{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"text-generation-webui","slug":"text-generation-webui","name":"Text Generation WebUI","type":"model","url":"https://github.com/oobabooga/text-generation-webui","page_url":"https://unfragile.ai/text-generation-webui","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"text-generation-webui__cap_0","uri":"capability://tool.use.integration.multi.backend.model.loading.with.unified.interface","name":"multi-backend model loading with unified interface","description":"Dynamically loads language models from multiple backends (llama.cpp, ExLlamaV2/V3, Transformers, TensorRT-LLM) through a hub-and-spoke architecture where models.py acts as a loader dispatcher that populates shared.model and shared.tokenizer global state. The system detects model format (GGUF, GPTQ, safetensors) and routes to the appropriate backend loader, abstracting backend-specific initialization complexity behind a single load_model() interface.","intents":["I want to switch between different quantized model formats without rewriting code","I need to load a model and have it automatically available to all UI components","I want to support multiple hardware backends (CPU, GPU, mixed) with a single codebase"],"best_for":["developers building local LLM applications with hardware flexibility","teams supporting multiple quantization formats (GGUF, GPTQ, AWQ) in production","researchers experimenting with different model backends without refactoring"],"limitations":["Model switching requires full unload/reload cycle — no hot-swapping between backends","VRAM management is backend-specific; no unified memory pooling across loaders","ExLlama backends require specific CUDA versions; compatibility matrix is complex"],"requires":["Python 3.9+","PyTorch 2.0+ (for Transformers backend)","CUDA 11.8+ (for GPU acceleration, optional for CPU-only)","Model files in GGUF, GPTQ, or safetensors format"],"input_types":["model identifier (string path or HuggingFace repo ID)","backend specification (enum: llama_cpp, exllamav2, transformers)","configuration dict with quantization and device settings"],"output_types":["loaded model object (backend-specific)","tokenizer object (HuggingFace AutoTokenizer)","metadata dict with model properties"],"categories":["tool-use-integration","model-loading"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_1","uri":"capability://text.generation.language.streaming.text.generation.with.configurable.sampling","name":"streaming text generation with configurable sampling","description":"Implements a text generation pipeline (text_generation.py) that streams tokens in real-time using backend-specific generate() methods while applying configurable sampling strategies (temperature, top-p, top-k, repetition penalty, etc.). The pipeline supports both greedy decoding and stochastic sampling, with per-model preset configurations stored in models_settings.py that override global defaults, enabling fine-grained control over generation behavior without code changes.","intents":["I want to stream model outputs token-by-token to the UI for real-time feedback","I need to tune sampling parameters per model without editing code","I want to prevent repetition and control output diversity across different models"],"best_for":["chat interface developers needing real-time token streaming","researchers tuning sampling hyperparameters across model families","production systems requiring deterministic or stochastic generation modes"],"limitations":["Sampling parameters are applied at generation time; no mid-generation adjustment","Streaming adds ~50-100ms latency per token due to UI update overhead","Some backends (ExLlama) have limited sampler support compared to Transformers"],"requires":["Loaded model in shared.model","Tokenizer in shared.tokenizer","Generation parameters dict with keys: temperature, top_p, top_k, repetition_penalty, etc.","Backend support for streaming (all supported backends implement this)"],"input_types":["prompt (string)","generation parameters (dict with sampling config)","max_new_tokens (int)","stopping_strings (list of strings)"],"output_types":["token stream (generator yielding strings)","complete generated text (string)","generation metadata (dict with stop_reason, tokens_generated)"],"categories":["text-generation-language","streaming"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_10","uri":"capability://search.retrieval.model.downloading.and.caching.from.huggingface.hub","name":"model downloading and caching from huggingface hub","description":"Integrates HuggingFace Hub integration for discovering, downloading, and caching models directly from the web UI. The system manages model downloads with progress tracking, supports resumable downloads, and caches models in a configurable directory to avoid re-downloading. Users can search for models by name or filter by size/quantization format, with automatic detection of model format (GGUF, safetensors, etc.) and routing to the appropriate backend loader.","intents":["I want to browse and download models from HuggingFace Hub without using command-line tools","I need to see download progress and resume interrupted downloads","I want to filter models by size and quantization format to find ones that fit my hardware"],"best_for":["non-technical users discovering and downloading models via web UI","teams managing model caches across multiple machines","researchers experimenting with different model variants from HuggingFace"],"limitations":["Download speed depends on HuggingFace Hub bandwidth; no P2P or CDN support","Model search is basic; no advanced filtering by architecture, license, or performance metrics","Cache management is manual; no automatic cleanup of old models or disk space monitoring"],"requires":["Internet connection to HuggingFace Hub","huggingface-hub library","Disk space for model caching (typically 5-100GB per model)"],"input_types":["model search query (string)","filter criteria (size, quantization format, model type)","HuggingFace model ID (e.g., 'meta-llama/Llama-2-7b-hf')"],"output_types":["list of available models with metadata (size, format, downloads)","download progress (percentage, speed, ETA)","cached model path (string)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_11","uri":"capability://tool.use.integration.gradio.based.responsive.web.interface.with.real.time.streaming","name":"gradio-based responsive web interface with real-time streaming","description":"Builds the entire web UI using Gradio 3.40+, which provides responsive HTML/CSS/JavaScript frontend with real-time streaming support via WebSockets. The interface is organized into tabs (Chat, Notebook, Training, Model Menu, Extensions) with Gradio components (Textbox, Slider, Dropdown, etc.) that automatically handle state management and event binding. Streaming responses are rendered in real-time as tokens arrive, with automatic UI updates without page refresh.","intents":["I want a responsive web interface that works on desktop and mobile without custom HTML/CSS","I need real-time token streaming with automatic UI updates as the model generates","I want to build custom UI components without learning web development"],"best_for":["developers building LLM interfaces without web development expertise","teams deploying models on shared servers with web-based access","researchers needing rapid UI prototyping for model experimentation"],"limitations":["Gradio abstracts away HTML/CSS; advanced styling requires custom CSS injection","Streaming performance degrades with very long outputs (>10k tokens) due to DOM updates","No built-in authentication; requires reverse proxy (nginx) for multi-user access control"],"requires":["Gradio 3.40+","Python 3.9+","Modern web browser (Chrome, Firefox, Safari, Edge)"],"input_types":["Gradio component definitions (Python code)","event handlers (Python functions)","streaming generators (Python generators yielding strings)"],"output_types":["HTML/CSS/JavaScript web interface","real-time streaming via WebSocket","JSON API for programmatic access"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_12","uri":"capability://memory.knowledge.context.window.management.with.automatic.truncation","name":"context window management with automatic truncation","description":"Implements intelligent context window management that counts tokens in the conversation history using the actual model's tokenizer and automatically truncates old messages when approaching the model's context limit. The system maintains a configurable buffer (e.g., 200 tokens) to ensure generation space. Truncation strategy is configurable (remove oldest messages, summarize, or sliding window). The context window size is auto-detected from model metadata or can be manually specified per model.","intents":["Prevent context overflow errors by automatically managing conversation history","Maintain long conversations without manual message deletion","Configure context window size per model without code changes"],"best_for":["chatbot developers who need automatic context management","long-running conversation applications"],"limitations":["Truncation is naive (removes oldest messages first) rather than semantic importance-based","No built-in conversation summarization (would require additional inference)","Context window auto-detection from model metadata is unreliable (many models have incorrect metadata)","Truncation happens silently without user notification"],"requires":["Python 3.10+","Tokenizer compatible with selected model","Model context window specification (via metadata or manual config)"],"input_types":["conversation_history (list of dicts)","context_window_size (int)","buffer_tokens (int)"],"output_types":["truncated_history (list of dicts)","tokens_removed (int)"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_13","uri":"capability://code.generation.editing.model.backend.abstraction.with.lazy.loading","name":"model backend abstraction with lazy loading","description":"Abstracts backend-specific implementation details (llama.cpp, ExLlama, Transformers) behind a unified Python interface in models.py. Each backend is loaded lazily (only when needed) to minimize startup time. The abstraction layer handles backend-specific initialization (e.g., ExLlama's context manager, llama.cpp's server startup) and exposes a common generate() method. Backend selection is automatic based on model format or can be explicitly specified via command-line flag.","intents":["Switch backends without rewriting inference code","Minimize startup time by deferring backend initialization","Support multiple backends in the same application"],"best_for":["developers building backend-agnostic LLM applications","teams evaluating different backends for performance"],"limitations":["Abstraction adds ~50-100ms overhead per inference call for method dispatch","Not all backends support all features (e.g., streaming, quantization)","Backend-specific optimizations are hidden (users can't tune backend-specific parameters)","Lazy loading can cause unexpected delays on first inference"],"requires":["Python 3.10+","At least one backend installed (Transformers, llama.cpp, ExLlama, etc.)"],"input_types":["model_path (string)","backend_name (string, optional)"],"output_types":["backend_instance (Python object)","generate_function (callable)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_14","uri":"capability://text.generation.language.sampler.configuration.and.custom.sampling.strategies","name":"sampler configuration and custom sampling strategies","description":"Exposes 15+ sampling methods (temperature, top-p, top-k, min-p, DRY, mirostat, etc.) via a configuration system that allows users to create and save custom sampling presets. Presets are stored in user_data/presets.yaml and can be selected via UI dropdown or API parameter. The sampling pipeline (text_generation.py) applies samplers in a configurable order, allowing composition of multiple sampling strategies. Advanced users can implement custom samplers as Python functions and register them with the sampling registry.","intents":["Experiment with different sampling strategies without code changes","Create and save sampling presets for different use cases (creative writing, factual QA, etc.)","Implement custom sampling algorithms for specialized applications"],"best_for":["researchers tuning sampling hyperparameters","teams building applications with different sampling requirements per use case"],"limitations":["Sampling parameter interactions are not well-documented (e.g., temperature + top-p behavior)","No built-in sampling parameter optimization or recommendation","Custom sampler implementation requires Python knowledge","Some samplers (mirostat, DRY) have limited backend support"],"requires":["Python 3.10+","Understanding of sampling algorithms","Optional: custom sampler implementation"],"input_types":["sampler_name (string)","sampler_params (dict with temperature, top_p, etc.)"],"output_types":["sampled_token_ids (list)","sampling_metadata (dict)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_2","uri":"capability://text.generation.language.chat.interface.with.conversation.history.and.role.based.formatting","name":"chat interface with conversation history and role-based formatting","description":"Provides a Gradio-based chat UI (ui.py, ui_chat.py) that maintains conversation history as a list of {role, content} dicts, automatically formats messages according to model-specific chat templates (Alpaca, ChatML, Llama2, etc.), and renders streaming responses in real-time. The system detects the appropriate template from model metadata and applies it during generation, handling edge cases like system prompts and multi-turn conversations without manual formatting.","intents":["I want a web chat interface that works with any model without manual prompt engineering","I need to maintain multi-turn conversation context and format it correctly for the model","I want to see responses stream in real-time as tokens are generated"],"best_for":["non-technical users interacting with local models via web UI","developers building chatbot applications with automatic prompt formatting","teams testing models across different chat template formats"],"limitations":["Chat template detection relies on model metadata; custom templates require manual specification","Conversation history is stored in memory only; no built-in persistence to disk","Maximum context length is model-dependent; no automatic context window management or summarization"],"requires":["Gradio 3.40+","Loaded model with tokenizer","Model metadata including chat_template field (optional; falls back to Alpaca format)"],"input_types":["user message (string)","conversation history (list of {role, content} dicts)","system prompt (string, optional)"],"output_types":["formatted prompt (string ready for model)","streamed response tokens (generator)","updated conversation history (list of dicts)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_3","uri":"capability://automation.workflow.lora.fine.tuning.with.training.ui.and.parameter.management","name":"lora fine-tuning with training ui and parameter management","description":"Integrates LoRA (Low-Rank Adaptation) fine-tuning through a dedicated training tab that manages training datasets, hyperparameters (learning rate, rank, alpha), and model checkpoints. The system loads base models, applies LoRA adapters on top, and trains using HuggingFace transformers Trainer API with support for multi-GPU training and gradient accumulation. Trained LoRA weights are saved separately and can be merged with the base model or applied dynamically during inference.","intents":["I want to fine-tune a model on custom data without modifying the base model weights","I need a UI to configure training hyperparameters and monitor training progress","I want to save and load multiple LoRA adapters for different tasks"],"best_for":["researchers experimenting with parameter-efficient fine-tuning","teams building domain-specific model variants without full retraining","developers needing to preserve base model weights while adapting to new tasks"],"limitations":["LoRA training requires significant VRAM (typically 24GB+ for 7B models); CPU training is very slow","Training UI is basic; no built-in learning rate scheduling, warmup, or advanced optimization","Merging LoRA adapters into base model is destructive; original base model cannot be recovered"],"requires":["Base model loaded in shared.model","Training dataset in JSON or CSV format with text column","CUDA GPU with 24GB+ VRAM recommended (8GB minimum for small models)","HuggingFace transformers 4.30+","peft library for LoRA implementation"],"input_types":["training dataset (JSON/CSV with text field)","LoRA hyperparameters (rank, alpha, target_modules, learning_rate)","training config (epochs, batch_size, gradient_accumulation_steps)"],"output_types":["trained LoRA weights (safetensors format)","training logs (loss, perplexity per epoch)","merged model (optional; base model + LoRA weights combined)"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_4","uri":"capability://tool.use.integration.extension.system.with.plugin.architecture.and.openai.compatible.api","name":"extension system with plugin architecture and openai-compatible api","description":"Implements a plugin architecture where extensions are Python modules loaded dynamically from the extensions/ directory, with hooks into the UI (custom tabs), generation pipeline (pre/post-processing), and API layer. Built-in extensions include an OpenAI-compatible REST API (compatible with ChatGPT client libraries) that exposes the local model as /v1/chat/completions and /v1/completions endpoints, allowing drop-in replacement of OpenAI API calls with local inference.","intents":["I want to extend the UI with custom tabs without modifying core code","I need to expose my local model via OpenAI-compatible API for existing applications","I want to add custom pre/post-processing to the generation pipeline"],"best_for":["developers building custom features on top of text-generation-webui","teams migrating from OpenAI API to local inference with minimal code changes","researchers adding custom sampling or evaluation logic to the generation pipeline"],"limitations":["Extension API is undocumented; requires reading source code to understand hooks","OpenAI API compatibility is partial — streaming, function calling, and vision features have limited support","Extensions run in the same process as the main app; no isolation or sandboxing"],"requires":["Python 3.9+","Gradio 3.40+ (for UI extensions)","Extension module in extensions/ directory with specific entry point functions","For OpenAI API: FastAPI (included in dependencies)"],"input_types":["extension module (Python file with ui(), setup(), or custom functions)","HTTP requests to /v1/chat/completions (JSON with messages, model, temperature, etc.)"],"output_types":["Gradio UI components (custom tabs, buttons, inputs)","HTTP responses (JSON with choices, usage, finish_reason)","streaming responses (Server-Sent Events format)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_5","uri":"capability://text.generation.language.notebook.mode.with.stateful.code.execution.and.markdown.rendering","name":"notebook mode with stateful code execution and markdown rendering","description":"Provides a Jupyter-like notebook interface where users can write markdown and code cells, execute them sequentially with persistent state, and interact with the loaded model through a Python API. The notebook mode maintains a shared execution context across cells, allowing users to call the model, process outputs, and build complex workflows without leaving the web UI. Supports both synchronous and asynchronous execution with streaming output.","intents":["I want to experiment with the model interactively like Jupyter without leaving the web UI","I need to write code that calls the model multiple times with different inputs and analyze results","I want to document my experiments with markdown and code in a single notebook"],"best_for":["researchers prototyping model interactions and analyzing outputs","developers building complex generation workflows with intermediate processing","non-technical users documenting model behavior and creating reproducible examples"],"limitations":["Notebook execution is single-threaded; long-running cells block the UI","No built-in persistence; notebooks are lost on page refresh unless manually saved","Limited debugging support — errors are displayed as text without stack traces"],"requires":["Gradio 3.40+","Python 3.9+ with exec() support","Loaded model in shared.model for API access"],"input_types":["markdown text (for documentation cells)","Python code (for execution cells)","model parameters (passed to generation functions)"],"output_types":["rendered markdown (HTML)","code execution results (text, plots, tables)","model outputs (text, tokens, metadata)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_6","uri":"capability://automation.workflow.model.specific.configuration.with.yaml.based.settings.override","name":"model-specific configuration with yaml-based settings override","description":"Implements a configuration system where model-specific settings (sampling parameters, chat template, system prompt, LoRA adapters) are stored in YAML files in models/ directory and automatically loaded when a model is selected. The system merges model-specific settings with global defaults, allowing per-model customization without UI changes. Configuration includes generation presets, quantization settings, and backend-specific optimizations that are applied transparently during model loading.","intents":["I want different sampling parameters for different models without manual adjustment each time","I need to specify model-specific system prompts and chat templates in configuration","I want to automatically load LoRA adapters and quantization settings per model"],"best_for":["teams managing multiple models with different optimal configurations","researchers comparing models with consistent sampling parameters","production systems requiring reproducible model behavior across deployments"],"limitations":["Configuration is static; changes require model reload to take effect","YAML schema is undocumented; users must infer structure from examples","No validation of configuration values; invalid settings fail silently at generation time"],"requires":["YAML file in models/ directory with model name","Valid keys: chat_template, system_prompt, generation_settings, lora_adapters, etc.","Model must be loaded for settings to be applied"],"input_types":["YAML configuration file with model settings","model identifier (string) to trigger settings load"],"output_types":["merged configuration dict (model-specific + global defaults)","applied settings (sampling parameters, chat template, etc.)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_7","uri":"capability://image.visual.multi.modal.image.generation.integration.with.stable.diffusion","name":"multi-modal image generation integration with stable diffusion","description":"Integrates image generation capabilities through extensions that wrap Stable Diffusion models, allowing users to generate images from text prompts within the same web UI. The system manages separate image model loading, prompt processing, and output rendering alongside text generation. Supports multiple Stable Diffusion variants (SD 1.5, SDXL) with configurable sampling steps, guidance scale, and seed control for reproducible image generation.","intents":["I want to generate images from text prompts using the same web interface as my text model","I need to control image generation parameters like steps, guidance scale, and seed","I want to use different Stable Diffusion models without switching applications"],"best_for":["creative professionals building multi-modal workflows","researchers experimenting with text-to-image generation alongside language models","teams building all-in-one local AI interfaces"],"limitations":["Image generation requires separate model loading; VRAM must accommodate both text and image models","Image generation is slower than text generation; no streaming of image generation progress","Limited to Stable Diffusion variants; no support for other image generation models"],"requires":["Stable Diffusion model (safetensors or checkpoint format)","diffusers library (HuggingFace)","CUDA GPU with 8GB+ VRAM for simultaneous text + image generation","Image generation extension enabled"],"input_types":["text prompt (string)","negative prompt (string, optional)","generation parameters (steps, guidance_scale, height, width, seed)"],"output_types":["generated image (PIL Image or PNG bytes)","image metadata (seed, steps, guidance_scale used)"],"categories":["image-visual","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_8","uri":"capability://automation.workflow.vram.management.with.automatic.model.offloading.and.quantization.selection","name":"vram management with automatic model offloading and quantization selection","description":"Implements VRAM-aware model loading that automatically selects quantization formats (GGUF, GPTQ, AWQ) based on available GPU memory, supports layer offloading to CPU when VRAM is insufficient, and provides memory profiling to estimate model size before loading. The system tracks allocated VRAM across models and can unload models to free memory for new ones. Backend-specific optimizations (ExLlama's VRAM pooling, llama.cpp's memory mapping) are applied transparently based on available resources.","intents":["I want to load the largest model that fits in my GPU VRAM without manual calculation","I need to run multiple models simultaneously with automatic memory management","I want to understand how much VRAM each model uses before loading it"],"best_for":["developers with limited GPU VRAM (8GB-24GB) needing to maximize model size","teams running multiple models on shared hardware with automatic resource allocation","researchers comparing models with different quantization levels on fixed hardware"],"limitations":["Automatic quantization selection is heuristic-based; may not choose optimal format for specific use cases","Layer offloading to CPU significantly reduces generation speed (10-100x slower)","VRAM estimation is approximate; actual usage may vary by 10-20% due to framework overhead"],"requires":["NVIDIA GPU with CUDA support (or CPU fallback)","nvidia-ml-py for VRAM monitoring (optional but recommended)","Model available in multiple quantization formats for automatic selection"],"input_types":["model identifier (string)","available VRAM (int, auto-detected or manual)","quantization preference (enum: auto, GGUF, GPTQ, AWQ, none)"],"output_types":["selected quantization format (string)","estimated VRAM usage (int, MB)","loaded model with optimal memory configuration"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__cap_9","uri":"capability://automation.workflow.command.line.argument.parsing.with.persistent.settings.storage","name":"command-line argument parsing with persistent settings storage","description":"Implements a comprehensive argument parsing system using Python's argparse that handles 50+ command-line flags for model selection, backend configuration, UI settings, and API options. Arguments are merged with YAML-based persistent settings from user_data/settings.yaml, with command-line arguments taking precedence. The system supports environment variable overrides and generates a settings file on first run with sensible defaults, enabling both CLI-driven and UI-driven configuration workflows.","intents":["I want to configure the application via command-line arguments for automation and scripting","I need to persist settings across sessions without manual reconfiguration","I want environment variables to override settings for containerized deployments"],"best_for":["DevOps engineers deploying text-generation-webui in containers with environment-based config","developers automating model loading and API startup via shell scripts","teams managing multiple instances with different configurations"],"limitations":["Argument names are inconsistent (some use hyphens, some underscores); documentation is sparse","Settings file format is YAML but schema is undocumented; invalid settings fail silently","No validation of argument combinations; conflicting flags may produce unexpected behavior"],"requires":["Python 3.9+","argparse (standard library)","PyYAML for settings file parsing"],"input_types":["command-line arguments (strings with -- prefix)","environment variables (uppercase with TEXT_GENERATION_ prefix)","settings.yaml file (YAML format)"],"output_types":["parsed arguments (argparse.Namespace)","merged settings dict (CLI + YAML + env vars)","generated settings.yaml file (on first run)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"text-generation-webui__headline","uri":"capability://text.generation.language.local.large.language.model.interface","name":"local large language model interface","description":"Text Generation WebUI is a feature-rich Gradio web interface designed for running large language models locally, supporting various backends and offering extensive customization options.","intents":["best local LLM interface","local model training for text generation","Gradio interface for language models","how to run large language models offline","text generation tools for local use"],"best_for":["developers needing offline access to LLMs","users wanting to customize model interactions"],"limitations":["requires local setup","may need specific hardware for optimal performance"],"requires":["Python environment","compatible model files"],"input_types":["text prompts"],"output_types":["generated text","model responses"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PyTorch 2.0+ (for Transformers backend)","CUDA 11.8+ (for GPU acceleration, optional for CPU-only)","Model files in GGUF, GPTQ, or safetensors format","Loaded model in shared.model","Tokenizer in shared.tokenizer","Generation parameters dict with keys: temperature, top_p, top_k, repetition_penalty, etc.","Backend support for streaming (all supported backends implement this)","Internet connection to HuggingFace Hub","huggingface-hub library"],"failure_modes":["Model switching requires full unload/reload cycle — no hot-swapping between backends","VRAM management is backend-specific; no unified memory pooling across loaders","ExLlama backends require specific CUDA versions; compatibility matrix is complex","Sampling parameters are applied at generation time; no mid-generation adjustment","Streaming adds ~50-100ms latency per token due to UI update overhead","Some backends (ExLlama) have limited sampler support compared to Transformers","Download speed depends on HuggingFace Hub bandwidth; no P2P or CDN support","Model search is basic; no advanced filtering by architecture, license, or performance metrics","Cache management is manual; no automatic cleanup of old models or disk space monitoring","Gradio abstracts away HTML/CSS; advanced styling requires custom CSS injection","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"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":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=text-generation-webui","compare_url":"https://unfragile.ai/compare?artifact=text-generation-webui"}},"signature":"GCb4D6hZ/8tvg55Ix+0zA9DC9LuqXxpFzcbM9fzGPKrKIi9wV4cu2f4z/lW8/8qLBTBpo99s2+o8n0op2b3fAA==","signedAt":"2026-06-21T00:41:15.763Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/text-generation-webui","artifact":"https://unfragile.ai/text-generation-webui","verify":"https://unfragile.ai/api/v1/verify?slug=text-generation-webui","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"}}