{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-llama-cpp","slug":"llama-cpp","name":"llama.cpp","type":"repo","url":"https://github.com/ggml-org/llama.cpp","page_url":"https://unfragile.ai/llama-cpp","categories":["model-training"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-llama-cpp__cap_0","uri":"capability://text.generation.language.cpu.optimized.llm.inference.with.quantization.support","name":"cpu-optimized llm inference with quantization support","description":"Executes large language models entirely on CPU using GGML (Ggerganov's Machine Learning library), a tensor computation framework optimized for inference. Implements multiple quantization schemes (Q4_0, Q4_1, Q5_0, Q8_0, etc.) that reduce model size by 75-90% while maintaining inference quality through mixed-precision arithmetic and custom SIMD kernels for x86/ARM architectures. Supports batch processing and streaming token generation without GPU dependencies.","intents":["Run LLaMA-scale models locally on consumer hardware without NVIDIA/AMD GPUs","Deploy inference in resource-constrained environments (edge devices, servers without accelerators)","Reduce inference latency by eliminating cloud API calls and network overhead","Quantize and optimize proprietary or fine-tuned models for local deployment"],"best_for":["Solo developers building privacy-first LLM applications","Teams deploying models in air-gapped or bandwidth-limited environments","Researchers benchmarking quantization trade-offs","DevOps engineers optimizing inference cost per token"],"limitations":["Inference speed 5-10x slower than GPU-accelerated inference (e.g., vLLM on A100)","Quantization introduces 1-3% accuracy degradation depending on bit-width and model architecture","No distributed inference across multiple CPUs — single-machine only","Limited to models that fit in RAM; no disk-based paging for larger models","Batch size typically capped at 1-4 on consumer CPUs due to memory bandwidth constraints"],"requires":["C++17 compiler (GCC 7+, Clang 5+, MSVC 2019+)","4GB+ RAM for 7B parameter models, 16GB+ for 13B models","x86-64 or ARM64 CPU with SSE2/AVX2 or NEON support for optimal performance","GGUF format model files (converted from HuggingFace or other sources)"],"input_types":["GGUF quantized model files","Plain text prompts","Structured JSON for multi-turn conversations"],"output_types":["Text tokens (streamed or batched)","Embeddings (if model supports)","Structured JSON responses (with grammar constraints)"],"categories":["text-generation-language","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_1","uri":"capability://data.processing.analysis.multi.format.model.quantization.and.conversion.pipeline","name":"multi-format model quantization and conversion pipeline","description":"Converts models from HuggingFace, SafeTensors, and other formats into GGUF (Ggerganov Universal Format) with configurable quantization schemes. The pipeline uses a modular converter architecture that parses model architectures (LLaMA, Mistral, Phi, etc.), maps tensor names to quantization strategies, and applies per-layer or per-tensor quantization with optional calibration data. Supports both symmetric and asymmetric quantization with configurable bit-widths and mixed-precision strategies (e.g., keeping attention layers at higher precision).","intents":["Convert HuggingFace models to GGUF format for llama.cpp compatibility","Reduce model size from 26GB (fp32) to 3-4GB (Q4) for local deployment","Experiment with different quantization levels to balance speed vs accuracy","Preserve model behavior during quantization through calibration on representative data"],"best_for":["ML engineers optimizing models for production deployment","Researchers studying quantization impact on model performance","Teams building model distribution pipelines with size constraints"],"limitations":["Conversion process requires loading full model into memory (26GB+ for 13B fp32 models)","No automated calibration dataset selection — requires manual specification or uses random data","Quantization is one-way; cannot recover original precision from GGUF files","Some custom model architectures not yet supported (requires manual converter implementation)","Conversion speed ~5-15 minutes for 13B models on consumer hardware"],"requires":["Python 3.8+","PyTorch or SafeTensors library for model loading","64GB+ RAM for converting 13B+ models","Source model in HuggingFace, SafeTensors, or PyTorch format"],"input_types":["HuggingFace model directories","SafeTensors files","PyTorch .pt/.pth checkpoints","GGML format files (for re-quantization)"],"output_types":["GGUF quantized model files","Quantization metadata (bit-widths, layer-wise strategies)","Conversion logs with tensor mapping details"],"categories":["data-processing-analysis","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_10","uri":"capability://data.processing.analysis.model.quantization.analysis.and.benchmarking","name":"model quantization analysis and benchmarking","description":"Provides tools to measure and compare quantization impact on model performance, including perplexity evaluation on benchmark datasets, inference speed benchmarking across quantization levels, and memory usage profiling. Generates detailed reports showing trade-offs between model size, inference speed, and output quality for different quantization schemes (Q4, Q5, Q8, etc.), enabling data-driven selection of quantization parameters.","intents":["Choose optimal quantization level for specific hardware and quality requirements","Measure quantization impact on model accuracy before production deployment","Benchmark inference speed across different quantization schemes","Document quantization trade-offs for stakeholder communication"],"best_for":["ML engineers optimizing models for production","Teams evaluating quantization strategies","Researchers studying quantization impact on model behavior"],"limitations":["Benchmarking requires running inference on full evaluation datasets (can take hours)","Perplexity is a proxy metric — doesn't directly measure downstream task performance","Benchmarks are hardware-specific — results don't transfer across different CPUs/GPUs","No automated recommendation system — requires manual interpretation of trade-offs","Evaluation datasets may not represent production use cases"],"requires":["Multiple quantized versions of the same model","Benchmark dataset (e.g., WikiText, C4)","Evaluation infrastructure (compute for benchmarking)"],"input_types":["Quantized model files (multiple versions)","Benchmark dataset","Evaluation configuration (metrics, batch size)"],"output_types":["Perplexity scores per quantization level","Inference speed benchmarks (tokens/sec)","Memory usage profiles","Trade-off analysis reports (CSV, JSON)"],"categories":["data-processing-analysis","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_11","uri":"capability://code.generation.editing.fine.tuning.support.with.lora.and.qlora.adapters","name":"fine-tuning support with lora and qlora adapters","description":"Enables parameter-efficient fine-tuning using Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA), which add small trainable adapter layers instead of updating all model weights. Supports training on consumer hardware by keeping base model weights frozen and quantized while only updating low-rank adapter matrices. Integrates with standard training frameworks (PyTorch, HuggingFace Transformers) and supports saving/loading adapters independently of base model.","intents":["Fine-tune large models on consumer GPUs without full model training","Adapt pre-trained models to domain-specific tasks with minimal data","Reduce training memory requirements from 80GB (full fine-tuning) to 8GB (QLoRA)","Maintain multiple task-specific adapters for a single base model"],"best_for":["Developers adapting models to specific domains or tasks","Teams with limited GPU resources (consumer GPUs, not enterprise)","Researchers studying parameter-efficient fine-tuning"],"limitations":["LoRA adapters add inference latency (5-10% slower than base model) due to adapter computation","Fine-tuning quality depends heavily on adapter rank and learning rate — requires careful tuning","Adapter composition (combining multiple adapters) is not well-supported","No built-in evaluation framework — requires external metrics","Adapters are model-specific — cannot transfer between different base models"],"requires":["PyTorch 1.13+","HuggingFace Transformers library","Training dataset (typically 1K-100K examples)","GPU with 8GB+ VRAM (for QLoRA) or 24GB+ (for LoRA)"],"input_types":["Base model (GGUF or HuggingFace format)","Training dataset (JSON, CSV, or HuggingFace Dataset)","LoRA configuration (rank, alpha, target modules)"],"output_types":["LoRA adapter weights (safetensors format)","Training logs (loss, validation metrics)","Merged model (base + adapter combined)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_12","uri":"capability://planning.reasoning.token.probability.and.logit.inspection.for.interpretability","name":"token probability and logit inspection for interpretability","description":"Exposes token probabilities and raw logits at each generation step, enabling analysis of model confidence, alternative token predictions, and attention patterns. Provides APIs to inspect top-k alternative tokens with their probabilities, allowing developers to understand why the model made specific choices and detect low-confidence generations. Supports exporting attention weights and hidden states for deeper model analysis.","intents":["Debug model behavior by inspecting token probabilities and alternatives","Detect low-confidence generations that may indicate hallucinations","Analyze model reasoning through attention patterns and hidden states","Build uncertainty quantification into applications (e.g., confidence scores)"],"best_for":["Researchers studying model behavior and interpretability","Developers building safety-critical applications requiring confidence scores","Teams debugging model failures and unexpected outputs"],"limitations":["Inspecting logits adds 10-20% computational overhead per inference","Attention weights are model-specific and difficult to interpret without domain knowledge","No automated interpretation — requires manual analysis of probabilities and patterns","Exporting hidden states requires significant memory (proportional to sequence length × hidden size)","Interpretability tools don't explain why model assigned specific probabilities (black-box)"],"requires":["Inference context with logit/probability export enabled","Analysis tools for interpreting probabilities (custom code or visualization libraries)"],"input_types":["Prompts for analysis","Configuration for which layers/tokens to inspect"],"output_types":["Token probabilities (per generation step)","Top-k alternative tokens with scores","Attention weights (optional)","Hidden state vectors (optional)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_2","uri":"capability://text.generation.language.interactive.cli.chat.interface.with.streaming.output","name":"interactive cli chat interface with streaming output","description":"Provides a command-line REPL for multi-turn conversations with streaming token generation, supporting both single-shot inference and interactive chat modes. Implements line-buffered input handling, real-time token streaming to stdout, and conversation history management in memory. Supports prompt templates (Alpaca, ChatML, etc.) for automatic formatting of user/assistant roles, and allows custom system prompts and sampling parameters (temperature, top-p, top-k) to be configured via CLI flags or interactive commands.","intents":["Test model behavior interactively without writing code","Stream responses in real-time for better UX in terminal environments","Prototype chatbot behavior before integrating into applications","Debug model outputs with configurable sampling strategies"],"best_for":["Researchers and developers prototyping model behavior","Non-technical users testing models via command-line","DevOps engineers validating model deployments"],"limitations":["No persistent conversation history — resets on process exit unless manually saved","Single-threaded interaction — cannot handle concurrent requests","Limited to terminal output — no rich formatting or multimedia support","No built-in logging or audit trail for conversations","Sampling parameters must be set before inference starts; cannot adjust mid-generation"],"requires":["Terminal with UTF-8 support","GGUF model file loaded in memory","2GB+ RAM for 7B models in interactive mode"],"input_types":["Plain text user prompts","CLI flags for configuration (--temp, --top-p, --prompt-template)","System prompt text"],"output_types":["Streamed text tokens to stdout","Conversation history (in-memory)","Sampling statistics (tokens/sec, total tokens)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_3","uri":"capability://text.generation.language.grammar.constrained.generation.with.ebnf.support","name":"grammar-constrained generation with ebnf support","description":"Enforces structured output by constraining token generation to match user-defined EBNF grammars, preventing invalid JSON, code, or domain-specific formats. The implementation compiles EBNF rules into a finite-state automaton that filters the logit distribution at each generation step, allowing only tokens that keep the output on a valid path. Supports common grammars (JSON, SQL, regex) with pre-built templates and allows custom grammar definition for domain-specific languages.","intents":["Generate valid JSON without post-processing or validation","Ensure SQL queries are syntactically correct before execution","Extract structured data in specific formats (CSV, YAML, etc.)","Prevent model hallucinations in code generation by enforcing syntax rules"],"best_for":["Developers building LLM-powered data extraction pipelines","Teams using LLMs for code generation with strict syntax requirements","Applications requiring guaranteed structured output without fallback parsing"],"limitations":["Grammar compilation adds 50-200ms overhead per inference call","Complex grammars (>1000 rules) can cause logit filtering to become a bottleneck","No support for context-sensitive grammars — only context-free EBNF","Grammar violations cause generation to halt rather than gracefully degrade","Requires careful grammar design; overly restrictive grammars reduce output diversity"],"requires":["EBNF grammar definition (text format)","Model with sufficient vocabulary overlap with target format","Inference context with grammar support enabled"],"input_types":["EBNF grammar rules (text)","User prompts (plain text)","Pre-built grammar templates (JSON, SQL, etc.)"],"output_types":["Text conforming to specified grammar","Structured data (JSON, CSV, code, etc.)","Generation metadata (tokens used, grammar violations prevented)"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_4","uri":"capability://data.processing.analysis.embedding.generation.with.vector.output","name":"embedding generation with vector output","description":"Extracts dense vector embeddings from text by running the model in embedding mode, extracting the final hidden state or pooled representation and normalizing to unit vectors. Supports batch embedding of multiple texts with configurable pooling strategies (mean, max, CLS token). Outputs embeddings in raw float32 format compatible with vector databases (Pinecone, Weaviate, Milvus) and similarity search libraries.","intents":["Generate embeddings for semantic search without external embedding APIs","Build vector indices for RAG systems using local models","Compute text similarity for clustering or deduplication","Avoid API costs and latency of cloud embedding services"],"best_for":["Teams building RAG systems with privacy requirements","Developers optimizing embedding inference cost","Researchers comparing embedding quality across models"],"limitations":["Embedding quality varies significantly by model — not all LLMs produce good embeddings","No built-in vector database integration — requires manual indexing","Batch embedding still limited by CPU memory (typically 32-128 texts per batch)","Embeddings are model-specific; switching models requires re-embedding entire corpus","No fine-tuning support — embeddings are frozen from pre-trained weights"],"requires":["Model with embedding support (most LLaMA variants support this)","Text input (single or batch)","Vector database or similarity library for downstream use"],"input_types":["Plain text strings","Batch text files (one per line)","Structured documents (with optional preprocessing)"],"output_types":["Float32 vectors (384-4096 dimensions depending on model)","Normalized unit vectors (L2 norm = 1)","Batch embedding matrices (N x D)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_5","uri":"capability://automation.workflow.multi.gpu.and.distributed.inference.coordination","name":"multi-gpu and distributed inference coordination","description":"Distributes model inference across multiple GPUs (CUDA, Metal, ROCm) or CPU cores using layer-wise model splitting and tensor parallelism. Automatically partitions model layers across available devices, manages inter-device communication, and coordinates token generation across distributed workers. Supports both data parallelism (batch splitting) and model parallelism (layer splitting) with configurable strategies based on available hardware.","intents":["Run 70B+ parameter models on consumer multi-GPU setups","Maximize throughput by distributing batch inference across GPUs","Reduce latency for large models by splitting layers across devices","Optimize GPU memory utilization by balancing layer distribution"],"best_for":["Teams deploying large models (30B+) in production","Researchers benchmarking distributed inference strategies","DevOps engineers optimizing GPU cluster utilization"],"limitations":["Inter-GPU communication overhead (PCIe/NVLink) can negate parallelism benefits for small batches","Requires careful tuning of layer distribution — suboptimal splits reduce throughput","No automatic load balancing — uneven layer distribution causes GPU underutilization","Limited to models that fit across total GPU VRAM (e.g., 2x 24GB GPUs = 48GB max)","Distributed inference adds 5-15% latency overhead vs single-GPU due to synchronization"],"requires":["Multiple GPUs (NVIDIA CUDA 11.8+, AMD ROCm 5.0+, or Apple Metal)","Model file size <= total GPU VRAM","NCCL or similar collective communication library for GPU coordination"],"input_types":["GGUF model files","Batch prompts (text)","Layer distribution configuration (manual or auto-tuned)"],"output_types":["Text tokens (streamed or batched)","Inference metrics (throughput, latency per GPU)","Layer distribution statistics"],"categories":["automation-workflow","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_6","uri":"capability://tool.use.integration.server.mode.with.http.api.and.openai.compatible.endpoints","name":"server mode with http api and openai-compatible endpoints","description":"Runs llama.cpp as a background server exposing a REST API compatible with OpenAI's Chat Completions and Embeddings endpoints, allowing drop-in replacement of cloud APIs in existing applications. Implements request queuing, concurrent request handling with configurable worker threads, and streaming responses via Server-Sent Events (SSE). Supports authentication via API keys and request rate limiting.","intents":["Replace OpenAI API calls with local inference without code changes","Build LLM applications that work offline or in air-gapped environments","Reduce API costs by running inference locally while maintaining API compatibility","Integrate llama.cpp with existing tools expecting OpenAI-compatible APIs (LangChain, LlamaIndex, etc.)"],"best_for":["Developers migrating from cloud APIs to local inference","Teams building privacy-sensitive applications","DevOps engineers deploying LLM inference in restricted networks"],"limitations":["Single-machine bottleneck — no horizontal scaling across servers","Request queuing can cause 100ms-1s latency spikes under load","No built-in load balancing or failover — single point of failure","Concurrent requests compete for CPU/GPU resources; throughput degrades with queue depth","API compatibility is partial — some OpenAI-specific features (function calling, vision) not fully supported"],"requires":["HTTP server capability (built-in, no external dependencies)","Port availability (default 8000)","Client library supporting OpenAI API (e.g., openai-python, LangChain)"],"input_types":["HTTP POST requests (JSON)","OpenAI Chat Completions format","Embedding requests (text)"],"output_types":["JSON responses (Chat Completions format)","Server-Sent Events (SSE) for streaming","Embedding vectors (float32 arrays)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_7","uri":"capability://text.generation.language.custom.sampling.strategies.with.temperature.top.p.and.top.k.control","name":"custom sampling strategies with temperature, top-p, and top-k control","description":"Implements multiple sampling algorithms (greedy, temperature-scaled softmax, nucleus/top-p, top-k, min-p) that modify the probability distribution over next tokens before sampling. Allows fine-grained control over generation diversity vs determinism through configurable parameters, and supports dynamic sampling (changing parameters mid-generation). Includes advanced strategies like repetition penalty, frequency penalty, and presence penalty to reduce hallucinations and repetitive output.","intents":["Control output diversity for different use cases (deterministic code generation vs creative writing)","Reduce repetitive or hallucinated text through penalty mechanisms","Experiment with sampling strategies to optimize output quality","Implement domain-specific sampling (e.g., lower temperature for factual tasks)"],"best_for":["Developers fine-tuning model behavior for specific applications","Researchers studying sampling impact on output quality","Teams optimizing inference for different use cases"],"limitations":["Sampling is non-deterministic — same prompt produces different outputs (unless temperature=0)","No principled way to select optimal parameters — requires empirical tuning","Penalties are heuristic-based and may not generalize across models or domains","Dynamic sampling (changing parameters mid-generation) can cause output quality degradation","Extreme parameter values (very low temperature, very high top-p) can cause generation to fail or produce nonsense"],"requires":["Inference context with sampling support","Understanding of sampling algorithms and their effects","Evaluation metrics to measure output quality"],"input_types":["Sampling parameters (temperature, top-p, top-k, etc.)","Penalty weights (repetition, frequency, presence)","Prompt text"],"output_types":["Sampled text tokens","Probability distributions (for analysis)","Sampling statistics (entropy, effective vocabulary size)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_8","uri":"capability://memory.knowledge.context.window.management.with.sliding.window.attention","name":"context window management with sliding window attention","description":"Manages model context efficiently using sliding window attention, which limits attention computation to a fixed window of recent tokens rather than all previous tokens. This reduces memory usage from O(n²) to O(n*w) where w is window size, enabling longer context windows on limited hardware. Implements KV cache management with automatic eviction policies and supports context compression techniques (e.g., summarization of old context).","intents":["Process documents longer than model's native context window (e.g., 8K window for 32K documents)","Reduce memory usage for long-context inference on consumer hardware","Maintain conversation history without hitting context limits","Implement efficient retrieval-augmented generation with large document sets"],"best_for":["Developers building document analysis tools","Teams implementing RAG systems with large document collections","Researchers studying long-context model behavior"],"limitations":["Sliding window attention loses information from tokens outside the window — may miss important context","Context compression (summarization) introduces additional latency and potential information loss","KV cache eviction policies are heuristic-based — no guarantee of optimal context retention","Longer context windows increase inference latency (linear with window size)","Not all models support sliding window attention — requires specific architecture support"],"requires":["Model with sliding window attention support (e.g., Mistral, Phi)","Sufficient RAM for KV cache (typically 2-4GB per 4K context window)","Document or conversation input"],"input_types":["Long text documents (>model context window)","Multi-turn conversations","Structured documents with metadata"],"output_types":["Text responses","Context window statistics (tokens used, evicted)","Compression metadata (if summarization used)"],"categories":["memory-knowledge","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-llama-cpp__cap_9","uri":"capability://automation.workflow.batch.inference.with.dynamic.batching.and.request.scheduling","name":"batch inference with dynamic batching and request scheduling","description":"Processes multiple inference requests concurrently by batching them together, reducing per-request overhead and improving GPU/CPU utilization. Implements dynamic batching where requests are grouped based on arrival time and context length, with configurable batch size and scheduling policies (FCFS, priority-based). Supports variable-length sequences within batches through padding and masking, and automatically schedules new requests into running batches when possible.","intents":["Maximize throughput by processing multiple requests in parallel","Reduce latency for concurrent requests through efficient batching","Optimize resource utilization (GPU/CPU) for production inference","Handle variable request sizes without wasting compute on padding"],"best_for":["Teams running production inference servers with multiple concurrent users","Developers optimizing inference throughput for batch processing","DevOps engineers maximizing GPU utilization in shared infrastructure"],"limitations":["Batching adds latency for individual requests (wait time in queue before batch fills)","Variable-length sequences require padding, which wastes computation on padding tokens","Batch scheduling is heuristic-based — suboptimal batching reduces throughput gains","Memory overhead increases with batch size (KV cache scales with batch dimension)","No built-in request prioritization — all requests treated equally regardless of importance"],"requires":["Multiple concurrent inference requests","Configurable batch size (typically 4-32 depending on model and hardware)","Sufficient memory for largest batch (KV cache + activations)"],"input_types":["Multiple prompts (variable length)","Batch configuration (size, scheduling policy)","Request metadata (priority, deadline)"],"output_types":["Batched text responses","Throughput metrics (tokens/sec, requests/sec)","Batch statistics (actual batch sizes, queue depth)"],"categories":["automation-workflow","inference-optimization"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["C++17 compiler (GCC 7+, Clang 5+, MSVC 2019+)","4GB+ RAM for 7B parameter models, 16GB+ for 13B models","x86-64 or ARM64 CPU with SSE2/AVX2 or NEON support for optimal performance","GGUF format model files (converted from HuggingFace or other sources)","Python 3.8+","PyTorch or SafeTensors library for model loading","64GB+ RAM for converting 13B+ models","Source model in HuggingFace, SafeTensors, or PyTorch format","Multiple quantized versions of the same model","Benchmark dataset (e.g., WikiText, C4)"],"failure_modes":["Inference speed 5-10x slower than GPU-accelerated inference (e.g., vLLM on A100)","Quantization introduces 1-3% accuracy degradation depending on bit-width and model architecture","No distributed inference across multiple CPUs — single-machine only","Limited to models that fit in RAM; no disk-based paging for larger models","Batch size typically capped at 1-4 on consumer CPUs due to memory bandwidth constraints","Conversion process requires loading full model into memory (26GB+ for 13B fp32 models)","No automated calibration dataset selection — requires manual specification or uses random data","Quantization is one-way; cannot recover original precision from GGUF files","Some custom model architectures not yet supported (requires manual converter implementation)","Conversion speed ~5-15 minutes for 13B models on consumer hardware","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"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":"2026-06-17T09:51:03.577Z","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=llama-cpp","compare_url":"https://unfragile.ai/compare?artifact=llama-cpp"}},"signature":"K1C1LUvpqOI6BmXB+/Vkjyshc0IG3C04TkVmQSABC+pcGnqcWTAmZ1Z2xXQUuozZUp97td3ia+E2LzUNiHTiAg==","signedAt":"2026-06-21T11:15:51.159Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llama-cpp","artifact":"https://unfragile.ai/llama-cpp","verify":"https://unfragile.ai/api/v1/verify?slug=llama-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"}}