What it teaches
Adapters are one of the most practical model-breeding operators. A small adapter can encode a skill without copying a full model. An adapter stack planner helps teams choose base family, adapter order, rank, density, local memory budget, and evaluation scope.
Planner fields
| Field | Example |
|---|---|
| Base model | tinylm-0.5b-instruct |
| Adapter 1 | legal-summary-r8 |
| Adapter 2 | citation-style-r4 |
| Compatibility | same tokenizer and tensor layout |
| Local budget | 900 MB memory, 700 ms p95 latency |
| Fitness dimensions | utility, citation quality, latency, local privacy, human benefit |
| Release target | specialist, shadow stage |
Pseudocode
PROCEDURE plan_adapter_stack(base, adapters, niche)
REQUIRE SAME_TOKENIZER_AND_LAYOUT(base, adapters)
stack <- ORDER_ADAPTERS_BY_NICHE_VALUE(adapters, niche)
footprint <- ESTIMATE_RUNTIME_FOOTPRINT(base, stack)
evidence <- DEFINE_FITNESS_VECTOR(niche, footprint)
RETURN ADAPTER_STACK_PLAN(base, stack, evidence)
END PROCEDURENext step
Use the Fitness Scorecard Calculator to compare the planned stack with the current champion.
Source reports used for this guide
These reports are preserved verbatim in the site archive. The guide above is an editorial synthesis and may narrow, qualify, or reorganize claims from the source material.
Architecture and runtime explorationModelBreeder Architecture ExplorationSource report · 48.9 KB
Model merging and adaptersTiny-LLM Adapters and LoRA: Breeding Operators, Compatibility, Evaluation, and PromotionEmerging implementation practice · 15.3 KB
Browser and edge runtimeArchitectural Advancements for Zero-Dependency In-Browser Large Language ModelsEmerging implementation architecture · 54.0 KB