---

name: specialized-tuners description: "Select, configure, and troubleshoot PEFT's specialized non-LoRA/non-prompt tuners such as IA3, BOFT, OFT, LyCORIS-style LoHa/LoKr, VeRA/PVeRA, VBLoRA, XLora, LN tuning, FourierFT, FRoD, HRA/HiRA/SHiRA, RandLoRA, RoAd, OSF, and newer experimental tuner configs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

PEFT Specialized Tuners

Use this sub-skill when the user needs a PEFT adapter that is not ordinary LoRA, not a LoRA quantization workflow, and not prompt/soft-prompt tuning. It covers method selection, config-class lookup, target-module planning, compatibility checks, and troubleshooting for specialized tuners.

Route here for

• IA3, BOFT, OFT, LoHa, LoKr, Poly, Vera/PVera, VBLoRA, XLora, LN tuning, FourierFT, FROD, HRA, Hira, Shira, RandLoRA, Road, WaveFT, OSF, Delora, Gralora, Adamss, Beft, C3A, Lily, MISS, Psoft, Peanut, TinyLoRA, and nearby config/model classes.
• Choosing a non-LoRA alternative when LoRA is too large, too low-rank, unsuitable for heterogeneous serving, or not expressive enough.
• Validating that a method has a public *Config class and that its target-module constraints match the base model.
• Debugging method-specific errors such as missing feedforward_modules, unsupported layer classes, projection reproducibility, or mixed-adapter incompatibility.

Route elsewhere

• LoRA, AdaLoRA, DoRA, RS-LoRA, LoRA initialization variants, QLoRA, LoftQ, bitsandbytes quantization, and target-parameter LoRA: use ../lora-and-quantization/SKILL.md.
• Prompt tuning, prefix tuning, P-tuning, multitask prompt tuning, CPT, trainable tokens, and adaption prompt methods: use ../prompt-and-soft-methods/SKILL.md.
• Generic PeftModel, save/load, adapter naming, hotswapping, merging, and model lifecycle: use ../adapter-core/SKILL.md.

Fast workflow

1. Identify the task surface: causal LM, seq2seq LM, sequence/token classification, vision, diffusion, continual learning, multi-adapter serving, or checkpoint-size reduction.
2. Pick a method family from references/method-catalog.md instead of mirroring a source folder name mechanically.
3. Confirm the public config class with references/api-reference.md or scripts/list_peft_methods.py --filter <method>.
4. Set target_modules explicitly for custom models; rely on PEFT defaults only when the method documents a mapping for the model family.
5. Use get_peft_model(base_model, config) and inspect trainable parameters before training.
6. If the adapter will be mixed with other adapters, check mixed compatibility before promising adapter_names batch routing or weighted fusion.

Minimal patterns

from peft import IA3Config, TaskType, get_peft_model

config = IA3Config(
    task_type=TaskType.CAUSAL_LM,
    target_modules=["q_proj", "v_proj", "fc2"],
    feedforward_modules=["fc2"],
)
model = get_peft_model(base_model, config)
model.print_trainable_parameters()

from peft import VeraConfig, get_peft_model

config = VeraConfig(
    target_modules=["q_proj", "v_proj"],
    r=256,
    save_projection=True,
)
model = get_peft_model(base_model, config)

Bundled references

• references/method-catalog.md: selection guidance by method family and use case.
• references/api-reference.md: config/model class names, common fields, and config naming gotchas.
• references/troubleshooting.md: errors and diagnosis for target layers, optional dependencies, mixed adapters, and method constraints.
• scripts/list_peft_methods.py: local PEFT introspection helper for PeftType and public *Config classes.