Services
Training and tuning
Fine-tuning, training workflows and runtime optimization for your task. Start with a reproducible baseline and finish with explicit artifacts and acceptance evidence.
The recorded case
The published Enthalpy case covers pool measurement, Stage 1 scoring-head training and held-out evaluation. The benchmarks also retain early failures, cross-pool limitations and the Stage 2 smoke run.
Engineering field guide
From a task baseline to a reproducible handoff.
Model quality, runtime efficiency and training infrastructure are different problems. Define the metric before choosing data, training or optimization work.
Choose the tuning path
Use representative tasks to locate the problem before assuming the weights need to change.
- Outputs miss the task
- Inspect data coverage, label consistency, prompts and scoring before evaluating fine-tuning or alignment. Reserve acceptance data outside training and configuration selection.
- Correct outputs, slow execution
- Profile kernels, memory, data movement and batching. Performance work also checks numerical agreement, rather than reporting only throughput on favorable inputs.
- Experiments are difficult to reproduce or resume
- Version the environment, data, configurations, checkpoints and logs. Make existing experiments reproducible before scaling the run.
The tuning loop
Every experiment should say what changed, why it changed, what happened and how to revert it.
Define data & baseline
Freeze sources, deduplication, splitting, scoring and the reference model. Record quality, runtime and resource usage together.
Run controlled experiments
Track data, training and runtime changes separately and retain unsuccessful experiments. Choose the training method and hardware around the model, permissions and budget.
Validate & package
Use agreed acceptance metrics, including regressions, limitations and rollback. Hand over weights or adapters together with configuration and operating notes.
More than a weight file
The agreed deliverables should support reruns, deployment decisions and maintenance.
- Data & experiment manifest
- Data versions, provenance and permitted use, split strategy, dependencies, key parameters, seeds and experiment records.
- Model or optimization implementation
- Agreed checkpoints, adapters or performance changes with loading instructions, compatibility requirements and a rollback version.
- Comparison report
- Compare against the original baseline under the same conditions. Include quality differences, runtime cost, failure categories and limits on generalization.
Set the scope before the run
A published case demonstrates the measurement process. Your task still needs its own baseline.
What does the Enthalpy case establish?
The positive published result is a Stage 1 checkpoint on CodeRouterBench. Stage 2 has a smoke-run record; cross-pool results do not establish general transfer gains. Read the complete benchmarks.
Will fine-tuning necessarily improve the model?
No outcome is assumed. Establish the gap and acceptable regressions on task data first. A change that fails acceptance should not be presented as a deployment result.
What should I prepare?
The base model and permissions, training and acceptance samples, business scoring rules, runtime environment, resource budget and capabilities that must be preserved.
Continue reading
Read the training evidence and its limits4ROUTER / SYSTEMS RESEARCH
Technical foundations of the service
Understand how operators, KV state and scheduling shape an inference path.
Read the systems notes in Resources ↗From operators to serving: cuBLAS & cuDNN
Identify the limiting resource before selecting the implementation.
↗ATTENTION / MEMORYFlashAttention & PagedAttention: different memory problems
One optimizes data movement; the other organizes persistent state.
↗STATE / CAPACITYKV cache: from tensor shapes to capacity budgets
Context length is a state budget that grows with concurrency.
↗Let’s talk infrastructure.
Connect to the platform, or discuss models, performance and deployment.