{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-lyogavin--airllm","slug":"lyogavin--airllm","name":"airllm","type":"repo","url":"https://github.com/lyogavin/airllm","page_url":"https://unfragile.ai/lyogavin--airllm","categories":["model-training"],"tags":["chinese-llm","chinese-nlp","finetune","generative-ai","instruct-gpt","instruction-set","llama","llm","lora","open-models","open-source","open-source-models","qlora"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-lyogavin--airllm__cap_0","uri":"capability://automation.workflow.layer.wise.model.sharding.for.memory.constrained.inference","name":"layer-wise model sharding for memory-constrained inference","description":"Decomposes large language models (70B+ parameters) into individual transformer layers that are loaded into GPU memory only when needed during forward passes, then unloaded after computation completes. Uses a layer-by-layer execution strategy where each layer is fetched from disk storage, processed with its input activations, and immediately freed, reducing peak memory footprint from full model size to single-layer size. This architectural approach enables 70B models to run on 4GB VRAM without quantization or distillation.","intents":["Run 70B parameter models on consumer GPUs with 4GB VRAM","Avoid model quantization/distillation that degrades accuracy","Minimize GPU memory footprint for inference-only workloads","Deploy large models on edge devices with limited VRAM"],"best_for":["developers deploying inference on consumer-grade GPUs","researchers requiring full-precision model evaluation","edge computing scenarios with strict memory constraints","teams avoiding quantization accuracy trade-offs"],"limitations":["Layer loading/unloading introduces I/O latency — disk speed becomes bottleneck","Requires fast storage (NVMe SSD recommended) for acceptable inference speed","No built-in batching across multiple sequences — single-sequence inference only","Prefetching adds complexity; benefits diminish on slow storage","Not suitable for real-time applications requiring sub-100ms latency"],"requires":["Python 3.8+","PyTorch 1.13+","4GB+ GPU VRAM (tested on NVIDIA/AMD/Apple Silicon)","Disk storage equal to model size (e.g., 140GB for 70B model)","NVMe SSD or fast storage for optimal performance"],"input_types":["text prompts","token sequences","model weights (HuggingFace format)"],"output_types":["text completions","token logits","hidden states"],"categories":["automation-workflow","memory-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lyogavin--airllm__cap_1","uri":"capability://automation.workflow.adaptive.prefetching.with.computation.i.o.overlap","name":"adaptive prefetching with computation-i/o overlap","description":"Overlaps disk I/O operations with GPU computation by prefetching the next transformer layer while the current layer is being processed. Uses a background I/O thread that predicts which layer will be needed next and loads it into a staging buffer during the current layer's forward pass, reducing idle GPU time. Achieves approximately 10% inference speed improvement by hiding disk latency behind computation.","intents":["Reduce inference latency caused by layer loading delays","Maximize GPU utilization during I/O-bound operations","Improve throughput on storage-constrained systems","Minimize wall-clock time for single-sequence inference"],"best_for":["systems with slow storage where I/O is the primary bottleneck","inference pipelines where 10% latency reduction is meaningful","multi-layer models where prefetching window is sufficient"],"limitations":["10% improvement assumes computation time > I/O time; benefit diminishes on fast NVMe","Requires additional memory for staging buffer (typically 1-2 layer sizes)","Prefetching prediction is sequential — cannot optimize for branching/dynamic layer selection","No adaptive prefetching based on actual I/O latency — fixed strategy"],"requires":["Multi-threaded Python environment","Sufficient free GPU memory for staging buffer","Sequential layer execution pattern"],"input_types":["layer execution schedule","disk I/O patterns"],"output_types":["prefetched layer tensors","latency metrics"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lyogavin--airllm__cap_10","uri":"capability://data.processing.analysis.model.agnostic.layer.extraction.and.transformer.architecture.introspection","name":"model-agnostic layer extraction and transformer architecture introspection","description":"Provides utilities to introspect transformer model architectures and automatically extract layer definitions from model configs. Uses config.json inspection to identify layer count, hidden dimensions, attention heads, and other architectural parameters. Supports dynamic layer extraction for models with non-standard layer structures. Enables programmatic access to layer boundaries and architectural metadata.","intents":["Automatically determine layer count and structure from model config","Extract architectural parameters without manual specification","Support models with custom layer definitions","Enable dynamic layer sharding for new model architectures"],"best_for":["framework developers adding new model support","research on model architecture analysis","automated model optimization pipelines","teams building model-agnostic tools"],"limitations":["Introspection relies on standard config.json format — custom models may not be detectable","No support for models with dynamic layer counts or conditional layers","Limited to transformer architectures — non-transformer models not supported","Architectural metadata extraction is best-effort — some parameters may be inferred incorrectly","No validation of extracted parameters against actual model weights"],"requires":["Model config.json file","Standard transformer architecture naming conventions"],"input_types":["model config.json","model weights (optional for validation)"],"output_types":["architectural parameters","layer definitions","metadata JSON"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lyogavin--airllm__cap_2","uri":"capability://data.processing.analysis.block.wise.weight.only.quantization.with.optional.4.bit.8.bit.compression","name":"block-wise weight-only quantization with optional 4-bit/8-bit compression","description":"Applies optional block-wise quantization to model weights only (not activations) to reduce model disk footprint and loading time, offering 4-bit or 8-bit quantization modes. Unlike traditional quantization that quantizes both weights and activations, this approach preserves activation precision during inference, maintaining model accuracy while achieving up to 3x inference speed improvement through reduced I/O overhead. Quantization is applied during model decomposition and stored per-layer on disk.","intents":["Reduce model disk footprint to fit on smaller storage devices","Speed up layer loading by reducing I/O data volume","Maintain model accuracy while reducing memory bandwidth requirements","Trade off compression ratio vs inference speed based on hardware constraints"],"best_for":["storage-constrained deployments (mobile, edge devices)","systems with slow storage where I/O bandwidth is limited","inference scenarios where 3x speed improvement justifies minor accuracy loss","teams willing to accept <1% accuracy degradation for speed gains"],"limitations":["Quantization introduces ~0.5-2% accuracy degradation depending on model and bit-width","4-bit quantization more aggressive than 8-bit; requires careful calibration","Block-wise quantization less effective than per-channel for some architectures","No dynamic quantization — fixed bit-width for entire model","Dequantization overhead during layer loading may offset I/O savings on fast storage"],"requires":["Quantization calibration dataset (typically 100-1000 samples)","Support for target bit-width in model architecture","Additional disk space during quantization process"],"input_types":["full-precision model weights","quantization configuration (4-bit or 8-bit)"],"output_types":["quantized weight tensors","quantization scales and zero-points"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lyogavin--airllm__cap_3","uri":"capability://tool.use.integration.automatic.model.architecture.detection.and.platform.specific.optimization","name":"automatic model architecture detection and platform-specific optimization","description":"Provides a unified AutoModel interface that automatically detects model architecture (Llama, ChatGLM, QWen, Baichuan, Mistral, Mixtral, InternLM) from model config and instantiates the appropriate implementation. Includes platform-specific optimizations: uses MLX framework on macOS for native Apple Silicon acceleration, CUDA on NVIDIA GPUs, and ROCm on AMD GPUs. Abstracts away platform differences through a single Python API.","intents":["Load and run models without manual architecture-specific code","Automatically leverage platform-specific optimizations (MLX on macOS)","Support multiple model families with single codebase","Simplify model loading for non-expert users"],"best_for":["developers building cross-platform inference applications","MacBook users wanting native MLX acceleration","teams supporting multiple model architectures","rapid prototyping scenarios requiring minimal boilerplate"],"limitations":["Architecture detection relies on config.json structure — custom models may not auto-detect","MLX optimization on macOS adds dependency on Apple-specific framework","Platform-specific code paths may have feature parity gaps","No fallback mechanism if platform-specific implementation is unavailable","Limited to supported architectures — custom transformer variants not supported"],"requires":["Model config.json with architecture identifier","Platform-specific dependencies (MLX for macOS, CUDA/ROCm for GPU)","HuggingFace model format compatibility"],"input_types":["model path or HuggingFace model ID","model configuration JSON"],"output_types":["instantiated model object","platform-specific inference engine"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lyogavin--airllm__cap_4","uri":"capability://memory.knowledge.model.decomposition.and.layer.persistence.with.disk.based.storage","name":"model decomposition and layer persistence with disk-based storage","description":"Decomposes full models into individual transformer layers during first run and persists each layer as a separate disk artifact in a structured directory hierarchy. Uses PyTorch's state_dict serialization to save layer weights, biases, and normalization parameters independently. Subsequent runs load layers on-demand from disk without redecomposition. Supports both full-precision and quantized layer storage with metadata tracking.","intents":["Convert full models to layer-sharded format for memory-efficient inference","Cache decomposed layers to avoid recomputation on subsequent runs","Enable selective layer loading without loading entire model","Support model versioning and layer-level caching strategies"],"best_for":["one-time model preparation workflows","inference systems requiring repeated model loads","scenarios where decomposition latency is acceptable (minutes)","teams with sufficient disk storage for layer artifacts"],"limitations":["First-run decomposition is slow — 70B model takes 10-30 minutes depending on storage","Requires disk space equal to model size (140GB for 70B model uncompressed)","No incremental updates — any model change requires full redecomposition","Layer persistence format is not standardized — incompatible with other frameworks","No built-in versioning or layer deduplication across model variants"],"requires":["Sufficient disk space (1.5-2x model size during decomposition)","Write permissions to decomposition directory","PyTorch 1.13+ for state_dict serialization","30+ minutes for 70B model decomposition"],"input_types":["full model weights (HuggingFace format)","model configuration"],"output_types":["layer-wise weight files","metadata JSON","quantization parameters (if applicable)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lyogavin--airllm__cap_5","uri":"capability://tool.use.integration.multi.model.architecture.support.with.unified.inference.interface","name":"multi-model architecture support with unified inference interface","description":"Provides architecture-specific implementations for 8+ transformer variants (Llama, ChatGLM, QWen, Baichuan, Mistral, Mixtral, InternLM) while exposing a unified inference interface. Each architecture has custom layer definitions that respect model-specific attention mechanisms, activation functions, and normalization schemes. Unified interface handles tokenization, prompt formatting, and output parsing consistently across all supported models.","intents":["Run different model families without rewriting inference code","Support emerging model architectures by adding architecture-specific implementations","Maintain consistent API across models with different internal structures","Enable model comparison and benchmarking with identical inference code"],"best_for":["teams evaluating multiple model families","inference platforms supporting model selection at runtime","research comparing model architectures","production systems requiring model flexibility"],"limitations":["Limited to 8 supported architectures — custom models require implementation","Architecture-specific optimizations may not be equally effective across all models","Unified interface abstracts away model-specific features (e.g., MoE routing in Mixtral)","No automatic architecture extension mechanism — requires code changes for new models","Performance characteristics vary significantly across architectures"],"requires":["Model config.json matching one of 8 supported architectures","Architecture-specific tokenizer","Model weights in HuggingFace format"],"input_types":["text prompts","model selection parameter"],"output_types":["text completions","token logits"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lyogavin--airllm__cap_6","uri":"capability://text.generation.language.long.context.model.support.with.extended.sequence.handling","name":"long-context model support with extended sequence handling","description":"Provides explicit support for models with extended context windows (e.g., 32K, 100K token contexts) through optimized attention computation and memory management. Handles long sequences by managing KV-cache memory more efficiently during layer-wise inference, avoiding full KV-cache materialization. Supports position interpolation and other long-context techniques at the layer level.","intents":["Run long-context models (32K+ tokens) on memory-constrained hardware","Process documents longer than standard 4K context windows","Maintain context across long conversations without truncation","Avoid KV-cache explosion that typically requires 24GB+ VRAM"],"best_for":["document analysis and summarization tasks","long-form conversation systems","retrieval-augmented generation with large context","research on long-context model behavior"],"limitations":["Long-context inference is slower than short-context due to quadratic attention complexity","KV-cache still grows with sequence length — memory savings are relative, not absolute","Position interpolation may degrade performance on out-of-distribution lengths","No sparse attention or other advanced long-context optimizations","Prefetching strategy may be suboptimal for very long sequences"],"requires":["Model with long-context support (e.g., Llama 2 with 32K context)","Sufficient GPU memory for extended KV-cache (8GB+ recommended)","Long-context-aware tokenizer"],"input_types":["text prompts up to model's context length","long documents"],"output_types":["text completions","context-aware responses"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lyogavin--airllm__cap_7","uri":"capability://planning.reasoning.direct.preference.optimization.dpo.training.with.rlhf.integration","name":"direct preference optimization (dpo) training with rlhf integration","description":"Provides Direct Preference Optimization training framework as an alternative to traditional RLHF with PPO. DPO eliminates the need for a separate reward model by directly optimizing model weights based on preference pairs (chosen vs rejected completions). Implements preference loss computation, gradient accumulation, and training loops optimized for limited GPU memory. Includes dataset preparation utilities for converting preference data into DPO format.","intents":["Fine-tune models using human preference data without reward model training","Improve model alignment with less GPU memory than PPO-based RLHF","Reduce training complexity by eliminating separate reward model","Stabilize training compared to traditional RLHF approaches"],"best_for":["teams with preference data but no reward model","resource-constrained fine-tuning scenarios","alignment research requiring preference optimization","production model improvement with human feedback"],"limitations":["Requires preference pairs (chosen/rejected) rather than scalar rewards","DPO assumes preference data is well-calibrated — noisy labels degrade performance","No built-in preference data collection or annotation tools","Training stability depends on hyperparameter tuning (beta parameter critical)","Limited to models that can fit in GPU memory during training (even with layer-sharding)"],"requires":["Preference dataset with (prompt, chosen, rejected) tuples","GPU with 8GB+ VRAM for training","PyTorch 1.13+","HuggingFace transformers library"],"input_types":["preference dataset (JSON/CSV format)","base model weights","training configuration"],"output_types":["fine-tuned model weights","training metrics (loss, accuracy)","preference prediction scores"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lyogavin--airllm__cap_8","uri":"capability://automation.workflow.macos.native.inference.with.mlx.framework.acceleration","name":"macos-native inference with mlx framework acceleration","description":"Provides native macOS support through integration with Apple's MLX framework, enabling optimized inference on Apple Silicon (M1/M2/M3) GPUs. Automatically detects macOS platform and routes inference through MLX backend instead of CUDA/ROCm, leveraging Metal Performance Shaders for GPU acceleration. Maintains layer-sharding architecture while using MLX's memory-efficient tensor operations.","intents":["Run 70B models on MacBook Pro/Max with native acceleration","Avoid CUDA/ROCm dependencies on macOS","Leverage Apple Silicon GPU for inference without external GPU","Enable local LLM inference on consumer MacBooks"],"best_for":["MacBook users wanting local LLM inference","Apple Silicon developers building LLM applications","teams avoiding cloud inference costs on macOS","research on Apple Silicon LLM performance"],"limitations":["MLX is macOS-only — no cross-platform portability","MLX ecosystem is smaller than PyTorch — fewer third-party integrations","Performance depends on Apple Silicon GPU memory (8GB base, up to 192GB on Max)","Some model architectures may have incomplete MLX implementations","Requires MLX installation and compatibility with model architecture"],"requires":["macOS 12.0+","Apple Silicon GPU (M1 or later)","MLX framework installed","8GB+ unified memory (16GB+ recommended for 70B models)"],"input_types":["text prompts","model weights in MLX-compatible format"],"output_types":["text completions","MLX tensor outputs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-lyogavin--airllm__cap_9","uri":"capability://text.generation.language.inference.api.with.streaming.and.batch.compatible.output.generation","name":"inference api with streaming and batch-compatible output generation","description":"Provides a Python inference API that supports both streaming and non-streaming text generation modes. Implements token-by-token generation with configurable sampling strategies (temperature, top-k, top-p), stopping criteria, and output formatting. Handles prompt tokenization, special token insertion, and response parsing automatically. Supports both single-sequence and batch inference patterns through a unified generate() interface.","intents":["Generate text completions from prompts with configurable sampling","Stream tokens in real-time for interactive applications","Control generation behavior via temperature and top-k/top-p parameters","Integrate inference into Python applications with minimal boilerplate"],"best_for":["Python developers building LLM applications","chatbot and conversational AI systems","text generation pipelines requiring sampling control","interactive applications needing real-time token streaming"],"limitations":["Streaming adds latency overhead — first token latency increases by 10-50ms","Batch inference not optimized — processes sequences sequentially","No built-in beam search or other advanced decoding strategies","Sampling strategies are basic (temperature, top-k, top-p) — no nucleus sampling variants","No support for constrained decoding or grammar-based generation"],"requires":["Python 3.8+","Model loaded via AutoModel interface","Tokenizer compatible with model"],"input_types":["text prompts","generation configuration (temperature, max_tokens, etc.)"],"output_types":["text completions","token streams (for streaming mode)","generation metadata (tokens, logits)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+","4GB+ GPU VRAM (tested on NVIDIA/AMD/Apple Silicon)","Disk storage equal to model size (e.g., 140GB for 70B model)","NVMe SSD or fast storage for optimal performance","Multi-threaded Python environment","Sufficient free GPU memory for staging buffer","Sequential layer execution pattern","Model config.json file","Standard transformer architecture naming conventions"],"failure_modes":["Layer loading/unloading introduces I/O latency — disk speed becomes bottleneck","Requires fast storage (NVMe SSD recommended) for acceptable inference speed","No built-in batching across multiple sequences — single-sequence inference only","Prefetching adds complexity; benefits diminish on slow storage","Not suitable for real-time applications requiring sub-100ms latency","10% improvement assumes computation time > I/O time; benefit diminishes on fast NVMe","Requires additional memory for staging buffer (typically 1-2 layer sizes)","Prefetching prediction is sequential — cannot optimize for branching/dynamic layer selection","No adaptive prefetching based on actual I/O latency — fixed strategy","Introspection relies on standard config.json format — custom models may not be detectable","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7186218226180994,"quality":0.32,"ecosystem":0.6000000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:21.998Z","last_commit":"2026-03-10T11:42:34Z"},"community":{"stars":17089,"forks":1843,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lyogavin--airllm","compare_url":"https://unfragile.ai/compare?artifact=lyogavin--airllm"}},"signature":"lIRMPDZ5e0CX/Sf1nmOs747o9mD+WD73QhQ4PUBRJa2BI7eEWsMdv3QWQbXmF2jpuLeWT2PwYYaVQd712fv8CQ==","signedAt":"2026-06-23T03:03:42.974Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lyogavin--airllm","artifact":"https://unfragile.ai/lyogavin--airllm","verify":"https://unfragile.ai/api/v1/verify?slug=lyogavin--airllm","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"}}