{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-ctransformers","slug":"pypi-ctransformers","name":"ctransformers","type":"repo","url":"https://github.com/marella/ctransformers","page_url":"https://unfragile.ai/pypi-ctransformers","categories":["frameworks-sdks"],"tags":["ctransformers","transformers","ai","llm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-ctransformers__cap_0","uri":"capability://text.generation.language.ggml.accelerated.causal.language.model.inference.with.hardware.aware.optimization.selection","name":"ggml-accelerated causal language model inference with hardware-aware optimization selection","description":"Executes transformer-based causal language models (GPT-2, LLaMA, Falcon, etc.) using C/C++ implementations compiled against GGML, with automatic runtime detection of CPU instruction sets (AVX/AVX2) and GPU capabilities (CUDA, Metal) to select the optimal compiled library variant without requiring user configuration. The Python layer wraps ctypes bindings to the native implementation, delegating all tensor operations and forward passes to the optimized C/C++ backend while maintaining a unified Python API across hardware configurations.","intents":["Run large language models efficiently on CPU-only machines without quantization loss","Automatically leverage GPU acceleration (CUDA/Metal) when available without code changes","Reduce memory footprint and latency compared to PyTorch-based inference on consumer hardware","Deploy LLMs locally with minimal dependencies and no cloud API calls"],"best_for":["Solo developers building local LLM agents with limited compute budgets","Teams deploying inference on heterogeneous hardware (laptops, edge devices, servers)","Builders prioritizing inference speed and memory efficiency over training flexibility"],"limitations":["Inference-only — no fine-tuning or training capabilities; model weights must be pre-trained","Limited to GGML-compatible model architectures (GPT-2, LLaMA, Falcon, MPT, StarCoder); newer architectures require upstream GGML support","GPU acceleration limited to CUDA (NVIDIA) and Metal (Apple Silicon); no ROCm support for AMD GPUs","Context length parameter only supported for LLaMA, MPT, and Falcon models; other architectures use fixed context windows","No built-in distributed inference across multiple machines; single-process execution only"],"requires":["Python 3.8+","Pre-quantized GGML model files (GGUF format) or compatible model weights","For CUDA: NVIDIA GPU with compute capability 3.5+, CUDA toolkit installed","For Metal: macOS 12.0+ with Apple Silicon or Intel GPU","For CPU: x86-64 processor (AVX/AVX2 support recommended for performance)"],"input_types":["text prompts (string)","GGML model files (.gguf, .bin formats)","configuration dictionaries (temperature, top_k, top_p, etc.)"],"output_types":["generated text (string)","token sequences (list of integers)","streaming token iterators (generator objects)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_1","uri":"capability://text.generation.language.streaming.text.generation.with.configurable.sampling.strategies.and.early.stopping","name":"streaming text generation with configurable sampling strategies and early stopping","description":"Generates text token-by-token with support for multiple sampling algorithms (top-k, top-p/nucleus, temperature scaling) and early stopping conditions, exposing a generator interface that yields tokens as they are produced rather than buffering the full output. The native C/C++ implementation maintains internal token history for repetition penalty calculation and applies stop sequences by checking generated tokens against a user-provided list, enabling real-time streaming to clients or interactive applications.","intents":["Stream LLM responses to users in real-time without waiting for full generation","Implement interactive chatbots with immediate token feedback","Control generation diversity and quality via temperature and top-p parameters","Prevent repetitive or unwanted outputs using repetition penalties and stop sequences"],"best_for":["Web application developers building chat interfaces with streaming responses","CLI tool builders requiring interactive text generation feedback","Researchers experimenting with different sampling strategies and hyperparameters"],"limitations":["Streaming adds minimal latency (~1-2ms per token) but requires client-side buffering for full output","Stop sequences are checked only after token generation, not during decoding; may generate partial tokens beyond stop sequence","Repetition penalty calculation uses only last_n_tokens (default 64); longer-range repetition patterns not penalized","No beam search or other advanced decoding algorithms; only greedy and sampling-based generation supported","Temperature and top-p parameters applied uniformly across all tokens; no per-token control"],"requires":["Loaded LLM model via LLM class","Python 3.8+","Generator-aware client code to consume streaming output"],"input_types":["text prompt (string)","generation config dict with keys: temperature (float 0.0-2.0), top_k (int), top_p (float 0.0-1.0), repetition_penalty (float), max_new_tokens (int), stop (list of strings), stream (bool)"],"output_types":["generator yielding individual tokens (str)","full text string if stream=False"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_10","uri":"capability://memory.knowledge.model.state.reset.and.context.management.for.multi.turn.conversations","name":"model state reset and context management for multi-turn conversations","description":"Provides reset parameter to clear model internal state (KV cache, token history) between generations, enabling clean context boundaries for multi-turn conversations or independent prompts. The native implementation maintains KV cache and token history across generations by default (reset=False) to enable efficient context reuse, but setting reset=True clears this state before generation. This allows users to control whether context persists across multiple __call__ invocations, enabling both stateful conversations and stateless independent generations.","intents":["Implement multi-turn conversations where context persists across exchanges","Generate independent responses to different prompts without context leakage","Manage conversation history explicitly without relying on implicit state","Reset context when switching between different conversation threads or users"],"best_for":["Chatbot developers building multi-turn conversation systems","Application developers needing explicit context management","Teams building multi-user systems where context isolation is critical"],"limitations":["No automatic context window management; users must manually reset if context exceeds model's context length","KV cache is not exposed; users cannot inspect or manipulate cached state","No conversation history tracking; users must manually maintain conversation history if needed","reset=True clears all state; no fine-grained state management (e.g., clear only token history but keep KV cache)","No built-in conversation memory or summarization; context grows linearly with conversation length"],"requires":["ctransformers library","reset parameter in Config or LLM.__call__()","Manual conversation history management if needed"],"input_types":["reset (bool): whether to clear model state before generation (default True)"],"output_types":["generated text (string)","implicit state management (KV cache cleared or preserved)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_11","uri":"capability://automation.workflow.deterministic.generation.with.seed.control.for.reproducibility","name":"deterministic generation with seed control for reproducibility","description":"Supports deterministic token generation via seed parameter that initializes the random number generator used for sampling, enabling reproducible outputs across multiple runs. The native C/C++ implementation uses the seed value to initialize GGML's RNG before sampling, ensuring that identical prompts with identical seeds produce identical outputs. Setting seed=-1 (default) uses non-deterministic seeding; explicit seed values (e.g., seed=42) enable reproducibility for testing, debugging, and result verification.","intents":["Generate reproducible outputs for testing and debugging","Verify model behavior across different hardware/software configurations","Create deterministic benchmarks for performance comparison","Enable result verification and auditing in production systems"],"best_for":["Researchers and developers testing model behavior and debugging issues","Teams building deterministic systems where reproducibility is critical","QA engineers verifying model outputs across different environments"],"limitations":["Determinism only applies to sampling; other sources of non-determinism may exist (floating-point rounding, thread scheduling)","Different hardware (CPU vs GPU, different CPU models) may produce different outputs even with same seed due to floating-point precision differences","Seed only controls sampling RNG; does not affect prompt tokenization or other preprocessing","No seed management across multiple generations; users must manually track and set seeds","Deterministic generation may be slower than non-deterministic (some optimizations disabled)"],"requires":["ctransformers library","seed parameter in Config or LLM.__call__()","Explicit seed value (integer)"],"input_types":["seed (int): random seed for sampling (-1 for non-deterministic, or explicit value)"],"output_types":["generated text (string)","deterministic output (identical across runs with same seed)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_2","uri":"capability://text.generation.language.multi.model.architecture.support.with.automatic.model.type.detection","name":"multi-model architecture support with automatic model type detection","description":"Supports inference across multiple transformer architectures (GPT-2, GPT-J, LLaMA, Falcon, MPT, StarCoder, Dolly, Replit, etc.) with automatic model type detection from GGML file headers or explicit specification via model_type parameter. The native implementation uses architecture-specific forward pass kernels compiled into the GGML library, while the Python layer provides a unified LLM class interface that abstracts away architecture differences, allowing users to swap models without code changes.","intents":["Experiment with different model architectures without rewriting inference code","Load GGML models without manually specifying architecture type","Compare model performance across different families (LLaMA vs Falcon vs MPT) with identical API","Support multiple models in a single application with consistent interface"],"best_for":["Researchers benchmarking multiple model architectures","Application developers supporting user-provided models without hardcoding architecture logic","Teams migrating between model families (e.g., GPT-J to LLaMA) with minimal code refactoring"],"limitations":["Only architectures with GGML implementations supported; newer models (GPT-4, Claude, Gemini) not available","Context length parameter only works for LLaMA, MPT, Falcon; other architectures ignore context_length setting","GPU acceleration (CUDA/Metal) support varies by architecture; some models CPU-only","Model type detection from file headers may fail for custom or non-standard GGML files; requires explicit model_type parameter","No automatic architecture inference from Hugging Face model cards; must manually specify or infer from file metadata"],"requires":["GGML model file (.gguf or .bin) for the target architecture","Python 3.8+","Optional: model_type string if automatic detection fails"],"input_types":["model file path (string)","model_type identifier (string: 'gpt2', 'gptj', 'llama', 'falcon', 'mpt', 'gpt_bigcode', etc.)","model configuration (dict with architecture-specific parameters)"],"output_types":["LLM object with unified interface","generated text via __call__ method"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_3","uri":"capability://automation.workflow.hardware.aware.layer.offloading.with.gpu.cpu.memory.management","name":"hardware-aware layer offloading with gpu/cpu memory management","description":"Enables selective execution of transformer layers on GPU (CUDA/Metal) while keeping remaining layers on CPU, controlled via gpu_layers parameter that specifies how many layers to offload. The native implementation manages GPU memory allocation, handles data transfer between CPU and GPU memory spaces, and automatically falls back to CPU-only execution if GPU memory is exhausted or GPU support is unavailable. This approach reduces peak memory usage and latency compared to full GPU execution while avoiding the overhead of CPU-only inference.","intents":["Run large models on GPUs with limited VRAM by offloading only some layers","Achieve faster inference than CPU-only while using less GPU memory than full GPU execution","Gracefully degrade to CPU execution on systems without GPU support","Optimize memory-latency tradeoffs for specific hardware configurations"],"best_for":["Developers with mid-range GPUs (4-8GB VRAM) running large models (7B-13B parameters)","Edge deployment scenarios with heterogeneous hardware (some nodes with GPU, some CPU-only)","Teams optimizing inference cost by using cheaper GPU instances with partial offloading"],"limitations":["GPU layer offloading only supported for CUDA (NVIDIA) and Metal (Apple Silicon); no ROCm support","GPU memory management is automatic but not user-configurable; no fine-grained control over memory allocation strategy","Data transfer between CPU and GPU memory adds latency (~1-5ms per layer depending on layer size); full GPU execution may be faster for small models","Layer offloading granularity is fixed at transformer layers; cannot offload sub-layer operations","No multi-GPU support; gpu_layers parameter applies to single GPU only"],"requires":["NVIDIA GPU with CUDA compute capability 3.5+ (for CUDA) OR Apple Silicon/Intel GPU (for Metal)","CUDA toolkit 11.0+ installed (for CUDA support)","macOS 12.0+ (for Metal support)","Sufficient GPU VRAM for offloaded layers (typically 1-4GB per 7B model with partial offloading)","gpu_layers parameter set to value > 0"],"input_types":["gpu_layers (int): number of layers to offload to GPU","model file path (string)","generation config (dict)"],"output_types":["generated text (string)","execution metrics (implicit: latency reduction vs CPU-only)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_4","uri":"capability://tool.use.integration.hugging.face.transformers.pipeline.integration.with.drop.in.model.replacement","name":"hugging face transformers pipeline integration with drop-in model replacement","description":"Integrates with Hugging Face Transformers library via custom pipeline classes that accept ctransformers LLM objects as the underlying model, enabling use of Transformers' pipeline abstraction (text-generation, question-answering, etc.) with GGML-optimized inference. The integration wraps the LLM class to expose a compatible interface (generate() method, tokenizer integration) that Transformers pipelines expect, allowing users to swap HF Transformers models for ctransformers models without changing pipeline code.","intents":["Use Transformers pipelines with locally-optimized GGML models instead of cloud APIs","Leverage Transformers' high-level abstractions (pipelines, task-specific classes) with ctransformers performance","Migrate existing Transformers code to use ctransformers by changing only model loading","Combine Transformers preprocessing/postprocessing with ctransformers inference"],"best_for":["Teams with existing Transformers codebases wanting to switch to local GGML inference","Developers building NLP applications using Transformers pipelines who need offline capability","Researchers comparing Transformers vs ctransformers performance on identical pipelines"],"limitations":["Integration is limited to text generation pipelines; other task types (NER, classification) require custom wrappers","Tokenizer must be manually loaded from Hugging Face (ctransformers does not provide tokenizer); requires separate HF model card access","Pipeline features like batch processing and attention visualization may not work with ctransformers backend","No automatic model conversion from Transformers format to GGML; requires external quantization tools (llama.cpp, GPTQ)","Transformers dependency adds ~500MB to installation size; not lightweight for edge deployment"],"requires":["transformers library (>=4.20.0)","ctransformers library","GGML model file (.gguf format)","Hugging Face tokenizer (loaded separately via AutoTokenizer)"],"input_types":["text prompt (string)","Transformers pipeline config (dict)","LLM object from ctransformers"],"output_types":["pipeline output (dict with 'generated_text' key)","token sequences (list)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_5","uri":"capability://tool.use.integration.langchain.llm.provider.integration.with.streaming.and.callback.support","name":"langchain llm provider integration with streaming and callback support","description":"Implements LangChain's BaseLLM interface to expose ctransformers models as LangChain LLM providers, enabling use in LangChain chains, agents, and memory systems. The integration wraps the LLM class to implement LangChain's required methods (_generate, _stream, _call), handles prompt formatting and token counting, and supports LangChain callbacks for monitoring generation progress. This allows ctransformers models to be used interchangeably with OpenAI, Anthropic, and other LangChain-supported providers.","intents":["Build LangChain agents and chains using locally-optimized GGML models instead of cloud APIs","Use LangChain's memory, retrieval, and tool-calling abstractions with ctransformers inference","Monitor and debug LLM generation in LangChain applications via callback hooks","Reduce API costs by replacing cloud LLM providers with local ctransformers models"],"best_for":["Teams building LangChain applications who want to avoid cloud LLM API costs","Developers prototyping agents and chains with local models before deploying to production","Organizations with data privacy requirements preventing cloud API usage"],"limitations":["LangChain integration requires LangChain library (>=0.0.200); adds dependency overhead","Token counting is approximate (uses simple heuristics) and may not match actual tokenizer; affects cost estimation in LangChain","Streaming support requires LangChain version with streaming callbacks; older versions may not stream tokens","Tool-calling and function-calling not natively supported by ctransformers; requires custom prompt engineering in LangChain","Memory systems (ConversationBufferMemory, etc.) work but may accumulate context beyond model's context window"],"requires":["langchain library (>=0.0.200)","ctransformers library","GGML model file","Python 3.8+"],"input_types":["text prompt (string)","LangChain chain/agent config (dict)","callback handlers (LangChain Callback objects)"],"output_types":["LangChain LLMResult object with generations","streaming token callbacks","chain/agent outputs (dict or structured data)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_6","uri":"capability://text.generation.language.configurable.text.generation.with.fine.grained.sampling.and.repetition.control","name":"configurable text generation with fine-grained sampling and repetition control","description":"Exposes a Config class that encapsulates all text generation hyperparameters (temperature, top_k, top_p, repetition_penalty, max_new_tokens, stop sequences, etc.) as a structured configuration object. The Config object is passed to the LLM's __call__ method to control generation behavior, with sensible defaults (temperature=0.8, top_p=0.95, max_new_tokens=256) that can be overridden per-generation. The native implementation applies these parameters during token sampling, with repetition penalty calculated over a configurable window (last_n_tokens) to penalize repeated tokens.","intents":["Fine-tune generation quality and diversity by adjusting temperature and top-p parameters","Prevent repetitive outputs using repetition_penalty and last_n_tokens configuration","Control maximum output length and stop generation at specific sequences","Experiment with different sampling strategies without modifying model code"],"best_for":["Researchers experimenting with sampling hyperparameters and their effects on output quality","Application developers tuning model behavior for specific use cases (creative writing vs factual Q&A)","Teams building multi-model systems with per-model generation configuration"],"limitations":["No per-token control; all parameters applied uniformly across generation","Repetition penalty uses only last_n_tokens window (default 64); longer-range repetition not penalized","No advanced decoding algorithms (beam search, constrained decoding); only greedy and sampling-based","Temperature and top_p parameters are mutually exclusive in some implementations; both applied simultaneously here","No dynamic parameter adjustment during generation; parameters fixed at start"],"requires":["ctransformers library","Loaded LLM model","Python 3.8+"],"input_types":["Config object with fields: temperature (float), top_k (int), top_p (float), repetition_penalty (float), max_new_tokens (int), stop (list), last_n_tokens (int), seed (int), stream (bool), reset (bool), batch_size (int), threads (int), context_length (int), gpu_layers (int)"],"output_types":["generated text (string)","token sequences (list of ints)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_7","uri":"capability://automation.workflow.automatic.model.download.and.caching.from.hugging.face.hub","name":"automatic model download and caching from hugging face hub","description":"Provides utility functions to automatically download GGML models from Hugging Face Hub repositories and cache them locally, with support for specifying model names, revisions, and cache directories. The download mechanism uses Hugging Face's hf_hub_download API to fetch model files with progress tracking and automatic retry logic, storing downloaded models in a local cache directory (default ~/.cache/huggingface/hub) to avoid re-downloading on subsequent loads.","intents":["Automatically fetch GGML models from Hugging Face without manual downloads","Cache downloaded models locally to avoid repeated downloads","Specify model versions/revisions from Hugging Face Hub","Simplify model loading with automatic path resolution"],"best_for":["Developers building applications that need to download models on first run","Teams deploying models to cloud/edge without pre-staging model files","Users wanting simple one-line model loading without manual file management"],"limitations":["Requires internet connectivity for initial download; no offline-first support","Cache directory must have sufficient disk space (7B model ~4GB, 13B model ~8GB)","No automatic model validation or checksum verification; relies on Hugging Face Hub integrity","Download progress tracking depends on hf_hub_download implementation; may not work with all network configurations (proxies, firewalls)","No built-in model selection or recommendation; users must know exact model name/repo"],"requires":["huggingface-hub library (>=0.10.0)","Internet connectivity","Disk space for model files (4-16GB depending on model size)","Hugging Face Hub account (optional, for gated models)"],"input_types":["model name (string, format: 'repo_id/model_name')","revision (string, optional: branch/tag/commit)","cache directory (string, optional)"],"output_types":["local file path (string) to downloaded model","LLM object initialized with downloaded model"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_8","uri":"capability://automation.workflow.multi.threaded.token.generation.with.configurable.thread.pool","name":"multi-threaded token generation with configurable thread pool","description":"Supports multi-threaded token generation via threads parameter that controls the number of CPU threads used for evaluating tokens during inference. The native C/C++ implementation uses thread-level parallelism (via OpenMP or pthreads) to distribute matrix operations across multiple cores, with threads parameter passed to GGML's compute graph executor. Setting threads=-1 uses all available CPU cores, while explicit values (e.g., threads=4) limit parallelism to improve latency on systems with many cores or reduce CPU contention in multi-process environments.","intents":["Maximize CPU utilization and throughput on multi-core systems","Reduce latency by limiting thread pool size on systems with many cores","Control CPU resource usage in multi-process or containerized environments","Optimize inference performance for specific hardware configurations"],"best_for":["Developers optimizing inference latency on multi-core CPUs","Teams running multiple inference processes on shared hardware","Researchers benchmarking CPU inference performance across different thread counts"],"limitations":["Thread pool overhead may exceed benefits on systems with <4 cores; single-threaded execution often faster","No automatic thread count tuning; users must manually experiment to find optimal value","Thread contention with other processes may degrade performance; no CPU affinity control","GPU execution (via gpu_layers) may not benefit from multi-threading; GPU compute dominates latency","No NUMA-aware thread scheduling; performance may degrade on NUMA systems without explicit configuration"],"requires":["Multi-core CPU (2+ cores recommended)","ctransformers library compiled with threading support (OpenMP or pthreads)","threads parameter in Config or LLM.__call__()"],"input_types":["threads (int): number of threads (-1 for all cores, or explicit count)"],"output_types":["generated text (string)","execution metrics (implicit: latency reduction via parallelism)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ctransformers__cap_9","uri":"capability://automation.workflow.batch.token.evaluation.with.configurable.batch.size.for.prompt.processing","name":"batch token evaluation with configurable batch size for prompt processing","description":"Supports batch processing of prompt tokens via batch_size parameter that controls how many tokens are evaluated simultaneously during the prompt phase (before generation). The native implementation uses GGML's batched matrix operations to process multiple tokens in a single forward pass, reducing total compute time compared to token-by-token evaluation. Larger batch sizes improve throughput but increase memory usage; batch_size parameter allows tuning this tradeoff for specific hardware constraints.","intents":["Reduce prompt processing latency by batching token evaluation","Optimize memory usage by tuning batch size for available VRAM","Improve throughput when processing multiple prompts or long contexts","Balance latency and memory usage for specific hardware configurations"],"best_for":["Developers processing long prompts or contexts where prompt latency dominates","Teams optimizing inference throughput on hardware with limited VRAM","Researchers benchmarking batch processing effects on inference performance"],"limitations":["Batch size only affects prompt processing; generation phase still token-by-token","Larger batch sizes increase memory usage; may cause OOM errors if set too high","No automatic batch size tuning; users must manually experiment or estimate from VRAM","Batch processing overhead may exceed benefits for short prompts (<100 tokens)","GPU execution (via gpu_layers) may have different optimal batch sizes than CPU; no automatic selection"],"requires":["ctransformers library","batch_size parameter in Config or LLM.__call__()","Sufficient memory for batch_size tokens (typically 1-4MB per token)"],"input_types":["batch_size (int): number of tokens to evaluate per batch (default 8)"],"output_types":["generated text (string)","execution metrics (implicit: prompt latency reduction)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","Pre-quantized GGML model files (GGUF format) or compatible model weights","For CUDA: NVIDIA GPU with compute capability 3.5+, CUDA toolkit installed","For Metal: macOS 12.0+ with Apple Silicon or Intel GPU","For CPU: x86-64 processor (AVX/AVX2 support recommended for performance)","Loaded LLM model via LLM class","Generator-aware client code to consume streaming output","ctransformers library","reset parameter in Config or LLM.__call__()","Manual conversation history management if needed"],"failure_modes":["Inference-only — no fine-tuning or training capabilities; model weights must be pre-trained","Limited to GGML-compatible model architectures (GPT-2, LLaMA, Falcon, MPT, StarCoder); newer architectures require upstream GGML support","GPU acceleration limited to CUDA (NVIDIA) and Metal (Apple Silicon); no ROCm support for AMD GPUs","Context length parameter only supported for LLaMA, MPT, and Falcon models; other architectures use fixed context windows","No built-in distributed inference across multiple machines; single-process execution only","Streaming adds minimal latency (~1-2ms per token) but requires client-side buffering for full output","Stop sequences are checked only after token generation, not during decoding; may generate partial tokens beyond stop sequence","Repetition penalty calculation uses only last_n_tokens (default 64); longer-range repetition patterns not penalized","No beam search or other advanced decoding algorithms; only greedy and sampling-based generation supported","Temperature and top-p parameters applied uniformly across all tokens; no per-token control","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.52,"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":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:19.404Z","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=pypi-ctransformers","compare_url":"https://unfragile.ai/compare?artifact=pypi-ctransformers"}},"signature":"DTppSY54tDhRGOHCwiCLBok2VYYlZSmVSzoMkDustlQZWow9Tuo154rJ6XB+I+84HLMJIdmkKyhCfv9MIUM0CQ==","signedAt":"2026-06-23T03:02:04.945Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-ctransformers","artifact":"https://unfragile.ai/pypi-ctransformers","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-ctransformers","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"}}