{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"autoawq","slug":"autoawq","name":"AutoAWQ","type":"repo","url":"https://github.com/casper-hansen/AutoAWQ","page_url":"https://unfragile.ai/autoawq","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"autoawq__cap_0","uri":"capability://data.processing.analysis.activation.aware.4.bit.weight.quantization.with.minimal.accuracy.loss","name":"activation-aware 4-bit weight quantization with minimal accuracy loss","description":"Implements the AWQ algorithm that identifies and preserves activation-salient weight channels during quantization, using per-channel scaling factors computed from calibration data to maintain model quality. The quantizer analyzes activation patterns across a calibration dataset, applies selective quantization that protects high-impact weights, and stores models in INT4 format while performing FP16 operations during inference, achieving 3x memory reduction and 3x speedup on memory-bound workloads.","intents":["Compress a 70B parameter model to fit on a single consumer GPU without retraining","Reduce inference latency for single-token generation on resource-constrained hardware","Deploy large language models with minimal accuracy degradation compared to full-precision baselines","Reduce model storage and download size for edge deployment scenarios"],"best_for":["ML engineers deploying open-source LLMs on consumer GPUs (RTX 4090, A100)","Teams building inference services with strict memory budgets","Researchers benchmarking quantization trade-offs across model families"],"limitations":["Requires representative calibration dataset (typically 128-512 samples) for accurate scaling factor computation; poor calibration data leads to accuracy degradation","Only supports 4-bit quantization; no support for 3-bit, 8-bit, or mixed-precision variants","Quantization process is one-time offline operation; cannot dynamically adjust quantization parameters post-deployment","Project is officially deprecated as of August 2025; maintenance has moved to vLLM's llm-compressor and MLX-LM"],"requires":["Python 3.9+","PyTorch 2.0+ (last tested with 2.6.0)","Transformers library 4.40+ (last tested with 4.51.3)","NVIDIA CUDA 11.8+ OR AMD ROCm 5.6+ OR Intel CPU/XPU support","Minimum 24GB VRAM for quantizing 70B models during calibration"],"input_types":["Pretrained model weights (HuggingFace format)","Calibration dataset (text samples, typically 128-512 sequences)","Model architecture definition (via Transformers)"],"output_types":["Quantized model weights (INT4 format)","Scaling factors and quantization metadata","Serialized model checkpoint compatible with HuggingFace"],"categories":["data-processing-analysis","model-compression"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_1","uri":"capability://tool.use.integration.multi.architecture.model.registry.with.automatic.implementation.selection","name":"multi-architecture model registry with automatic implementation selection","description":"Implements a factory pattern (AutoAWQForCausalLM) that maintains a registry mapping 35+ model architectures (Llama, Mistral, MPT, Falcon, Qwen, etc.) to their corresponding quantized implementations. The factory automatically detects model type from HuggingFace config and instantiates the correct BaseAWQForCausalLM subclass, handling architecture-specific quantization logic and optimized inference kernels without requiring users to specify implementation details.","intents":["Load and quantize a new open-source model without writing architecture-specific code","Automatically apply AWQ to any Transformers-compatible model by specifying only the model ID","Switch between different model architectures while maintaining identical quantization API","Support new model families as they are added to the Transformers ecosystem"],"best_for":["ML practitioners who want to quantize multiple model architectures with a single codebase","Teams building model-agnostic inference platforms","Researchers comparing quantization effectiveness across model families"],"limitations":["Registry is static and requires code changes to add new architectures; no dynamic plugin system for community contributions","Only supports causal language models; no support for encoder-only (BERT) or encoder-decoder (T5) architectures","Model detection relies on HuggingFace config.model_type field; custom or modified models may not be recognized","Architecture-specific optimizations (fused kernels) are not available for all 35+ supported models; some fall back to generic implementations"],"requires":["HuggingFace Transformers 4.40+","Model config.json with valid model_type field","Model weights in HuggingFace format (safetensors or PyTorch)"],"input_types":["Model ID string (e.g., 'meta-llama/Llama-2-70b-hf')","Local model path","HuggingFace config.json"],"output_types":["Instantiated BaseAWQForCausalLM subclass","Model-specific quantizer instance"],"categories":["tool-use-integration","model-registry"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_10","uri":"capability://image.visual.multimodal.model.quantization.support","name":"multimodal model quantization support","description":"Extends AWQ quantization to vision-language models (e.g., LLaVA, Qwen-VL) by selectively quantizing language model components while preserving vision encoder precision, or applying quantization to both components with architecture-aware scaling. This approach maintains image understanding quality while reducing overall model size and inference latency.","intents":["Compress vision-language models for deployment on resource-constrained devices","Maintain image understanding quality while reducing model size","Deploy multimodal models with lower latency and memory requirements","Support emerging vision-language model architectures"],"best_for":["Teams deploying vision-language models (LLaVA, Qwen-VL) on edge devices","Applications requiring both text and image understanding with strict resource constraints","Researchers exploring quantization trade-offs in multimodal models"],"limitations":["Multimodal quantization is less mature than text-only quantization; fewer models supported and less testing","Vision encoder quantization may degrade image understanding more than text quantization degrades language understanding","Calibration requires multimodal dataset (text + images); harder to obtain than text-only calibration data","No clear guidance on whether to quantize vision encoder, language model, or both; requires experimentation"],"requires":["Vision-language model (LLaVA, Qwen-VL, etc.)","Multimodal calibration dataset (images + text descriptions)","Vision processor and tokenizer"],"input_types":["Model weights (vision encoder + language model)","Calibration images and text","Vision processor"],"output_types":["Quantized vision-language model","Quantization metadata for both components"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_11","uri":"capability://automation.workflow.command.line.quantization.and.inference.interface","name":"command-line quantization and inference interface","description":"Provides awq-cli command-line tools for quantizing models and running inference without writing Python code. Users can specify model ID, calibration dataset, quantization parameters, and output path via command-line arguments, enabling integration with shell scripts, CI/CD pipelines, and non-Python workflows. The CLI abstracts away Python API complexity while maintaining access to all core functionality.","intents":["Quantize models from command line without writing Python code","Integrate model quantization into CI/CD pipelines and automation workflows","Enable non-Python developers to use AutoAWQ","Create reproducible quantization scripts for documentation and sharing"],"best_for":["DevOps engineers integrating quantization into deployment pipelines","Researchers documenting quantization procedures in shell scripts","Teams building model serving platforms with quantization as a preprocessing step"],"limitations":["CLI is less flexible than Python API; advanced customization requires Python code","Error messages may be cryptic for non-Python users; debugging requires understanding Python stack traces","CLI doesn't support interactive workflows; all parameters must be specified upfront","No built-in progress reporting; long quantization jobs provide no feedback until completion"],"requires":["AutoAWQ installed via pip","Shell environment (bash, zsh, etc.)","Model ID and calibration dataset path"],"input_types":["Command-line arguments (model ID, dataset path, output path, etc.)","Calibration dataset (text file or directory)"],"output_types":["Quantized model (saved to output path)","Quantization logs and metrics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_12","uri":"capability://code.generation.editing.custom.model.architecture.extension.and.plugin.system","name":"custom model architecture extension and plugin system","description":"Allows users to extend AutoAWQ with custom model architectures by subclassing BaseAWQForCausalLM and implementing architecture-specific quantization logic. Provides hooks for custom layer quantization, attention patterns, and inference kernels. Enables quantization of proprietary or research models not in the official registry.","intents":["Add AWQ support for a custom or proprietary model architecture","Implement architecture-specific quantization optimizations (e.g., custom attention fusion)","Experiment with quantization techniques on research models","Integrate AutoAWQ into custom model training pipelines"],"best_for":["Researchers working with custom model architectures","Teams with proprietary models needing quantization","Framework developers extending AutoAWQ"],"limitations":["Extension API is not well-documented; requires reading source code to understand hooks","Custom implementations may have bugs or performance issues; no validation framework","No automatic testing of custom implementations; users must validate accuracy and performance","Custom architectures do not benefit from optimized kernels unless explicitly implemented"],"requires":["Understanding of AutoAWQ architecture (BaseAWQForCausalLM, AwqQuantizer)","Knowledge of target model architecture and layer types","Python 3.9+ and PyTorch development environment"],"input_types":["Custom model class inheriting from BaseAWQForCausalLM","Custom quantization logic (layer-specific implementations)"],"output_types":["Quantized custom model","Custom inference kernels (optional)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_2","uri":"capability://data.processing.analysis.calibration.driven.per.channel.scaling.factor.computation","name":"calibration-driven per-channel scaling factor computation","description":"Analyzes activation statistics from a calibration dataset to compute per-channel scaling factors that minimize quantization error for each weight channel independently. The AwqQuantizer processes calibration samples through the model, captures activation magnitudes at each layer, identifies the most important channels based on activation variance, and derives optimal INT4 clipping ranges that preserve high-activation weights at full precision while aggressively quantizing low-activation channels.","intents":["Compute optimal quantization parameters without manual tuning or hyperparameter search","Preserve model accuracy by protecting weights that have high activation magnitudes","Understand which weight channels are most critical for model behavior","Generate quantization metadata (scaling factors, zero-points) for efficient INT4 inference"],"best_for":["Teams quantizing proprietary or domain-specific models where accuracy is critical","Researchers analyzing which model components are activation-salient","Production systems requiring reproducible, data-driven quantization decisions"],"limitations":["Calibration dataset quality directly impacts quantization quality; domain mismatch between calibration and deployment data causes accuracy loss","Requires forward passes through the entire model on calibration data; 128-512 samples typically needed, adding 30-60 minutes to quantization pipeline","Per-channel scaling factors add ~5-10% overhead to model size compared to per-layer or per-tensor quantization","No adaptive calibration; scaling factors are fixed post-quantization and cannot be updated based on deployment data"],"requires":["Calibration dataset with 128-512 representative samples","Tokenizer matching the model's training tokenizer","Sufficient GPU memory to hold model + activation statistics (typically 2x model size)","Python 3.9+"],"input_types":["Raw text samples (list of strings)","Tokenized sequences (list of token IDs)","Model weights and architecture"],"output_types":["Per-channel scaling factors (float32 tensors)","Quantization metadata (zero-points, clipping ranges)","Activation statistics (min/max/variance per channel)"],"categories":["data-processing-analysis","model-compression"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_3","uri":"capability://code.generation.editing.optimized.int4.linear.layer.inference.with.fused.kernels","name":"optimized int4 linear layer inference with fused kernels","description":"Implements specialized WQLinear_* modules (variants for different hardware: GEMM for batch inference, GEMV for single-token generation) that perform INT4 weight dequantization and matrix multiplication in fused CUDA/ROCm kernels. These kernels avoid materializing full FP16 weights in memory, instead keeping weights in INT4 format and dequantizing on-the-fly during computation, reducing memory bandwidth requirements and enabling 3x speedup on memory-bound workloads.","intents":["Achieve 3x inference speedup on consumer GPUs compared to full-precision models","Reduce peak memory usage during inference by keeping weights in INT4 format","Optimize for single-token generation (GEMV) vs batch inference (GEMM) based on deployment scenario","Maintain FP16 precision for activations while using INT4 weights"],"best_for":["Production inference services with strict latency SLAs on consumer GPUs","Real-time chat/API endpoints where single-token latency matters","Edge deployment scenarios with limited GPU memory (8GB-24GB)","Batch inference systems processing multiple requests simultaneously"],"limitations":["Fused kernels are hardware-specific; GEMM/GEMV variants require NVIDIA CUDA 11.8+ or AMD ROCm 5.6+; CPU inference falls back to slow Python implementations","Speedup is primarily for memory-bound operations; compute-bound scenarios (large batch sizes) see minimal benefit","Fused kernels are not available for all 35+ supported architectures; some models use generic implementations with 10-20% less speedup","Requires exact quantization format match; cannot mix INT4 weights from different quantization methods"],"requires":["NVIDIA GPU with CUDA Compute Capability 7.0+ (RTX 2060+) OR AMD GPU with RDNA/CDNA architecture","CUDA 11.8+ or ROCm 5.6+","PyTorch 2.0+ compiled with CUDA/ROCm support","Quantized model weights in AutoAWQ INT4 format"],"input_types":["Quantized model weights (INT4 format)","Scaling factors and zero-points","Input tokens (shape: [batch_size, seq_len])","Optional KV cache for efficient generation"],"output_types":["Logits (shape: [batch_size, seq_len, vocab_size])","Updated KV cache (for autoregressive generation)"],"categories":["code-generation-editing","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_4","uri":"capability://code.generation.editing.fused.attention.and.transformer.block.optimization","name":"fused attention and transformer block optimization","description":"Provides architecture-specific implementations of attention mechanisms and transformer blocks that fuse multiple operations (QKV projection, attention computation, output projection) into single CUDA kernels. These fused blocks reduce kernel launch overhead, improve memory locality, and enable optimizations like in-place operations and reduced intermediate tensor allocations, resulting in 10-20% additional speedup beyond INT4 weight quantization.","intents":["Further reduce inference latency beyond INT4 quantization through operation fusion","Minimize GPU kernel launch overhead for transformer models","Reduce peak memory usage by avoiding intermediate tensor materialization","Optimize attention computation for both prefill and decoding phases"],"best_for":["High-throughput inference services where every millisecond of latency matters","Memory-constrained deployments (8GB-16GB GPUs) where intermediate tensor allocation is a bottleneck","Teams deploying specific model architectures (Llama, Mistral) where fused implementations are available"],"limitations":["Fused implementations are architecture-specific; only available for popular models (Llama, Mistral, Falcon); other architectures fall back to unfused implementations","Fused kernels are not compatible with arbitrary attention variants (e.g., multi-query attention, grouped query attention); requires exact architecture match","Debugging and profiling fused operations is harder than modular implementations; error messages may be cryptic","Fused implementations may not support all features (e.g., attention masks, custom attention patterns) that modular code supports"],"requires":["NVIDIA CUDA 11.8+ or AMD ROCm 5.6+","Model architecture with available fused implementation (Llama, Mistral, MPT, Falcon, Qwen)","PyTorch 2.0+ with CUDA/ROCm support"],"input_types":["Hidden states (shape: [batch_size, seq_len, hidden_dim])","Attention masks (optional)","KV cache from previous tokens (for decoding)"],"output_types":["Attention output (shape: [batch_size, seq_len, hidden_dim])","Updated KV cache"],"categories":["code-generation-editing","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_5","uri":"capability://tool.use.integration.model.loading.from.pretrained.and.quantized.checkpoints","name":"model loading from pretrained and quantized checkpoints","description":"Provides from_pretrained() and from_quantized() factory methods that load models from HuggingFace Hub or local paths, automatically detecting model architecture and instantiating the correct quantizer or inference engine. from_pretrained() loads full-precision models for quantization, while from_quantized() loads pre-quantized INT4 checkpoints with scaling factors and metadata, enabling both quantization and inference workflows through a unified API.","intents":["Load any HuggingFace model for quantization with a single line of code","Load pre-quantized models from HuggingFace Hub or local storage for immediate inference","Automatically handle model architecture detection and implementation selection","Share quantized models with others via HuggingFace Hub"],"best_for":["ML practitioners who want to quantize models without understanding architecture details","Teams building model serving platforms that need to support multiple model families","Researchers sharing quantized models with the community"],"limitations":["from_pretrained() requires sufficient GPU memory to load full-precision model; 70B models need 140GB VRAM (FP16)","Model detection relies on HuggingFace config.model_type; custom models or modified architectures may fail to load","from_quantized() only works with models quantized by AutoAWQ; incompatible with GPTQ, bitsandbytes, or other quantization formats","No support for loading models from private HuggingFace repositories without explicit token authentication"],"requires":["HuggingFace Transformers 4.40+","Model available on HuggingFace Hub or local path","HuggingFace API token for private models (optional)","Sufficient GPU memory for full-precision model (from_pretrained) or 25% of model size (from_quantized)"],"input_types":["Model ID string (e.g., 'meta-llama/Llama-2-70b-hf')","Local model path","HuggingFace API token (optional)"],"output_types":["Loaded model instance (BaseAWQForCausalLM subclass)","Model config and tokenizer"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_6","uri":"capability://automation.workflow.quantization.aware.model.serialization.and.checkpoint.management","name":"quantization-aware model serialization and checkpoint management","description":"Implements save_quantized() method that serializes quantized models with INT4 weights, scaling factors, zero-points, and quantization metadata into HuggingFace-compatible format (safetensors or PyTorch). The serialization preserves all information needed for inference while maintaining compatibility with HuggingFace Hub, enabling users to share quantized models and load them with from_quantized() without re-quantizing.","intents":["Save quantized models to disk for later inference without re-quantizing","Share quantized models on HuggingFace Hub with community","Version control quantized checkpoints alongside original models","Load quantized models from local storage or HuggingFace Hub"],"best_for":["Teams building model zoos of quantized models","Researchers sharing quantized baselines with the community","Production systems that need to persist quantized models across deployments"],"limitations":["Serialized quantized models are ~25% of original size but still require 17.5GB for 70B models; not suitable for extreme edge cases","Quantization metadata (scaling factors) adds ~5-10% overhead compared to raw INT4 weights","No built-in versioning or metadata tracking; users must manually manage quantization parameters (calibration dataset, clipping strategy)","Safetensors format support requires transformers 4.40+; older versions fall back to slower PyTorch format"],"requires":["Quantized model instance (from quantization process)","Local filesystem with sufficient space (25% of original model size)","HuggingFace account and API token for Hub uploads (optional)"],"input_types":["Quantized model instance","Output path (local or HuggingFace Hub)","Optional metadata (description, tags)"],"output_types":["Serialized model files (safetensors or PyTorch format)","config.json with quantization metadata","Model card (optional)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_7","uri":"capability://automation.workflow.benchmark.and.performance.profiling.utilities","name":"benchmark and performance profiling utilities","description":"Provides command-line tools and Python APIs for benchmarking quantized models across different hardware configurations, measuring throughput (tokens/second), latency (ms/token), and memory usage. The benchmark suite compares quantized vs full-precision models, profiles different batch sizes and sequence lengths, and generates performance reports that help users understand trade-offs between compression and speed.","intents":["Measure inference speedup from quantization on specific hardware","Compare latency and throughput across different batch sizes","Profile memory usage before and after quantization","Generate performance reports for deployment planning"],"best_for":["Teams evaluating whether quantization is worth the accuracy trade-off for their use case","ML engineers optimizing inference performance on specific hardware","Researchers benchmarking quantization methods across model families"],"limitations":["Benchmarks measure inference only; quantization time is not included in performance metrics","Benchmark results are hardware-specific; speedup on RTX 4090 may not translate to A100 or consumer GPUs","Benchmarks assume ideal conditions (no other processes, full GPU utilization); real-world performance may vary","No built-in accuracy benchmarking; users must separately evaluate perplexity or task-specific metrics"],"requires":["Quantized model loaded via from_quantized()","Tokenizer matching the model","GPU with sufficient memory for inference","Optional: calibration dataset for accuracy evaluation"],"input_types":["Model instance","Batch size (int)","Sequence length (int)","Number of iterations (int)"],"output_types":["Throughput (tokens/second)","Latency (ms/token)","Memory usage (GB)","Performance report (JSON or CSV)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_8","uri":"capability://tool.use.integration.multi.hardware.backend.support.with.automatic.selection","name":"multi-hardware backend support with automatic selection","description":"Abstracts hardware-specific implementations (NVIDIA CUDA, AMD ROCm, Intel CPU/XPU) behind a unified Python API that automatically detects available hardware and selects the appropriate backend. The framework compiles optimized kernels for each platform during installation, enabling the same Python code to run on different hardware without modification while maintaining performance characteristics.","intents":["Deploy quantized models across different GPU types (NVIDIA, AMD) without code changes","Support CPU inference as a fallback when GPU is unavailable","Automatically select the fastest available backend for the current hardware","Enable cross-platform model serving (cloud GPUs, edge devices, consumer hardware)"],"best_for":["Teams deploying models across heterogeneous hardware (mix of NVIDIA and AMD GPUs)","Cloud platforms supporting multiple GPU types (AWS, GCP, Azure)","Edge deployment scenarios where hardware varies by device"],"limitations":["Installation requires compilation of hardware-specific kernels; pre-built wheels are only available for NVIDIA CUDA 11.8/12.1 and ROCm 5.6/5.7; other configurations require building from source (30-60 minutes)","CPU inference is 10-100x slower than GPU; only suitable for low-throughput scenarios","Intel XPU support is experimental and not well-tested; performance characteristics are unknown","Automatic backend selection may choose suboptimal backend if multiple are available; no manual override mechanism"],"requires":["NVIDIA CUDA 11.8+ with NVIDIA GPU (Compute Capability 7.0+) OR AMD ROCm 5.6+ with RDNA/CDNA GPU OR Intel CPU with XPU support","PyTorch 2.0+ compiled for the target backend","Pre-built wheels or build tools (gcc, cmake) for source compilation"],"input_types":["Quantized model","Hardware configuration (auto-detected)"],"output_types":["Backend instance (CUDA, ROCm, or CPU)","Optimized inference kernels for selected backend"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__cap_9","uri":"capability://code.generation.editing.llama.and.mistral.family.model.specialization","name":"llama and mistral family model specialization","description":"Implements architecture-specific quantization and inference optimizations for Llama (1/2/3) and Mistral models, including fused attention blocks, grouped query attention (GQA) support, and RoPE position encoding optimizations. These specializations leverage knowledge of model-specific design patterns to achieve better compression and faster inference than generic implementations.","intents":["Quantize Llama or Mistral models with maximum accuracy preservation","Achieve fastest inference on Llama/Mistral models through architecture-specific optimizations","Support grouped query attention and other Llama/Mistral-specific features","Maintain compatibility with Llama/Mistral ecosystem tools and workflows"],"best_for":["Teams deploying Llama or Mistral models in production","Researchers fine-tuning Llama/Mistral and needing efficient inference","Users wanting maximum performance on the most popular open-source models"],"limitations":["Specializations are only available for Llama and Mistral; other architectures use generic implementations with 10-20% less performance","Grouped query attention support adds complexity; may not work correctly with all GQA variants","RoPE optimizations assume standard RoPE implementation; custom position encodings may not be supported"],"requires":["Llama or Mistral model from HuggingFace Hub","Model config with correct architecture type (llama or mistral)"],"input_types":["Llama or Mistral model weights","Calibration dataset"],"output_types":["Quantized Llama/Mistral model with fused kernels","Optimized inference engine"],"categories":["code-generation-editing","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"autoawq__headline","uri":"capability://model.training.activation.aware.weight.quantization.for.large.language.models","name":"activation-aware weight quantization for large language models","description":"AutoAWQ is a user-friendly package that implements Activation-aware Weight Quantization, allowing large language models to be compressed to 4-bit precision, making them suitable for consumer GPUs while preserving their performance quality.","intents":["best quantization tool for LLMs","4-bit quantization for large models","Activation-aware Weight Quantization for inference","how to compress LLMs for GPU usage","best practices for model quantization"],"best_for":["developers needing efficient model deployment"],"limitations":["deprecated and no longer maintained"],"requires":["PyTorch","compatible GPU"],"input_types":["large language models"],"output_types":["quantized models"],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PyTorch 2.0+ (last tested with 2.6.0)","Transformers library 4.40+ (last tested with 4.51.3)","NVIDIA CUDA 11.8+ OR AMD ROCm 5.6+ OR Intel CPU/XPU support","Minimum 24GB VRAM for quantizing 70B models during calibration","HuggingFace Transformers 4.40+","Model config.json with valid model_type field","Model weights in HuggingFace format (safetensors or PyTorch)","Vision-language model (LLaVA, Qwen-VL, etc.)","Multimodal calibration dataset (images + text descriptions)"],"failure_modes":["Requires representative calibration dataset (typically 128-512 samples) for accurate scaling factor computation; poor calibration data leads to accuracy degradation","Only supports 4-bit quantization; no support for 3-bit, 8-bit, or mixed-precision variants","Quantization process is one-time offline operation; cannot dynamically adjust quantization parameters post-deployment","Project is officially deprecated as of August 2025; maintenance has moved to vLLM's llm-compressor and MLX-LM","Registry is static and requires code changes to add new architectures; no dynamic plugin system for community contributions","Only supports causal language models; no support for encoder-only (BERT) or encoder-decoder (T5) architectures","Model detection relies on HuggingFace config.model_type field; custom or modified models may not be recognized","Architecture-specific optimizations (fused kernels) are not available for all 35+ supported models; some fall back to generic implementations","Multimodal quantization is less mature than text-only quantization; fewer models supported and less testing","Vision encoder quantization may degrade image understanding more than text quantization degrades language understanding","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"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":"2026-06-17T09:51:02.370Z","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=autoawq","compare_url":"https://unfragile.ai/compare?artifact=autoawq"}},"signature":"c86QbmLa9SyMxRZOKFoXjIz7dxS3L5X0AO5LT+orjBBtUfGmkCKzLw+IEGaC+t7b7NRoFd+5eFBQxubn+M6NBw==","signedAt":"2026-06-21T13:09:30.040Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/autoawq","artifact":"https://unfragile.ai/autoawq","verify":"https://unfragile.ai/api/v1/verify?slug=autoawq","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"}}