PEFT (HuggingFace)
Parameter-Efficient Fine-Tuning library — adapts large language models with minimal trainable parameters. PEFT features: LoRA (Low-Rank Adaptation) for attention weight modification, QLoRA (quantized LoRA with bitsandbytes), IA3, Prefix Tuning, Prompt Tuning, AdaLoRA, LoftQ, LoHa, get_peft_model() wrapper, PeftModel.from_pretrained() for loading adapters, merge_and_unload() for full model merging, and model.print_trainable_parameters() for inspection. Enables fine-tuning 7B-70B LLMs on consumer GPUs by training only 0.1-1% of parameters for agent specialization.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Agent fine-tuned adapters are lightweight (10-100MB) — protect as proprietary IP. HF_TOKEN as environment secret. Agent training data may contain PII — ensure data governance before fine-tuning. bitsandbytes dependency has known security issues in older versions — keep updated.
⚡ Reliability
Best When
Fine-tuning large language models (7B-70B) for agent specialization on limited GPU resources — LoRA/QLoRA reduces trainable parameters to <1% enabling agent fine-tuning on consumer or cloud GPUs at fraction of full fine-tuning cost.
Avoid When
You have unlimited GPU compute and want full fine-tuning for maximum adaptation, or you're deploying and want pure inference speed without adapter overhead.
Use Cases
- • Agent LoRA fine-tuning — lora_config = LoraConfig(r=16, lora_alpha=32, target_modules=['q_proj', 'v_proj'], task_type='CAUSAL_LM'); model = get_peft_model(base_model, lora_config); model.print_trainable_parameters() shows ~0.5% trained; agent specialization on 7B model with 16GB GPU
- • Agent QLoRA on consumer GPU — model = AutoModelForCausalLM.from_pretrained('llama-3.1-8B', load_in_4bit=True); peft_model = get_peft_model(model, lora_config) — 4-bit quantization + LoRA fine-tunes 8B agent model on 10GB GPU; accessible agent specialization on RTX 3080/4080
- • Agent adapter loading — model = PeftModel.from_pretrained(base_model, 'myorg/agent-lora-adapter'); model.merge_and_unload() merges LoRA weights into base for inference without PEFT overhead; agent specialized models served efficiently
- • Multiple agent adapters — model = PeftModel.from_pretrained(base, adapter1); model.load_adapter(adapter2, 'second'); model.set_adapter('second') — switch between agent personality/skill adapters at inference time without reloading base model
- • Agent continual learning — train domain-specific LoRA adapter for each agent tool set; load task-specific adapter at runtime; base model shared across all agent variants; efficient multi-task agent deployment
Not For
- • Full fine-tuning — PEFT is for parameter-efficient adaptation; for full fine-tuning of smaller models use standard PyTorch training
- • Inference-only deployment — PEFT adds adapter overhead during inference; merge_and_unload() before production agent deployment for full speed
- • Non-transformer architectures — PEFT LoRA targets attention/linear layers; for CNN or other architectures effectiveness varies; primarily optimized for transformer LLMs
Interface
Authentication
HF_TOKEN for loading gated base models (LLaMA, Mistral) and pushing adapters to Hub. Public adapters: no auth.
Pricing
PEFT is Apache 2.0 licensed, maintained by HuggingFace. Free for all use. GPU compute and Hub storage costs separate.
Agent Metadata
Known Gotchas
- ⚠ target_modules must match actual model layer names — LoraConfig(target_modules=['q_proj', 'v_proj']) must match exact layer names in model architecture; LLaMA uses q_proj, GPT-2 uses c_attn, BERT uses query/key/value; wrong target_modules silently applies LoRA to no layers (0 trainable params); always print model architecture and verify names
- ⚠ QLoRA requires bitsandbytes — load_in_4bit=True needs bitsandbytes>=0.41.0; bitsandbytes has platform limitations (Linux/CUDA required; no macOS GPU support); agent QLoRA on macOS M-series falls back to CPU (very slow); use cloud GPU (Colab, RunPod) for agent QLoRA fine-tuning
- ⚠ Adapter not merged adds inference overhead — PeftModel with unmerged LoRA adapter is ~5% slower than base model; agent production serving should call model.merge_and_unload() before deployment; merged model behaves identically but faster; keep unmerged adapter for continued training or multi-adapter switching
- ⚠ rank (r) and alpha must be tuned per task — LoRA rank r=4 (minimal) vs r=64 (expressive); lora_alpha typically 2x rank; too low rank under-fits agent task; too high rank approaches full fine-tuning cost; start with r=16, lora_alpha=32 for agent fine-tuning; tune based on eval loss
- ⚠ PEFT version must match training version for loading — PeftModel.from_pretrained() may fail if PEFT version differs between training and loading environments; adapter_config.json records PEFT version; agent model deployment must pin same PEFT version used for training; version mismatch causes AttributeError on config fields
- ⚠ Gradient checkpointing incompatibility — gradient checkpointing (model.gradient_checkpointing_enable()) with PEFT requires model.enable_input_require_grads(); forgetting enable_input_require_grads() with gradient checkpointing causes all gradients to be None; agent fine-tuning on limited VRAM needing gradient checkpointing must call both methods
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Scores are editorial opinions as of 2026-03-07.