{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-bitnet-cpp","slug":"bitnet-cpp","name":"bitnet.cpp","type":"framework","url":"https://github.com/microsoft/BitNet","page_url":"https://unfragile.ai/bitnet-cpp","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-bitnet-cpp__cap_0","uri":"capability://data.processing.analysis.1.bit.ternary.weight.quantization.with.lookup.table.matrix.operations","name":"1-bit ternary weight quantization with lookup table matrix operations","description":"Implements BitNet b1.58 ternary quantization (-1, 0, +1) using lookup table (LUT) based matrix operations instead of traditional floating-point arithmetic. The framework converts full-precision weights to ternary representations and uses specialized kernels that perform matrix multiplications through efficient table lookups, eliminating expensive arithmetic operations and reducing memory bandwidth requirements by 16x compared to FP32.","intents":["Deploy 100B+ parameter models on single CPU hardware at inference speeds of 5-7 tokens/second","Reduce model memory footprint and energy consumption by 55-82% while maintaining output quality","Run LLMs on edge devices and resource-constrained environments without quality degradation"],"best_for":["Edge device developers deploying LLMs on ARM/x86 CPUs without GPU access","Teams optimizing inference cost and energy consumption for large-scale deployments","Researchers validating 1-bit quantization effectiveness on production models"],"limitations":["Limited to BitNet b1.58 and compatible 1-bit/1.58-bit models; cannot quantize arbitrary LLMs","LUT-based approach requires model-specific kernel generation; not plug-and-play with standard GGUF models","Experimental GPU support (W2A8 CUDA kernels) lacks production maturity and optimization"],"requires":["BitNet b1.58 model in HuggingFace format or safetensors","Python 3.8+ for model conversion pipeline","C++ compiler with AVX2 (x86) or NEON (ARM) support for kernel compilation"],"input_types":["HuggingFace model checkpoints","safetensors format weights","GGUF format (after conversion)"],"output_types":["Quantized GGUF model files","Architecture-specific binary kernels","Token sequences (inference output)"],"categories":["data-processing-analysis","quantization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bitnet-cpp__cap_1","uri":"capability://code.generation.editing.architecture.specific.kernel.code.generation.and.selection","name":"architecture-specific kernel code generation and selection","description":"Automatically detects CPU architecture (ARM64 with NEON, x86_64 with AVX2) and generates or selects optimized quantization kernels (I2_S portable baseline, TL1 for ARM, TL2 for x86). The framework uses a code generation pipeline that produces architecture-specific assembly-level optimizations, with runtime selection ensuring the fastest kernel variant runs on detected hardware without manual configuration.","intents":["Deploy the same model binary across heterogeneous hardware (ARM servers, x86 laptops, edge devices) with automatic performance optimization","Eliminate manual kernel tuning by auto-selecting the fastest quantization scheme for detected CPU architecture","Generate custom kernels for new architectures without modifying core inference engine"],"best_for":["DevOps teams managing multi-architecture deployments (cloud + edge)","Hardware vendors optimizing inference for specific CPU instruction sets","Developers building portable LLM inference without architecture-specific code branches"],"limitations":["Kernel generation adds ~5-10 minutes to first-run setup; not suitable for real-time model loading","Limited to ARM64 (NEON) and x86_64 (AVX2); no support for older CPUs or other ISAs (RISC-V, PowerPC)","Custom kernel configuration requires understanding of quantization schemes and CPU microarchitecture"],"requires":["C++ compiler with target architecture support (GCC 9+ or Clang 10+)","CMake 3.15+ for build system","Target CPU with NEON (ARM) or AVX2 (x86) instruction set"],"input_types":["Quantized GGUF model","CPU architecture detection (automatic)","Optional custom kernel configuration files"],"output_types":["Compiled binary kernels (.so/.dll)","Kernel selection metadata","Performance telemetry (speedup metrics)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bitnet-cpp__cap_10","uri":"capability://code.generation.editing.multi.quantization.scheme.abstraction.with.automatic.selection","name":"multi-quantization scheme abstraction with automatic selection","description":"Abstracts three quantization schemes (I2_S portable baseline, TL1 ARM-optimized, TL2 x86-optimized) behind unified interface that automatically selects fastest variant for detected architecture. The abstraction layer decouples quantization algorithm from hardware implementation, enabling new schemes to be added without modifying inference engine, and allows runtime selection based on CPU capabilities.","intents":["Support multiple quantization schemes optimized for different architectures without code duplication","Add new quantization schemes without modifying core inference engine","Automatically select fastest quantization variant for any CPU without user intervention"],"best_for":["Hardware vendors implementing quantization schemes for specific CPUs","Researchers exploring quantization algorithm design space","Teams deploying across heterogeneous hardware requiring automatic optimization"],"limitations":["Abstraction adds small overhead (~1-2%) for scheme selection and dispatch","Each new scheme requires separate kernel implementation; no code sharing between schemes","Scheme selection is static at compile time; cannot switch schemes at runtime"],"requires":["C++ compiler with template support for abstraction layer","Separate kernel implementation for each quantization scheme","Architecture detection at build time"],"input_types":["Quantized model weights","Architecture detection (automatic)"],"output_types":["Selected quantization scheme identifier","Compiled kernels for selected scheme"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bitnet-cpp__cap_2","uri":"capability://data.processing.analysis.model.conversion.from.huggingface.to.quantized.gguf.format","name":"model conversion from huggingface to quantized gguf format","description":"Provides Python-based conversion pipeline (convert-hf-to-gguf-bitnet.py) that transforms HuggingFace checkpoints and safetensors format models into GGUF format with 1-bit quantization applied. The pipeline handles weight extraction, ternary quantization, embedding layer processing, and metadata serialization, integrating with llama.cpp's GGUF specification while adding BitNet-specific quantization metadata for kernel selection.","intents":["Convert publicly available BitNet b1.58 models from HuggingFace to inference-ready format","Batch convert multiple model checkpoints with consistent quantization parameters","Preserve model metadata and tokenizer configuration during format conversion"],"best_for":["ML engineers preparing models for production deployment","Researchers benchmarking BitNet models across different hardware","Teams automating model pipeline from HuggingFace to inference servers"],"limitations":["Only supports BitNet b1.58 and compatible 1-bit models; cannot convert arbitrary LLMs","Requires full model weights in memory during conversion; impractical for models >100GB unquantized","Embedding quantization is optional and may require manual tuning for optimal quality"],"requires":["Python 3.8+","PyTorch or transformers library for model loading","Sufficient disk space for both source and converted models","HuggingFace model access (public or authenticated)"],"input_types":["HuggingFace model identifier (e.g., 'BitNet/BitNet-b1.58-3B')","safetensors checkpoint files","Model configuration JSON"],"output_types":["GGUF format binary file","Quantization metadata (JSON)","Conversion log with statistics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bitnet-cpp__cap_3","uri":"capability://text.generation.language.interactive.cli.inference.with.streaming.token.generation","name":"interactive cli inference with streaming token generation","description":"Provides run_inference.py script that enables single-prompt or multi-turn conversation mode inference through command-line interface with streaming token output. The implementation wraps the compiled C++ inference engine, handles prompt tokenization, manages conversation context across turns, and streams tokens to stdout in real-time, enabling interactive debugging and user-facing chatbot applications without server overhead.","intents":["Test model quality and behavior interactively before deploying to production","Run conversational AI locally without external API dependencies","Benchmark token generation speed and latency in realistic interactive scenarios"],"best_for":["Developers prototyping LLM applications locally","Researchers evaluating model outputs interactively","Users running LLMs on personal devices without server infrastructure"],"limitations":["Single-threaded inference; cannot handle concurrent requests","Conversation context stored in memory only; no persistence across sessions","No built-in rate limiting, authentication, or multi-user isolation"],"requires":["Compiled BitNet.cpp binary with inference engine","Quantized GGUF model file","Python 3.8+ with ctypes for C++ library binding","Terminal with UTF-8 support for token streaming"],"input_types":["Text prompts (single or multi-turn)","Optional configuration parameters (temperature, top-p, max tokens)"],"output_types":["Streamed text tokens to stdout","Inference statistics (tokens/second, latency)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bitnet-cpp__cap_4","uri":"capability://tool.use.integration.http.server.deployment.with.restful.inference.api","name":"http server deployment with restful inference api","description":"Implements run_inference_server.py that wraps the C++ inference engine as an HTTP server exposing RESTful endpoints for prompt submission and token generation. The server handles request parsing, manages inference queue (single-threaded), streams responses via chunked transfer encoding, and provides JSON-formatted output compatible with OpenAI API conventions, enabling drop-in replacement for cloud LLM APIs.","intents":["Deploy BitNet models as production inference service accessible over network","Replace cloud LLM API calls with local inference for cost reduction and latency improvement","Integrate BitNet inference into existing applications using standard HTTP clients"],"best_for":["Teams deploying LLMs on-premise or in private cloud","Applications requiring sub-100ms latency that cloud APIs cannot provide","Cost-sensitive deployments where per-token pricing is prohibitive"],"limitations":["Single-threaded inference queue; concurrent requests are serialized (no parallelism)","No built-in load balancing, clustering, or horizontal scaling","No authentication, rate limiting, or request validation beyond basic JSON parsing","Streaming responses require client support for chunked transfer encoding"],"requires":["Python 3.8+ with http.server or Flask/FastAPI framework","Compiled BitNet.cpp inference binary","Quantized GGUF model file","Network access to server port (default 8000 or configurable)"],"input_types":["JSON POST request with 'prompt' field","Optional parameters: temperature, top_p, max_tokens, stream (boolean)"],"output_types":["JSON response with 'text' field (non-streaming)","Server-sent events (SSE) or chunked JSON (streaming mode)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bitnet-cpp__cap_5","uri":"capability://data.processing.analysis.end.to.end.performance.benchmarking.with.throughput.and.latency.measurement","name":"end-to-end performance benchmarking with throughput and latency measurement","description":"Provides e2e_benchmark.py script that measures inference performance across multiple dimensions: token generation throughput (tokens/second), latency (time-to-first-token, inter-token latency), energy consumption, and memory usage. The benchmarking pipeline runs standardized prompt sets, aggregates statistics across multiple runs, and outputs detailed performance reports comparing different quantization schemes and hardware configurations.","intents":["Quantify performance improvements from 1-bit quantization vs baseline models","Compare inference speed across different CPU architectures and quantization kernels","Validate that quantization meets production latency/throughput requirements before deployment"],"best_for":["Performance engineers optimizing inference pipelines","Hardware vendors validating CPU performance for LLM inference","Researchers publishing benchmarks comparing quantization methods"],"limitations":["Benchmarks are single-threaded; does not measure concurrent request performance","Energy measurement requires hardware support (RAPL on x86, not available on all ARM systems)","Results are hardware-specific; benchmarks on one CPU do not predict performance on different architecture"],"requires":["Compiled BitNet.cpp binary","Quantized GGUF model file","Python 3.8+ with psutil for system metrics","Optional: RAPL access for energy measurement (Linux with perf tools)"],"input_types":["Model file path","Benchmark configuration (prompt count, sequence length, number of runs)","Optional: custom prompt dataset"],"output_types":["CSV or JSON report with throughput, latency, energy metrics","Aggregated statistics (mean, std dev, percentiles)","Comparison tables across quantization schemes"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bitnet-cpp__cap_6","uri":"capability://automation.workflow.configurable.kernel.parameters.and.performance.tuning.presets","name":"configurable kernel parameters and performance tuning presets","description":"Exposes kernel configuration parameters (block size, unrolling factors, cache line optimization) and provides preset configurations optimized for different hardware profiles (mobile ARM, server x86, edge devices). The tuning system allows developers to trade off memory bandwidth, cache efficiency, and computation density by adjusting kernel parameters, with presets providing sensible defaults for common deployment scenarios without requiring deep microarchitecture knowledge.","intents":["Optimize inference speed for specific CPU models by tuning kernel parameters to hardware microarchitecture","Balance memory bandwidth and compute utilization for different workloads (latency-sensitive vs throughput-optimized)","Experiment with kernel configurations to find Pareto-optimal trade-offs between speed and energy"],"best_for":["Performance engineers fine-tuning inference for specific hardware","Hardware vendors optimizing kernel implementations for their CPUs","Researchers exploring quantization kernel design space"],"limitations":["Kernel parameter tuning requires understanding CPU microarchitecture (cache sizes, memory bandwidth, instruction latency)","Changes to kernel parameters require recompilation; no runtime parameter adjustment","Presets are heuristic-based; optimal parameters vary by specific CPU model and workload"],"requires":["C++ compiler with optimization flags (-O3, -march=native)","CMake 3.15+ for build configuration","Knowledge of target CPU microarchitecture for manual tuning","Benchmarking infrastructure to validate parameter changes"],"input_types":["Kernel configuration file (JSON or CMake variables)","Preset name (e.g., 'mobile-arm', 'server-x86')"],"output_types":["Compiled binary with tuned kernel parameters","Performance metrics showing impact of tuning"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bitnet-cpp__cap_7","uri":"capability://automation.workflow.experimental.gpu.inference.with.cuda.w2a8.kernels","name":"experimental gpu inference with cuda w2a8 kernels","description":"Provides experimental CUDA-based kernels for GPU inference using W2A8 quantization (2-bit weights, 8-bit activations), extending CPU-only inference to NVIDIA GPUs. The implementation compiles CUDA kernels that perform quantized matrix multiplications on GPU, with automatic device detection and fallback to CPU if CUDA is unavailable, enabling GPU acceleration for deployments with NVIDIA hardware.","intents":["Accelerate inference on NVIDIA GPUs for higher throughput than CPU-only deployment","Evaluate GPU performance for 1-bit quantization models before production rollout","Provide fallback GPU path for deployments with mixed CPU/GPU hardware"],"best_for":["Teams with NVIDIA GPU infrastructure seeking inference acceleration","Researchers evaluating GPU efficiency for quantized LLM inference","Deployments requiring both CPU and GPU inference paths"],"limitations":["Experimental status; lacks production maturity and comprehensive optimization","W2A8 quantization differs from CPU 1-bit approach; requires separate model quantization","Limited to NVIDIA GPUs with CUDA compute capability 7.0+; no AMD or Intel GPU support","No multi-GPU support; single GPU inference only","CUDA kernel performance not yet competitive with optimized CPU kernels on high-end CPUs"],"requires":["NVIDIA GPU with CUDA compute capability 7.0+ (Volta or newer)","CUDA Toolkit 11.0+ and cuDNN 8.0+","NVIDIA driver compatible with CUDA version","C++ compiler with CUDA support (nvcc)"],"input_types":["Quantized GGUF model (W2A8 format)","CUDA device ID (automatic detection if single GPU)"],"output_types":["Token sequences (inference output)","GPU memory usage metrics","Throughput measurements"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bitnet-cpp__cap_8","uri":"capability://automation.workflow.automated.environment.setup.and.model.preparation.orchestration","name":"automated environment setup and model preparation orchestration","description":"Provides setup_env.py script that orchestrates complete model preparation workflow: downloads BitNet models from HuggingFace, generates architecture-specific kernels, builds C++ binaries, applies quantization, and validates setup. The orchestration script handles dependency installation, environment configuration, and end-to-end validation, reducing manual setup steps from dozens to single command execution.","intents":["Get BitNet inference running in minimal time without manual configuration","Automate model preparation for CI/CD pipelines and containerized deployments","Validate that all components (kernels, binaries, models) are correctly installed before inference"],"best_for":["Developers new to BitNet seeking quick start without deep technical knowledge","DevOps engineers automating model deployment in containers or VMs","Teams building reproducible inference pipelines"],"limitations":["Setup script assumes standard Linux/macOS environment; Windows support is limited","Kernel generation adds 5-10 minutes to setup time; not suitable for rapid iteration","Script downloads full models from HuggingFace; requires sufficient disk space and network bandwidth","Validation is basic; does not catch all configuration errors"],"requires":["Python 3.8+","C++ compiler (GCC 9+ or Clang 10+)","CMake 3.15+","Git for cloning repository","Internet access to download models and dependencies","~50GB free disk space for models and build artifacts"],"input_types":["Model name (e.g., 'BitNet/BitNet-b1.58-3B')","Optional: custom configuration parameters"],"output_types":["Compiled C++ binaries","Generated architecture-specific kernels","Quantized GGUF model files","Setup validation report"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-bitnet-cpp__cap_9","uri":"capability://tool.use.integration.llama.cpp.integration.and.gguf.format.compatibility","name":"llama.cpp integration and gguf format compatibility","description":"Extends llama.cpp's mature inference infrastructure by implementing BitNet-specific quantization schemes while maintaining GGUF format compatibility. The integration reuses llama.cpp's tokenization, context management, and sampling logic, adding specialized 1-bit quantization kernels as pluggable components. This approach leverages llama.cpp's production-tested infrastructure while isolating BitNet-specific code to quantization layer.","intents":["Leverage llama.cpp's mature codebase and community for production stability","Maintain compatibility with GGUF ecosystem tools and model formats","Reuse llama.cpp's tokenization and sampling without reimplementation"],"best_for":["Teams already using llama.cpp seeking 1-bit quantization support","Developers wanting to extend BitNet with llama.cpp features (multi-GPU, batching)","Projects requiring GGUF format compatibility for tool ecosystem"],"limitations":["Tightly coupled to llama.cpp version; updates to llama.cpp may require BitNet changes","Cannot use llama.cpp's multi-GPU or batching features with 1-bit kernels (single-threaded only)","GGUF format adds metadata overhead; not optimal for minimal model size"],"requires":["llama.cpp source code (included in BitNet repository)","C++ compiler compatible with llama.cpp build requirements","CMake 3.15+ for build system"],"input_types":["GGUF format model files","llama.cpp-compatible configuration"],"output_types":["Token sequences (inference output)","Compatible with llama.cpp tools and ecosystem"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["BitNet b1.58 model in HuggingFace format or safetensors","Python 3.8+ for model conversion pipeline","C++ compiler with AVX2 (x86) or NEON (ARM) support for kernel compilation","C++ compiler with target architecture support (GCC 9+ or Clang 10+)","CMake 3.15+ for build system","Target CPU with NEON (ARM) or AVX2 (x86) instruction set","C++ compiler with template support for abstraction layer","Separate kernel implementation for each quantization scheme","Architecture detection at build time","Python 3.8+"],"failure_modes":["Limited to BitNet b1.58 and compatible 1-bit/1.58-bit models; cannot quantize arbitrary LLMs","LUT-based approach requires model-specific kernel generation; not plug-and-play with standard GGUF models","Experimental GPU support (W2A8 CUDA kernels) lacks production maturity and optimization","Kernel generation adds ~5-10 minutes to first-run setup; not suitable for real-time model loading","Limited to ARM64 (NEON) and x86_64 (AVX2); no support for older CPUs or other ISAs (RISC-V, PowerPC)","Custom kernel configuration requires understanding of quantization schemes and CPU microarchitecture","Abstraction adds small overhead (~1-2%) for scheme selection and dispatch","Each new scheme requires separate kernel implementation; no code sharing between schemes","Scheme selection is static at compile time; cannot switch schemes at runtime","Only supports BitNet b1.58 and compatible 1-bit models; cannot convert arbitrary LLMs","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:20.516Z","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=bitnet-cpp","compare_url":"https://unfragile.ai/compare?artifact=bitnet-cpp"}},"signature":"9HjXj2AL66HjC2SqpAO4ekO+ECevISk8Ryv6FfcfIbNvJXaKwg4fGoC89IhsU8tAsXO0HFnE1QOBehuoqURKCg==","signedAt":"2026-06-21T02:11:43.811Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/bitnet-cpp","artifact":"https://unfragile.ai/bitnet-cpp","verify":"https://unfragile.ai/api/v1/verify?slug=bitnet-cpp","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"}}