{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llmcompressor","slug":"llmcompressor","name":"llmcompressor","type":"repo","url":"https://github.com/vllm-project/llm-compressor","page_url":"https://unfragile.ai/llmcompressor","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llmcompressor__cap_0","uri":"capability://data.processing.analysis.one.shot.post.training.quantization.with.calibration.free.execution","name":"one-shot post-training quantization with calibration-free execution","description":"Applies quantization algorithms (GPTQ, AWQ, AutoRound) to pre-trained models in a single forward pass without requiring fine-tuning, using a modifier-based system that injects quantization observers into the model graph during a calibration phase. The framework traces model execution sequentially, collecting activation statistics, then applies learned quantization parameters to weights and activations with minimal accuracy loss.","intents":["I want to quantize a 7B parameter model to INT8 in under 30 minutes without retraining","I need to reduce model size by 4x while maintaining <1% accuracy drop on my benchmark","I want to apply multiple quantization schemes (weight-only vs activation-aware) and compare results"],"best_for":["ML engineers deploying large models to resource-constrained environments","teams needing rapid model optimization without access to training compute","researchers comparing quantization algorithm effectiveness"],"limitations":["Requires representative calibration dataset (typically 128-512 samples); poor dataset selection degrades accuracy","Sequential tracing adds memory overhead proportional to model size; distributed compression needed for >70B models","Quantization parameters are static post-compression; no dynamic per-batch adaptation","Some model architectures (custom attention, sparse operations) may require custom modifier implementations"],"requires":["PyTorch 2.0+","HuggingFace Transformers 4.30+","CUDA 11.8+ for GPU acceleration (CPU fallback available but slow)","Calibration dataset in HuggingFace datasets format or custom DataLoader"],"input_types":["PyTorch model (HuggingFace format)","Calibration dataset (text samples or preprocessed tensors)","Compression recipe YAML (algorithm selection, bit-widths, targets)"],"output_types":["Quantized model (safetensors format with compressed-tensors metadata)","Quantization statistics (scales, zero-points, per-channel/per-token)","Calibration logs (activation ranges, outlier detection)"],"categories":["data-processing-analysis","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_1","uri":"capability://data.processing.analysis.multi.algorithm.quantization.scheme.composition","name":"multi-algorithm quantization scheme composition","description":"Enables mixing of different quantization algorithms (GPTQ for weights, AWQ for activations, SmoothQuant for layer normalization) within a single compression recipe, applying algorithm-specific modifiers to different layer types based on a declarative YAML specification. The modifier system resolves dependencies between algorithms and applies them in topologically-sorted order during the compression session.","intents":["I want to apply weight-only GPTQ to transformer blocks but activation-aware quantization to attention layers","I need to smooth activations before quantization to reduce outliers in specific layers","I want to compare hybrid quantization schemes (e.g., INT8 weights + INT4 activations) on the same model"],"best_for":["ML engineers fine-tuning compression strategies for domain-specific models","researchers exploring algorithm combinations for optimal accuracy-efficiency tradeoffs","teams with heterogeneous hardware (some GPUs support FP8, others don't)"],"limitations":["Algorithm interactions are not automatically validated; incompatible combinations (e.g., two conflicting weight quantizers) require manual recipe debugging","Modifier ordering matters but is implicit in YAML; circular dependencies or missing dependencies can cause silent failures","No built-in A/B testing framework; comparing schemes requires running separate compression sessions"],"requires":["Understanding of quantization algorithm differences (GPTQ vs AWQ vs AutoRound)","YAML recipe file defining modifier targets and parameters","Calibration dataset representative of target use case"],"input_types":["YAML recipe file (modifier definitions, layer targets, algorithm parameters)","PyTorch model","Calibration dataset"],"output_types":["Compressed model with mixed quantization schemes","Modifier execution log (which algorithms applied to which layers)","Per-layer quantization statistics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_10","uri":"capability://automation.workflow.distributed.compression.for.models.exceeding.single.gpu.memory","name":"distributed compression for models exceeding single-gpu memory","description":"Enables compression of very large models (100B+) across multiple GPUs using distributed calibration and modifier application. The framework partitions the model across GPUs, coordinates calibration data flow, synchronizes quantization parameters across devices, and reconstructs the full model for export, supporting both data parallelism and model parallelism strategies.","intents":["I want to compress a 100B model using 4 A100 GPUs","I need to coordinate quantization calibration across multiple devices","I want to apply compression to models too large for single-GPU memory"],"best_for":["teams with multi-GPU infrastructure compressing very large models","organizations deploying 100B+ models where single-GPU compression is infeasible","research groups studying compression at scale"],"limitations":["Distributed compression adds significant complexity; debugging is harder than single-GPU","Communication overhead between GPUs can dominate for small models; only worthwhile for 50B+","Requires careful synchronization of quantization parameters; race conditions can cause subtle bugs","Not all modifiers support distributed execution; custom modifiers may need reimplementation"],"requires":["Multi-GPU setup (minimum 2 GPUs, typically 4+ for large models)","NCCL or similar collective communication library","Distributed PyTorch setup (torch.distributed)","Sufficient calibration data to distribute across GPUs"],"input_types":["Large PyTorch model","Calibration dataset","Distributed compression configuration (number of GPUs, parallelism strategy)"],"output_types":["Compressed model (reconstructed on single GPU for export)","Distributed execution log (per-GPU calibration statistics)","Communication overhead metrics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_11","uri":"capability://automation.workflow.fine.tuning.with.compression.for.accuracy.recovery","name":"fine-tuning with compression for accuracy recovery","description":"Enables training models with compression modifiers active, allowing weights to adapt to quantization constraints during fine-tuning. The framework applies quantization-aware training (QAT) by injecting fake quantization operations into the forward pass, computing gradients through quantized weights, and updating parameters to minimize loss while respecting quantization constraints.","intents":["I want to recover accuracy lost during one-shot quantization by fine-tuning","I need to train a model with INT4 quantization constraints from the start","I want to apply quantization-aware training to improve INT4 model quality"],"best_for":["teams pursuing maximum accuracy for aggressive quantization (INT4, INT3)","researchers studying quantization-aware training and its effectiveness","production systems where fine-tuning budget is available for accuracy recovery"],"limitations":["Fine-tuning adds significant training cost; only worthwhile if accuracy gap is >1-2%","Requires training data and compute; not suitable for one-shot compression scenarios","Quantization-aware training is slower than standard training due to fake quantization overhead","Hyperparameter tuning (learning rate, batch size, number of steps) needed for best results"],"requires":["Training dataset (can be same as calibration data or larger)","GPU with sufficient memory for training (typically 2-3x inference memory)","Training hyperparameters (learning rate, batch size, number of epochs)","Optimizer and loss function compatible with quantization constraints"],"input_types":["PyTorch model","Training dataset","Compression recipe with quantization modifiers","Training configuration (learning rate, batch size, epochs)"],"output_types":["Fine-tuned quantized model","Training loss curves and accuracy metrics","Quantization parameter evolution during training"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_12","uri":"capability://data.processing.analysis.model.free.post.training.quantization.without.model.loading","name":"model-free post-training quantization without model loading","description":"Enables quantization of models without loading the full model into memory, using a model-free approach that analyzes model structure from metadata and applies quantization based on layer statistics. The framework reads model weights on-demand, computes quantization parameters, and writes quantized weights back without keeping the full model in memory, suitable for extremely large models or resource-constrained environments.","intents":["I want to quantize a 200B model on a machine with limited GPU memory","I need to quantize models without loading them fully into memory","I want to apply quantization to models that don't fit in available VRAM"],"best_for":["teams with limited GPU memory quantizing very large models","organizations running quantization on edge devices or CPUs","research groups studying memory-efficient compression"],"limitations":["Model-free approach has limited visibility into layer interactions; may produce suboptimal quantization","Requires sequential weight processing; slower than batch processing for large models","Cannot leverage activation statistics; weight-only quantization only","Incompatible with algorithms requiring full model context (e.g., some pruning strategies)"],"requires":["Model metadata (layer structure, weight shapes, data types)","Sufficient disk space for reading/writing weights","Minimal GPU memory (typically <10GB even for 200B models)"],"input_types":["Model metadata (HuggingFace config, layer definitions)","Model weights (can be on disk, not loaded into memory)","Quantization configuration"],"output_types":["Quantized model weights","Quantization parameters (per-layer scales, zero-points)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_13","uri":"capability://data.processing.analysis.mixture.of.experts.moe.model.compression.with.expert.level.targeting","name":"mixture of experts (moe) model compression with expert-level targeting","description":"Provides specialized compression support for MoE models by enabling per-expert quantization, pruning, and distillation. The framework identifies expert layers, applies compression modifiers to individual experts or expert groups, and preserves routing logic, enabling efficient compression of sparse MoE architectures where only a subset of experts are active per token.","intents":["I want to quantize individual experts in a MoE model with different bit-widths","I need to prune inactive or low-importance experts to reduce model size","I want to compress MoE models while preserving routing efficiency"],"best_for":["teams deploying MoE models (Mixtral, Switch Transformers) with compression","researchers studying compression strategies for sparse architectures","production systems where MoE compression can reduce inference cost"],"limitations":["MoE compression is less mature than dense model compression; fewer algorithm options","Expert-level quantization adds complexity; routing logic must be preserved","Pruning experts can break load balancing; requires careful validation","Limited hardware support for sparse expert execution; vLLM support is partial"],"requires":["MoE model architecture (Mixtral, Switch Transformers, etc.)","Understanding of expert routing and load balancing","Calibration data representative of expert activation patterns"],"input_types":["MoE PyTorch model","Calibration dataset","Expert-level compression configuration"],"output_types":["Compressed MoE model with per-expert quantization/pruning","Expert activation statistics","Load balancing metrics"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_14","uri":"capability://data.processing.analysis.multimodal.model.compression.with.vision.language.alignment","name":"multimodal model compression with vision-language alignment","description":"Extends compression to multimodal models (vision-language models) by applying compression to vision encoders, text encoders, and fusion layers while preserving cross-modal alignment. The framework handles different modality-specific compression strategies (e.g., more aggressive quantization for vision encoders) and validates that compressed models maintain alignment between vision and language representations.","intents":["I want to compress a vision-language model (CLIP, LLaVA) while preserving image-text alignment","I need to apply different compression strategies to vision and text encoders","I want to quantize multimodal models without degrading cross-modal understanding"],"best_for":["teams deploying vision-language models with compression","researchers studying compression for multimodal architectures","production systems where multimodal compression reduces inference cost"],"limitations":["Multimodal compression is less mature; fewer algorithm options and examples","Cross-modal alignment validation is complex; requires multimodal evaluation metrics","Different modalities may have different compression sensitivity; requires careful tuning","Limited hardware support for efficient multimodal inference; vLLM support is partial"],"requires":["Multimodal model (vision-language model)","Calibration dataset with paired vision-language samples","Evaluation metrics for cross-modal alignment"],"input_types":["Multimodal PyTorch model","Calibration dataset (images + text)","Multimodal compression configuration"],"output_types":["Compressed multimodal model","Per-modality compression statistics","Cross-modal alignment metrics"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_15","uri":"capability://data.processing.analysis.compression.metrics.and.accuracy.evaluation.framework","name":"compression metrics and accuracy evaluation framework","description":"Provides built-in evaluation tools for measuring compression impact on model accuracy, including task-specific metrics (perplexity, BLEU, exact match), benchmark datasets (MMLU, HellaSwag, TruthfulQA), and comparison utilities for quantifying accuracy loss. The framework integrates with HuggingFace Evaluate and supports custom evaluation functions, enabling systematic assessment of compression quality.","intents":["I want to measure accuracy loss from quantization on standard benchmarks","I need to compare compression quality across different algorithms and bit-widths","I want to validate that my compressed model meets accuracy requirements before deployment"],"best_for":["teams validating compression quality before production deployment","researchers comparing compression algorithms on standard benchmarks","organizations with strict accuracy requirements for compressed models"],"limitations":["Evaluation is time-consuming; full benchmark runs can take hours to days","Benchmark selection affects results; different benchmarks may show different accuracy impacts","Custom evaluation functions require implementation; no automatic metric selection","Limited support for domain-specific evaluation; general-purpose benchmarks may not reflect actual use cases"],"requires":["Evaluation dataset (can be standard benchmarks or custom)","Evaluation metrics (task-specific or custom functions)","Sufficient compute for running evaluations (typically 1-4 GPUs)"],"input_types":["Compressed model","Evaluation dataset","Evaluation configuration (metrics, batch size, number of samples)"],"output_types":["Accuracy metrics (perplexity, BLEU, exact match, etc.)","Comparison with baseline (accuracy loss percentage)","Per-sample evaluation results"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_2","uri":"capability://automation.workflow.sequential.model.tracing.and.subgraph.execution.for.memory.constrained.compression","name":"sequential model tracing and subgraph execution for memory-constrained compression","description":"Decomposes large models into sequential subgraphs (e.g., individual transformer layers) and processes them one at a time, keeping only the current subgraph in GPU memory while offloading others to disk or CPU. The framework traces model execution using PyTorch's symbolic tracing, identifies layer boundaries, and reconstructs activations on-demand during calibration, enabling compression of models larger than GPU VRAM.","intents":["I want to compress a 70B model on a single A100 GPU without distributed training","I need to reduce peak memory usage during quantization calibration by 50%","I want to compress models on consumer GPUs (24GB VRAM) that normally require enterprise hardware"],"best_for":["teams with limited GPU resources (single GPU, <80GB VRAM)","researchers working on very large models (70B+) without multi-GPU setups","cost-conscious organizations avoiding cloud GPU rental"],"limitations":["Sequential processing adds 20-40% wall-clock time overhead vs batch processing due to repeated model loading/unloading","Disk I/O becomes bottleneck for models with large intermediate activations; NVMe SSD strongly recommended","Some layer dependencies (e.g., skip connections across subgraphs) require activation caching, reducing memory savings","Incompatible with dynamic control flow (if/while statements); requires static model graphs"],"requires":["PyTorch 2.0+ with symbolic tracing support","Sufficient disk space (typically 2-3x model size for activation caching)","NVMe SSD for acceptable I/O performance (HDD will be very slow)"],"input_types":["PyTorch model with static graph structure","Calibration dataset","Disk path for activation offloading"],"output_types":["Compressed model","Subgraph execution trace (layer boundaries, activation shapes)","Memory usage profile (peak GPU, disk I/O statistics)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_3","uri":"capability://data.processing.analysis.gptq.weight.quantization.with.hessian.based.optimization","name":"gptq weight quantization with hessian-based optimization","description":"Implements GPTQ (Generative Pre-trained Transformer Quantization) algorithm that quantizes model weights to low bit-widths (INT4, INT3, INT2) by solving per-layer least-squares problems using Hessian information from the calibration data. The algorithm iteratively quantizes weights while updating remaining weights to minimize reconstruction error, achieving near-lossless compression with minimal calibration data.","intents":["I want to compress model weights to INT4 with <0.5% accuracy loss using only 128 calibration samples","I need to understand which weights are most sensitive to quantization (Hessian analysis)","I want to apply different bit-widths to different layers based on sensitivity analysis"],"best_for":["teams deploying weight-only quantized models where activation precision is less critical","researchers studying weight sensitivity and layer-wise quantization strategies","production systems where INT4 weights provide 4x compression with acceptable accuracy"],"limitations":["Requires computing Hessian matrix (inverse of Fisher information); for large layers this is O(n²) memory and O(n³) compute","Calibration quality heavily impacts final accuracy; requires representative data distribution","Per-layer quantization means different layers may have different bit-widths, complicating hardware optimization","No built-in support for mixed-precision (some layers INT4, others INT8); requires manual recipe configuration"],"requires":["Calibration dataset with at least 128 samples","GPU with sufficient memory for Hessian computation (typically 2-3x model layer size)","PyTorch with autograd support"],"input_types":["PyTorch model","Calibration dataset (text tokens or preprocessed activations)","GPTQ configuration (bit-width, group size, Hessian block size)"],"output_types":["INT4/INT3/INT2 quantized weights","Quantization scales and zero-points","Hessian-based sensitivity metrics per layer"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_4","uri":"capability://data.processing.analysis.awq.activation.aware.weight.quantization","name":"awq activation-aware weight quantization","description":"Implements Activation-aware Weight Quantization (AWQ) which identifies and preserves activation outliers by smoothing weight distributions before quantization. The algorithm analyzes activation ranges across calibration data, identifies channels with extreme values, and applies per-channel scaling to reduce outlier impact, enabling lower bit-widths while maintaining accuracy.","intents":["I want to quantize weights to INT4 while preserving accuracy for models with activation outliers","I need to understand which channels have extreme activations and how to handle them","I want to apply activation-aware smoothing before weight quantization to improve INT4 quality"],"best_for":["teams deploying INT4 models where activation outliers would otherwise cause accuracy loss","researchers studying activation distributions and their impact on quantization","production systems where AWQ provides better INT4 accuracy than GPTQ"],"limitations":["Requires full calibration pass to collect activation statistics; adds 10-20% overhead vs weight-only quantization","Per-channel scaling factors must be stored and applied during inference, adding ~1-2% model size overhead","Outlier detection heuristics are dataset-dependent; poor calibration data leads to suboptimal smoothing","Incompatible with some hardware optimizers that assume uniform weight distributions"],"requires":["Calibration dataset representative of deployment distribution","GPU memory for storing activation statistics (typically 100MB-1GB)","vLLM or compatible inference engine that supports per-channel scaling"],"input_types":["PyTorch model","Calibration dataset","AWQ configuration (smoothing strength, outlier threshold)"],"output_types":["INT4 quantized weights","Per-channel scaling factors","Activation statistics (min/max per channel)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_5","uri":"capability://data.processing.analysis.structured.and.unstructured.pruning.with.layer.wise.sparsity.patterns","name":"structured and unstructured pruning with layer-wise sparsity patterns","description":"Applies structured (removing entire channels/heads) and unstructured (removing individual weights) pruning to reduce model parameters, using a modifier system that targets specific layer patterns and applies sparsity masks. The framework supports magnitude-based pruning, gradient-based pruning, and learned sparsity patterns, with automatic mask generation and application during model inference.","intents":["I want to remove 30% of model parameters while maintaining 99% accuracy","I need to prune attention heads that contribute least to model output","I want to apply different sparsity levels to different layers (e.g., 50% pruning in early layers, 10% in later)"],"best_for":["teams optimizing for inference latency where parameter reduction directly improves speed","researchers studying layer-wise importance and pruning strategies","production systems where sparse models enable better hardware utilization"],"limitations":["Structured pruning (channels/heads) is more hardware-friendly but less flexible; unstructured pruning requires sparse tensor support","Pruning masks are static post-compression; no dynamic sparsity adaptation","Magnitude-based pruning is simple but suboptimal; better results require fine-tuning after pruning","Sparse tensor operations have limited hardware support; speedup depends on inference engine (vLLM has good support)"],"requires":["PyTorch with sparse tensor support (optional, for unstructured pruning)","Fine-tuning data if pursuing accuracy recovery post-pruning","Inference engine with sparse operation support (vLLM recommended)"],"input_types":["PyTorch model","Pruning targets (layer patterns, sparsity levels)","Optional: calibration data for importance estimation"],"output_types":["Pruned model with sparsity masks","Layer-wise sparsity statistics","Importance scores per parameter/channel"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_6","uri":"capability://data.processing.analysis.smoothquant.activation.smoothing.for.mixed.precision.quantization","name":"smoothquant activation smoothing for mixed-precision quantization","description":"Implements SmoothQuant algorithm that smooths activation distributions by transferring quantization difficulty from activations to weights through learned per-channel scaling. The algorithm identifies activation channels with extreme ranges, applies inverse scaling to weights, and forward scaling to activations, enabling lower-precision activation quantization while maintaining weight precision.","intents":["I want to quantize both weights and activations to INT8 while handling activation outliers","I need to reduce activation quantization difficulty by smoothing weight distributions","I want to apply mixed-precision quantization (INT8 weights + INT8 activations) with better accuracy"],"best_for":["teams deploying INT8 weight + INT8 activation quantization for maximum compression","researchers studying activation quantization and outlier handling","production systems where mixed-precision INT8 provides 8x compression with acceptable accuracy"],"limitations":["Requires full calibration pass to collect activation statistics; adds 15-25% overhead","Per-channel scaling factors must be stored and applied during inference, adding model size overhead","Scaling factors are learned per-layer; transferring models between hardware may require recalibration","Incompatible with some quantization-aware training approaches that assume fixed scaling"],"requires":["Calibration dataset with representative activation distributions","GPU memory for activation statistics collection","Inference engine supporting per-channel scaling (vLLM has native support)"],"input_types":["PyTorch model","Calibration dataset","SmoothQuant configuration (smoothing strength, per-channel vs per-token)"],"output_types":["Smoothed model with per-channel scaling factors","INT8 quantized weights and activations","Activation statistics and smoothing metrics"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_7","uri":"capability://data.processing.analysis.autoround.learned.quantization.with.gradient.based.parameter.optimization","name":"autoround learned quantization with gradient-based parameter optimization","description":"Implements AutoRound algorithm that learns optimal quantization parameters (scales, zero-points, rounding) through gradient-based optimization on calibration data. The algorithm treats quantization as a differentiable operation, computes gradients with respect to quantization parameters, and iteratively updates them to minimize reconstruction error, achieving better accuracy than fixed quantization schemes.","intents":["I want to learn optimal quantization parameters instead of using fixed schemes","I need to quantize models to INT4 with better accuracy than magnitude-based approaches","I want to optimize quantization parameters for specific calibration datasets"],"best_for":["teams pursuing maximum accuracy for low-bit quantization (INT4, INT3)","researchers studying learned quantization and parameter optimization","production systems where INT4 accuracy is critical and worth the extra calibration cost"],"limitations":["Gradient-based optimization adds 30-50% calibration time overhead vs one-shot quantization","Requires differentiable quantization operations; some hardware-specific quantizers may not be compatible","Learned parameters are dataset-specific; transferring to different data distributions may degrade accuracy","Hyperparameter tuning (learning rate, optimization steps) needed for best results; no automatic tuning"],"requires":["Calibration dataset with sufficient diversity","GPU with autograd support","Optimization hyperparameters (learning rate, number of steps, loss function)"],"input_types":["PyTorch model","Calibration dataset","AutoRound configuration (bit-width, optimization steps, learning rate)"],"output_types":["Learned quantization parameters (scales, zero-points, rounding modes)","Quantized model","Optimization loss curves and convergence metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_8","uri":"capability://automation.workflow.compression.recipe.specification.and.execution.engine","name":"compression recipe specification and execution engine","description":"Provides a declarative YAML-based recipe system for specifying compression pipelines, where users define modifiers (quantization, pruning, distillation), targets (layer patterns), and parameters without writing code. The execution engine parses recipes, resolves modifier dependencies, validates compatibility, and orchestrates the compression session, enabling reproducible and shareable compression workflows.","intents":["I want to define a compression pipeline in YAML and apply it to multiple models","I need to share compression recipes with team members without requiring code changes","I want to version control compression configurations alongside model checkpoints"],"best_for":["teams standardizing compression workflows across multiple models","organizations with non-technical stakeholders who need to understand compression strategies","researchers publishing compression recipes for reproducibility"],"limitations":["YAML syntax is rigid; complex conditional logic requires workarounds or custom modifiers","Recipe validation is limited; incompatible modifier combinations may only fail at runtime","No built-in recipe composition or inheritance; large recipes become hard to maintain","Limited debugging support; recipe parsing errors can be cryptic"],"requires":["YAML file with valid recipe syntax","Understanding of available modifiers and their parameters","PyTorch model compatible with recipe targets"],"input_types":["YAML recipe file","PyTorch model","Calibration dataset"],"output_types":["Compressed model","Execution log (which modifiers applied, in what order)","Compression metrics (model size reduction, accuracy impact)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__cap_9","uri":"capability://automation.workflow.vllm.native.model.export.with.quantization.metadata.preservation","name":"vllm-native model export with quantization metadata preservation","description":"Exports compressed models in a format optimized for vLLM inference, preserving quantization metadata (scales, zero-points, bit-widths) in safetensors format with custom JSON metadata. The exporter ensures compatibility with vLLM's quantization kernels, validates that exported models can be loaded and inferred, and provides fallback options for unsupported quantization schemes.","intents":["I want to export a quantized model that vLLM can load and run without additional conversion","I need to preserve quantization metadata so vLLM knows how to dequantize weights during inference","I want to validate that my compressed model is compatible with vLLM before deployment"],"best_for":["teams deploying models with vLLM inference engine","organizations standardizing on vLLM for LLM serving","production systems where model export must be fast and reliable"],"limitations":["Export format is vLLM-specific; models may not be compatible with other inference engines without conversion","Quantization metadata validation is limited; some edge cases may not be caught until inference","Large models (70B+) take significant time to export due to safetensors serialization","No built-in support for exporting to other formats (ONNX, TensorRT); requires separate conversion tools"],"requires":["vLLM 0.3.0+ installed","Compressed model in llm-compressor format","Sufficient disk space for safetensors export (typically 1.5-2x model size)"],"input_types":["Compressed PyTorch model","Quantization metadata (scales, zero-points, bit-widths)"],"output_types":["Safetensors model file with quantization metadata","JSON metadata file (quantization scheme, per-layer parameters)","Validation report (compatibility check results)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llmcompressor__headline","uri":"capability://model.training.large.language.model.compression.toolkit","name":"large language model compression toolkit","description":"llmcompressor is a comprehensive toolkit for compressing large language models using advanced techniques like quantization, pruning, and distillation, aimed at optimizing performance on CPUs and GPUs with minimal accuracy loss.","intents":["best model compression toolkit","model compression for deployment","how to compress large language models","efficient model optimization techniques","tools for reducing model size and improving inference"],"best_for":["developers working with large language models","teams deploying models on limited hardware"],"limitations":[],"requires":["basic knowledge of machine learning","PyTorch and Hugging Face frameworks"],"input_types":["large language models"],"output_types":["optimized, compressed models"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"low","permissions":["PyTorch 2.0+","HuggingFace Transformers 4.30+","CUDA 11.8+ for GPU acceleration (CPU fallback available but slow)","Calibration dataset in HuggingFace datasets format or custom DataLoader","Understanding of quantization algorithm differences (GPTQ vs AWQ vs AutoRound)","YAML recipe file defining modifier targets and parameters","Calibration dataset representative of target use case","Multi-GPU setup (minimum 2 GPUs, typically 4+ for large models)","NCCL or similar collective communication library","Distributed PyTorch setup (torch.distributed)"],"failure_modes":["Requires representative calibration dataset (typically 128-512 samples); poor dataset selection degrades accuracy","Sequential tracing adds memory overhead proportional to model size; distributed compression needed for >70B models","Quantization parameters are static post-compression; no dynamic per-batch adaptation","Some model architectures (custom attention, sparse operations) may require custom modifier implementations","Algorithm interactions are not automatically validated; incompatible combinations (e.g., two conflicting weight quantizers) require manual recipe debugging","Modifier ordering matters but is implicit in YAML; circular dependencies or missing dependencies can cause silent failures","No built-in A/B testing framework; comparing schemes requires running separate compression sessions","Distributed compression adds significant complexity; debugging is harder than single-GPU","Communication overhead between GPUs can dominate for small models; only worthwhile for 50B+","Requires careful synchronization of quantization parameters; race conditions can cause subtle bugs","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":"2026-06-17T09:51:04.692Z","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=llmcompressor","compare_url":"https://unfragile.ai/compare?artifact=llmcompressor"}},"signature":"+rdIjQN1irxkdPS+tAPnnRBud0zJ0YX3Ck4/zbUNfkJ5RkFakHyqWyozhEGusnFuhcBDxNt9B55tCse/eizACw==","signedAt":"2026-06-20T18:40:57.893Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llmcompressor","artifact":"https://unfragile.ai/llmcompressor","verify":"https://unfragile.ai/api/v1/verify?slug=llmcompressor","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"}}