---

name: adapter-core description: "Core PEFT adapter setup, configuration, target module selection, adapter lifecycle, model/layer status diagnostics, custom models, and low-level adapter injection." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Adapter Core

Use this sub-skill when the task is about constructing PEFT configs, wrapping a model with adapters, managing adapters after creation, diagnosing adapter state, targeting modules in custom models, or using low-level adapter injection.

Route Here For

• Install/import smoke checks for peft, torch, transformers, and accelerate without downloading models.
• Core API choices: PeftConfig, TaskType, PeftType, get_peft_model, PeftModel, and adapter names.
• Adapter lifecycle: add, load, activate, disable, delete, inspect trainable parameters, and inspect model/layer status.
• Target module selection for custom or non-Transformers torch.nn.Module models.
• Low-level injection with inject_adapter_in_model when a PeftModel wrapper is not desired.
• Diagnosing invalid task/PEFT types, missing target modules, unsupported module classes, or unexpected trainable status.

Route Elsewhere

• LoRA variants, quantized loading, DoRA, rsLoRA, QLoRA-style workflows, and LoRA-specific initialization: use ../lora-and-quantization/SKILL.md.
• Prompt tuning, prefix tuning, p-tuning, soft prompts, or prompt encoder internals: use ../prompt-and-soft-methods/SKILL.md.
• Saving, loading checkpoints, merging adapters into base weights, or deployment packaging: use ../save-load-merge/SKILL.md.
• Trainer, Accelerate, distributed, FSDP, DeepSpeed, or multi-GPU training integration: use ../training-and-integrations/SKILL.md.

Start Fast

1. Run python scripts/check_peft_env.py from this sub-skill directory to verify imports and local package facts.
2. Read references/api-reference.md for the core class/function surface and common enum values.
3. Follow references/workflows.md for adapter construction, custom model targeting, lifecycle management, and low-level injection.
4. Use references/troubleshooting.md when errors mention invalid task types, missing target_modules, unsupported modules, adapter status irregularities, or optional dependency imports.

Core Decision Pattern

• Prefer get_peft_model(base_model, peft_config, adapter_name="default") for normal Transformers and custom PyTorch models when PEFT should return a PeftModel wrapper with helper methods.
• Use explicit target_modules for custom models unless the method and architecture have a known PEFT default mapping.
• Use modules_to_save for non-adapter layers that must remain trainable and be stored with the adapter, such as custom heads or task heads.
• Use print_trainable_parameters(), get_model_status(), and get_layer_status() before training when trainable counts, active adapters, or merged/disabled states look surprising.
• Use low-level inject_adapter_in_model(peft_config, model, adapter_name=...) only when the caller needs adapter layers in a plain torch.nn.Module and does not need PeftModel helper methods.

Bundled Materials

• references/api-reference.md: API inventory, enum usage, config validation, lifecycle helpers, and status helpers.
• references/workflows.md: practical recipes for standard wrapping, custom modules, target discovery, adapter lifecycle, and low-level injection.
• references/troubleshooting.md: diagnosis paths for target module, enum, trainability, optional dependency, custom module, and low-level injection failures.
• scripts/check_peft_env.py: safe local import/config smoke check with --help, JSON output option, and optional CUDA facts.