{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"peft","slug":"peft","name":"PEFT","type":"repo","url":"https://github.com/huggingface/peft","page_url":"https://unfragile.ai/peft","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"peft__cap_0","uri":"capability://code.generation.editing.low.rank.adapter.lora.parameter.injection.and.training","name":"low-rank adapter (lora) parameter injection and training","description":"Injects trainable low-rank decomposition matrices (A and B) into transformer attention and feed-forward layers, reducing trainable parameters from billions to millions while maintaining model capacity through rank-based factorization. Uses a registry-based dispatch mechanism (src/peft/mapping.py) to instantiate LoRA tuners that wrap base model layers, enabling selective parameter freezing and gradient computation only on adapter weights during backpropagation.","intents":["Fine-tune a 7B parameter LLM on a single consumer GPU by training only 0.1-2% of parameters","Create task-specific adapters that can be swapped without reloading the base model","Reduce checkpoint size from multi-GB to ~19MB for model distribution and versioning"],"best_for":["ML engineers fine-tuning large language models on limited hardware","Teams building multi-task systems requiring task-specific model variants","Researchers experimenting with adapter composition and merging strategies"],"limitations":["LoRA rank selection requires manual tuning; no automated rank discovery mechanism","Merged adapters cannot be unmerged without storing original base weights separately","Inference latency increases ~5-10% due to additional matrix multiplications in forward pass","Not suitable for tasks requiring structural model changes (e.g., adding new output heads)"],"requires":["PyTorch 1.13+","Transformers library 4.20+","Base model in float32, float16, or bfloat16 precision","GPU with minimum 8GB VRAM for billion-parameter models"],"input_types":["pretrained transformer model (from transformers library)","LoRA configuration (rank, alpha, target modules, dropout)","training data (tokenized sequences)"],"output_types":["PeftModel wrapper with injected LoRA layers","adapter checkpoint (JSON config + safetensors weights)","merged model weights (optional)"],"categories":["code-generation-editing","model-training","parameter-efficient-adaptation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_1","uri":"capability://code.generation.editing.quantization.aware.adapter.training.qlora.integration","name":"quantization-aware adapter training (qlora integration)","description":"Enables fine-tuning of 4-bit and 8-bit quantized models by training adapters on top of frozen quantized weights, using bitsandbytes integration to handle quantized forward passes while computing gradients only through adapter parameters. The architecture freezes the quantized base model and routes gradients exclusively through LoRA layers, eliminating the need to dequantize weights during training.","intents":["Fine-tune a 70B parameter model on a single 24GB GPU using 4-bit quantization","Reduce GPU memory footprint by 4-8x compared to standard LoRA on full-precision models","Train adapters for quantized models without access to original full-precision weights"],"best_for":["Individual researchers and small teams with limited GPU resources","Production systems requiring extreme memory efficiency for multi-model serving","Organizations deploying on edge devices or constrained cloud instances"],"limitations":["Quantization introduces ~0.5-2% accuracy degradation depending on quantization bits and model size","Adapter training speed is 10-20% slower than standard LoRA due to quantization overhead","Requires bitsandbytes library which is CUDA-specific; no CPU or AMD GPU support","Cannot merge quantized adapters back into base weights without dequantization"],"requires":["bitsandbytes 0.37.0+","CUDA 11.6+ (for 4-bit quantization support)","GPU with compute capability 7.0+ (V100, A100, RTX 30/40 series)","PyTorch 1.13+ with CUDA support"],"input_types":["pretrained model quantized via bitsandbytes (load_in_4bit or load_in_8bit)","LoRA configuration with quantization parameters","training data"],"output_types":["adapter checkpoint compatible with quantized base model","training metrics and loss curves"],"categories":["code-generation-editing","model-training","quantization-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_10","uri":"capability://automation.workflow.model.library.integration.and.auto.detection","name":"model library integration and auto-detection","description":"Automatically detects model architecture and applies adapter-specific optimizations for popular model families (LLaMA, Mistral, GPT-2, BERT, ViT, etc.) through architecture-aware tuner selection. The integration layer (src/peft/mapping.py) maps model classes to appropriate tuner implementations, enabling seamless adapter injection without manual layer specification. Supports automatic target module detection for different model architectures, reducing configuration complexity.","intents":["Automatically configure adapters for popular models without manual target module specification","Apply architecture-specific optimizations for different model families","Enable one-line adapter instantiation for supported models"],"best_for":["Teams using standard model architectures (LLaMA, Mistral, GPT-2, BERT, ViT)","Rapid prototyping requiring minimal configuration","Production systems with standard model families"],"limitations":["Custom model architectures require manual target module specification","Auto-detection may select suboptimal target modules for specialized architectures","Architecture detection is based on model class name; custom model subclasses may not be recognized","Limited support for vision models and multimodal architectures"],"requires":["PEFT 0.2.0+","model from transformers library or compatible architecture","model class must be recognized by PEFT's architecture detection"],"input_types":["pretrained model from transformers library","optional: explicit target modules if auto-detection is insufficient"],"output_types":["PeftModel with automatically configured adapters","detected target modules and architecture metadata"],"categories":["automation-workflow","model-training","integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_11","uri":"capability://automation.workflow.gradient.checkpointing.and.memory.optimization","name":"gradient checkpointing and memory optimization","description":"Integrates with PyTorch's gradient checkpointing to reduce memory footprint during training by recomputing activations during backpropagation instead of storing them. Works seamlessly with adapter training by checkpointing the base model while maintaining gradient flow through adapter parameters. Reduces peak memory usage by 30-50% during training with minimal computational overhead (10-15% slower training).","intents":["Train larger models or larger batch sizes on the same GPU by reducing memory footprint","Enable training of models that would otherwise exceed GPU memory","Balance memory usage and training speed through gradient checkpointing configuration"],"best_for":["Teams training large models with limited GPU memory","Production training pipelines optimizing for memory efficiency","Researchers exploring memory-computation tradeoffs"],"limitations":["Gradient checkpointing increases training time by 10-15% due to recomputation","Incompatible with some optimizers and training techniques (e.g., certain mixed precision strategies)","Debugging is more difficult because intermediate activations are not stored","Memory savings are model-dependent; some architectures benefit more than others"],"requires":["PyTorch 1.13+","base model supporting gradient checkpointing","training loop with gradient computation"],"input_types":["PeftModel with gradient checkpointing enabled","training data and optimizer"],"output_types":["trained adapter checkpoint","training metrics with memory usage statistics"],"categories":["automation-workflow","model-training","memory-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_12","uri":"capability://automation.workflow.adapter.state.management.and.lifecycle.control","name":"adapter state management and lifecycle control","description":"Manages adapter lifecycle through add_adapter(), set_adapter(), delete_adapter(), and disable_adapter() methods, enabling programmatic control over which adapters are active during inference or training. The state management system maintains a registry of adapters and their activation status, enabling dynamic adapter switching without model reloading. Supports adapter enable/disable without deletion, allowing temporary deactivation and reactivation.","intents":["Dynamically activate and deactivate adapters during inference without model reloading","Manage multiple adapters and switch between them programmatically","Enable/disable adapters for A/B testing or gradual rollout"],"best_for":["Production inference systems handling multiple tasks with adapter switching","A/B testing and gradual rollout of new adapters","Research on adapter composition and ensemble methods"],"limitations":["Adapter switching adds 50-100ms latency due to state changes","No built-in conflict detection for overlapping adapter modifications","Disabled adapters still consume memory; deletion is required for memory reclamation","Adapter state is not persisted; must be managed programmatically"],"requires":["PEFT 0.2.0+","PeftModel with adapters already added"],"input_types":["adapter name (string)","optional: adapter configuration for add_adapter()"],"output_types":["updated PeftModel state","adapter metadata and status"],"categories":["automation-workflow","model-deployment","state-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_13","uri":"capability://automation.workflow.mixed.precision.training.with.automatic.loss.scaling","name":"mixed-precision training with automatic loss scaling","description":"Enables training adapters in mixed precision (float16 or bfloat16) with automatic loss scaling to prevent gradient underflow, reducing memory usage by 50% and improving training speed by 1.5-2x. Integrates with PyTorch's automatic mixed precision (AMP) and transformers' native mixed-precision support to maintain numerical stability while reducing precision.","intents":["Reduce memory usage by 50% by training in float16 instead of float32","Improve training speed by 1.5-2x using lower precision computation","Maintain numerical stability during mixed-precision training via automatic loss scaling"],"best_for":["Practitioners training on GPUs with limited VRAM (8GB-24GB)","Teams prioritizing training speed over maximum precision","Scenarios where 1-2% performance degradation is acceptable for 50% memory savings"],"limitations":["Mixed precision can introduce 1-2% performance degradation on some tasks","Requires GPU with native float16 support (NVIDIA V100+, A100, RTX series)","Loss scaling requires careful tuning to prevent gradient overflow/underflow","Some operations (e.g., reductions) may have numerical stability issues in float16"],"requires":["PyTorch 1.13+ with AMP support","transformers 4.20+","NVIDIA GPU with native float16 support (compute capability 7.0+)","fp16=True or bf16=True in training arguments"],"input_types":["PeftModel instance","mixed precision flag (fp16=True or bf16=True)","training data"],"output_types":["trained adapter (same format as full-precision training)","training logs showing reduced memory and improved speed"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_14","uri":"capability://automation.workflow.adapter.inference.with.dynamic.routing","name":"adapter inference with dynamic routing","description":"Enables selecting and routing to different adapters at inference time based on input characteristics or external signals, without reloading base model weights. Implements set_adapter() method that switches active adapter in-place, enabling dynamic adapter selection in production systems where different inputs may require different task-specific adapters.","intents":["Route different inputs to different task-specific adapters at inference time","Implement dynamic adapter selection based on input classification or user selection","Support multi-tenant inference where different users have different task-specific adapters"],"best_for":["Multi-task inference systems where input type determines adapter selection","Multi-tenant deployments where different customers have different adapters","Production systems requiring dynamic adapter switching without model reloading"],"limitations":["Adapter switching adds ~10-50ms latency per switch due to parameter reloading","No built-in mechanism for automatic adapter selection; requires external routing logic","Switching adapters during inference requires synchronization in multi-threaded/async scenarios","Composed adapters (multiple adapters stacked) have multiplicative latency overhead"],"requires":["PyTorch 1.13+","transformers 4.20+","multiple trained adapters loaded on same base model","inference framework that can manage adapter switching (custom serving code)"],"input_types":["PeftModel with multiple adapters loaded","adapter name (string) to activate","input data for inference"],"output_types":["predictions from selected adapter","adapter metadata (name, type, parameter count)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_2","uri":"capability://code.generation.editing.multi.adapter.composition.and.switching","name":"multi-adapter composition and switching","description":"Manages multiple independent adapters attached to a single base model, enabling runtime switching between task-specific adapters via set_adapter() and composition of multiple adapters through add_adapter(). The architecture maintains a registry of named adapters and routes forward passes through the active adapter(s), supporting both sequential and parallel adapter composition patterns defined in the configuration system.","intents":["Train separate adapters for different tasks (e.g., summarization, translation, QA) and switch between them without reloading the model","Combine multiple adapters for multi-task inference or domain-specific specialization","Manage adapter lifecycle (add, delete, activate) programmatically during inference"],"best_for":["Multi-task learning systems requiring task-specific model variants","Production inference servers handling multiple use cases with a single base model","Research teams exploring adapter composition and ensemble methods"],"limitations":["Adapter switching adds ~50-100ms latency due to module state changes and gradient graph reconstruction","Parallel adapter composition increases memory footprint linearly with number of active adapters","No built-in conflict resolution for overlapping adapter modifications to the same layers","Adapter merging is not supported for composed adapters; must unmerge all before merging individual adapters"],"requires":["PEFT 0.4.0+","Base model with multiple adapters already added via add_adapter()","adapter names and configurations stored in model config"],"input_types":["PeftModel with multiple adapters registered","adapter name (string) to activate","optional composition configuration (for multi-adapter inference)"],"output_types":["model output from selected adapter","adapter metadata (name, type, parameters)"],"categories":["code-generation-editing","model-training","multi-task-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_3","uri":"capability://automation.workflow.adapter.checkpoint.serialization.and.loading","name":"adapter checkpoint serialization and loading","description":"Saves and loads adapter weights and configurations independently from base model weights using save_pretrained() and from_pretrained() methods, storing only the trainable adapter parameters (~19MB) rather than full model checkpoints (multi-GB). The serialization format uses JSON for configuration and safetensors for weights, enabling portable adapter distribution and version control without base model dependencies.","intents":["Save trained adapters to disk and load them onto different base model instances","Share adapter checkpoints across teams without distributing large base model weights","Version control adapter configurations and weights separately from base models","Load adapters from Hugging Face Hub without downloading full model checkpoints"],"best_for":["Teams distributing fine-tuned models with minimal storage overhead","Researchers publishing adapter weights on Hugging Face Hub","Production systems requiring rapid model updates by swapping adapter checkpoints"],"limitations":["Adapters are not portable across different base model architectures or versions","Loading adapters requires the exact base model architecture and initialization; no automatic compatibility checking","Merged adapters cannot be saved in adapter format; must save as full model checkpoint","No built-in versioning or dependency tracking between adapter and base model versions"],"requires":["PEFT 0.2.0+","safetensors library for weight serialization","base model already loaded in memory for adapter loading"],"input_types":["trained PeftModel instance (for save_pretrained)","adapter directory path or Hugging Face Hub model ID (for from_pretrained)","base model instance (for loading adapters)"],"output_types":["adapter checkpoint directory with adapter_config.json and adapter_model.safetensors","loaded PeftModel with adapters attached to base model"],"categories":["automation-workflow","data-processing-analysis","model-persistence"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_4","uri":"capability://code.generation.editing.dynamic.rank.allocation.adalora","name":"dynamic rank allocation (adalora)","description":"Automatically adjusts LoRA rank during training based on parameter importance scores, pruning low-importance parameters and allocating rank budget to high-importance dimensions. Uses a parametrized rank matrix with importance-weighted pruning to dynamically reduce the effective rank, optimizing the rank-performance tradeoff without manual hyperparameter tuning. The mechanism computes importance scores via gradient-based analysis and applies structured pruning to adapter matrices.","intents":["Automatically find optimal LoRA rank for a given task without manual hyperparameter search","Reduce adapter size further by pruning unimportant parameters during training","Achieve better performance-efficiency tradeoff than fixed-rank LoRA"],"best_for":["Researchers optimizing adapter efficiency without extensive hyperparameter tuning","Production systems requiring minimal adapter size with maximum performance","Teams with limited compute for hyperparameter search"],"limitations":["AdaLoRA training is 15-25% slower than standard LoRA due to importance score computation","Rank allocation is task-specific; optimal rank discovered during training may not transfer to other tasks","Requires careful tuning of pruning schedule and importance threshold parameters","Incompatible with adapter merging; must save as separate adapter checkpoint"],"requires":["PEFT 0.3.0+","AdaLoRA configuration with target_r (target rank) and init_r (initial rank)","training loop with gradient computation for importance scoring"],"input_types":["base model and training data","AdaLoRA configuration (target_r, init_r, lora_alpha, pruning_schedule)","importance scoring method (gradient-based or magnitude-based)"],"output_types":["adapter with dynamically allocated ranks","importance scores and pruning history","final adapter checkpoint with optimized rank allocation"],"categories":["code-generation-editing","model-training","hyperparameter-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_5","uri":"capability://code.generation.editing.prompt.tuning.and.prefix.tuning","name":"prompt tuning and prefix tuning","description":"Adds learnable prompt tokens (prompt tuning) or prefix embeddings (prefix tuning) to the input sequence or hidden states, enabling model adaptation without modifying model weights. Prompt tuning prepends learnable soft prompts to the input embeddings, while prefix tuning injects learnable prefix vectors into each transformer layer's key-value cache. Both methods freeze all model parameters and train only the prompt/prefix embeddings, reducing trainable parameters to 0.01-0.1% of model size.","intents":["Fine-tune models by learning task-specific prompts without modifying model weights","Adapt models to new tasks with minimal parameter overhead (thousands vs millions)","Enable prompt-based model specialization for multi-task systems"],"best_for":["Few-shot learning scenarios with limited task-specific data","Systems requiring extreme parameter efficiency (< 0.1% of model)","Research on prompt-based adaptation and in-context learning"],"limitations":["Prompt tuning performance lags behind LoRA by 5-15% on most benchmarks","Learned prompts are not interpretable and cannot be analyzed as natural language","Prefix tuning adds latency to every forward pass due to prefix computation in each layer","Prompt/prefix length must be fixed at training time; cannot be adjusted at inference"],"requires":["PEFT 0.1.0+","base model with transformer architecture","prompt_len or num_prefix_tokens configuration parameter"],"input_types":["base model","prompt tuning or prefix tuning configuration","training data"],"output_types":["learned prompt embeddings or prefix vectors","adapter checkpoint with prompt/prefix weights"],"categories":["code-generation-editing","model-training","prompt-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_6","uri":"capability://automation.workflow.adapter.merging.and.unmerging","name":"adapter merging and unmerging","description":"Merges trained adapter weights into base model weights via merge_adapter(), combining adapter parameters with base weights to create a single unified model without separate adapter modules. Unmerging via unmerge_adapter() restores the original base model weights and adapter separation, enabling reversible adapter composition. The merge operation computes merged_weight = base_weight + adapter_weight, eliminating the adapter module from the forward pass.","intents":["Convert trained adapters into standalone models for deployment without PEFT dependencies","Eliminate adapter inference latency by merging adapters into base weights","Create task-specific model variants for distribution without base model dependencies"],"best_for":["Production deployment requiring minimal inference latency and no PEFT runtime dependency","Creating standalone model checkpoints for distribution without adapter infrastructure","Inference systems with strict latency requirements"],"limitations":["Merged adapters cannot be unmerged without storing original base weights separately","Merging is irreversible without keeping a backup of the original base model","Merged models are full-size checkpoints (multi-GB), eliminating storage benefits of adapters","Multiple adapters cannot be merged simultaneously; must merge one at a time"],"requires":["PEFT 0.2.0+","trained PeftModel with at least one adapter","optional: original base model weights for unmerging"],"input_types":["PeftModel with trained adapters","adapter name to merge (optional; merges active adapter if not specified)"],"output_types":["model with merged weights (adapter parameters added to base weights)","standard transformers model checkpoint (no PEFT wrapper)"],"categories":["automation-workflow","model-deployment","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_7","uri":"capability://automation.workflow.distributed.training.with.adapter.synchronization","name":"distributed training with adapter synchronization","description":"Integrates with PyTorch Distributed Data Parallel (DDP) and Hugging Face Accelerate to synchronize adapter gradients across multiple GPUs/nodes during training. The architecture freezes base model weights and distributes only adapter parameters across devices, reducing communication overhead and enabling efficient multi-GPU training. Gradient synchronization occurs only for adapter parameters, not the full model, reducing communication bandwidth by 99%+ compared to full model distributed training.","intents":["Train adapters across multiple GPUs to reduce training time by 4-8x","Scale adapter training to multiple nodes without proportional communication overhead","Use distributed training infrastructure (DDP, FSDP) with minimal code changes"],"best_for":["Teams with multi-GPU infrastructure training large models","Production training pipelines requiring distributed training","Research teams scaling adapter training across clusters"],"limitations":["Distributed training adds 5-10% overhead for gradient synchronization and communication","Requires careful batch size tuning to maintain training stability across devices","FSDP (Fully Sharded Data Parallel) support is limited; DDP is recommended","Gradient accumulation and mixed precision training require additional configuration"],"requires":["PyTorch 1.13+ with distributed training support","Hugging Face Accelerate 0.12.0+","multiple GPUs (2+) or nodes with NCCL communication","CUDA 11.0+ for multi-GPU synchronization"],"input_types":["PeftModel configured for distributed training","training data distributed across devices","Accelerate configuration for DDP or FSDP"],"output_types":["trained adapter checkpoint","training logs with distributed metrics"],"categories":["automation-workflow","model-training","distributed-systems"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_8","uri":"capability://automation.workflow.configuration.driven.adapter.instantiation","name":"configuration-driven adapter instantiation","description":"Uses a declarative configuration system (PeftConfig subclasses) to specify adapter type, hyperparameters, and target modules, enabling adapter instantiation via get_peft_model(model, config) without manual layer wrapping. The configuration system maps adapter types to tuner classes via a registry (src/peft/mapping.py), enabling extensible adapter support. Configurations are serializable to JSON, enabling reproducible adapter creation and version control.","intents":["Define adapter configurations in JSON and instantiate adapters programmatically without code changes","Version control adapter hyperparameters separately from training code","Enable reproducible adapter creation across different environments"],"best_for":["Teams managing multiple adapter configurations for different tasks","Production systems requiring configuration-driven model adaptation","Researchers tracking adapter hyperparameters and experimental configurations"],"limitations":["Configuration validation is minimal; invalid configurations may fail at instantiation time","No built-in configuration migration for backward compatibility across PEFT versions","Complex adapter compositions require nested configuration structures that are difficult to manage","Configuration schema is not standardized; each adapter type has its own config class"],"requires":["PEFT 0.1.0+","adapter configuration class matching the adapter type (LoraConfig, PrefixTuningConfig, etc.)","base model instance"],"input_types":["PeftConfig subclass instance or JSON configuration","base model"],"output_types":["PeftModel with adapters instantiated according to configuration","configuration metadata"],"categories":["automation-workflow","data-processing-analysis","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__cap_9","uri":"capability://code.generation.editing.ia3.infused.adapter.by.inhibiting.and.amplifying.inner.activations","name":"ia3 (infused adapter by inhibiting and amplifying inner activations)","description":"Injects learnable scaling vectors into transformer feed-forward and attention layers to modulate intermediate activations, enabling parameter-efficient adaptation through element-wise scaling rather than low-rank decomposition. IA3 learns multiplicative masks applied to inner activations, reducing trainable parameters to 0.01% of model size while maintaining model capacity through activation modulation. The mechanism is simpler than LoRA, requiring only vector-scale parameters instead of matrix decomposition.","intents":["Fine-tune models with minimal parameters (0.01% vs 0.1-2% for LoRA) using activation scaling","Adapt models to new tasks with extreme parameter efficiency for memory-constrained environments","Explore alternative adapter mechanisms beyond low-rank decomposition"],"best_for":["Extreme parameter efficiency scenarios where even LoRA is too large","Edge deployment with severe memory constraints","Research on activation-based model adaptation"],"limitations":["IA3 performance lags LoRA by 10-20% on most benchmarks","Activation scaling is less expressive than low-rank decomposition for complex task adaptation","Scaling vectors are not interpretable; cannot analyze what aspects of activations are being modulated","Incompatible with adapter merging; must maintain separate adapter weights"],"requires":["PEFT 0.3.0+","base model with transformer architecture","IA3Config with target_modules specification"],"input_types":["base model","IA3 configuration (target_modules, feedforward_modules)","training data"],"output_types":["learned scaling vectors","adapter checkpoint with IA3 weights"],"categories":["code-generation-editing","model-training","parameter-efficient-adaptation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"peft__headline","uri":"capability://model.training.parameter.efficient.fine.tuning.library","name":"parameter-efficient fine-tuning library","description":"PEFT is a library designed for parameter-efficient fine-tuning of large pretrained models, allowing users to adapt models by training only a small subset of parameters, significantly reducing computational costs while maintaining high performance.","intents":["best parameter-efficient fine-tuning library","parameter-efficient fine-tuning for large models","how to fine-tune models efficiently","top libraries for fine-tuning pretrained models","efficient model training on consumer GPUs"],"best_for":["users with limited computational resources","developers looking for efficient model adaptation"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.13+","Transformers library 4.20+","Base model in float32, float16, or bfloat16 precision","GPU with minimum 8GB VRAM for billion-parameter models","bitsandbytes 0.37.0+","CUDA 11.6+ (for 4-bit quantization support)","GPU with compute capability 7.0+ (V100, A100, RTX 30/40 series)","PyTorch 1.13+ with CUDA support","PEFT 0.2.0+","model from transformers library or compatible architecture"],"failure_modes":["LoRA rank selection requires manual tuning; no automated rank discovery mechanism","Merged adapters cannot be unmerged without storing original base weights separately","Inference latency increases ~5-10% due to additional matrix multiplications in forward pass","Not suitable for tasks requiring structural model changes (e.g., adding new output heads)","Quantization introduces ~0.5-2% accuracy degradation depending on quantization bits and model size","Adapter training speed is 10-20% slower than standard LoRA due to quantization overhead","Requires bitsandbytes library which is CUDA-specific; no CPU or AMD GPU support","Cannot merge quantized adapters back into base weights without dequantization","Custom model architectures require manual target module specification","Auto-detection may select suboptimal target modules for specialized architectures","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=peft","compare_url":"https://unfragile.ai/compare?artifact=peft"}},"signature":"Ss1KtKjsQxFw3FCypfJeJg5WouGbi5kWvi1P++DI9dKwi5MmM8HtRZnmTaob4YJ9zvi96LsY/iQmZxrkOQnKAg==","signedAt":"2026-06-22T12:18:29.524Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/peft","artifact":"https://unfragile.ai/peft","verify":"https://unfragile.ai/api/v1/verify?slug=peft","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}