from evaluation gates.
Evaluation strategy
- Domain-task probes for specialised language and structure
- Functional checks aligned to RTL and hardware-design usage patterns
- Comparison against general-model baselines on representative tasks
- Failure-mode review for hallucination, format and domain errors
Outcomes
- A documented continued-pretraining and adaptation path for semiconductor language-model work
- Clearer separation between data readiness, training design and evaluation gates
- A practical foundation for iterative model improvement under real constraints
What was learned
- Domain adaptation work fails early when evaluation is deferred until after expensive training runs
- Corpus quality and task definition shape outcomes more than model-family branding
- Parameter-efficient methods are most useful when domain exposure and evaluation are already coherent
Technologies
- Python
- PyTorch
- Transformers
- Parameter-efficient fine-tuning
- Domain evaluation harnesses
- Training infrastructure
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