{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"vscode-ex3ndr-llama-coder","slug":"llama-coder","name":"Llama Coder","type":"extension","url":"https://marketplace.visualstudio.com/items?itemName=ex3ndr.llama-coder","page_url":"https://unfragile.ai/llama-coder","categories":["code-editors"],"tags":["ai","assistant","code","development","llm"],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"vscode-ex3ndr-llama-coder__cap_0","uri":"capability://code.generation.editing.local.inference.code.autocompletion.with.quantized.language.models","name":"local-inference code autocompletion with quantized language models","description":"Generates inline code suggestions as developers type by running quantized CodeLlama models (3b-34b parameters) through a local Ollama runtime, eliminating cloud API calls and data transmission. The extension monitors editor state, extracts surrounding code context from the current file, and streams completion suggestions with configurable temperature and top-p sampling parameters. Unlike cloud-based alternatives, inference happens entirely on the developer's machine or a self-hosted remote Ollama server, with no telemetry or external API dependencies.","intents":["Get code completions without sending code to cloud services or GitHub","Run a self-hosted Copilot replacement that respects privacy and data sovereignty","Use code completion on machines with limited or no internet connectivity","Avoid GitHub Copilot licensing costs while maintaining IDE-integrated suggestions"],"best_for":["solo developers and small teams prioritizing code privacy and data sovereignty","enterprises with strict data residency requirements or IP protection policies","developers working offline or in air-gapped environments","builders seeking a free, open-architecture alternative to GitHub Copilot"],"limitations":["Inference latency varies 500ms-5s per completion depending on model size and hardware; no built-in latency metrics or performance monitoring","Context window size is undocumented — unclear how much surrounding code is analyzed for suggestions, potentially limiting multi-file awareness","Requires 16GB+ RAM minimum and 3-32GB VRAM depending on model selection; consumer GPUs and older NVIDIA cards (pre-30xx) experience significant slowdown","No built-in project structure analysis — cannot leverage type information, imports, or dependency graphs for context-aware suggestions","Model selection is manual; no automatic hardware detection or recommendation engine to guide users toward optimal model-hardware pairing","Remote inference adds network latency and requires manual Ollama server setup with `OLLAMA_HOST=0.0.0.0` environment variable configuration"],"requires":["Visual Studio Code (minimum version not specified in documentation)","Ollama runtime installed and running (version compatibility unknown)","16GB RAM minimum; 5GB+ VRAM for smallest models (stable-code:3b), up to 32GB for largest (codellama:34b-q6_K)","One of: Apple Silicon Mac (M1/M2/M3+), NVIDIA GPU with CUDA support, or CPU-only inference (significantly slower)"],"input_types":["source code (current file context)","programming language (auto-detected)","configuration parameters (temperature, top-p, trigger delay)"],"output_types":["code completion suggestions (inline, streamed)","multi-line code blocks"],"categories":["code-generation-editing","self-hosted-inference"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ex3ndr-llama-coder__cap_1","uri":"capability://code.generation.editing.multi.language.code.completion.with.automatic.language.detection","name":"multi-language code completion with automatic language detection","description":"Automatically detects the programming language of the current file (added in v0.0.8) and adapts CodeLlama inference to generate syntactically correct suggestions for that language. The extension supports any language that CodeLlama was trained on (Python, JavaScript, TypeScript, Java, C++, Go, Rust, etc.) as well as human languages for documentation and comments. Language detection is implicit in the file extension and syntax analysis, with no manual language selection required by the user.","intents":["Get language-appropriate code suggestions without manually specifying the language","Write code comments and docstrings in natural language alongside code","Switch between multiple programming languages in a single project without reconfiguration","Generate code in less common languages that GitHub Copilot may not support well"],"best_for":["polyglot developers working across multiple programming languages","teams using niche or domain-specific languages (Rust, Go, Kotlin, etc.)","developers writing documentation and comments alongside code"],"limitations":["Specific list of supported languages is not documented; unclear which languages receive optimal training coverage vs. degraded performance","Language detection relies on file extension and syntax heuristics; ambiguous file types (e.g., `.txt`, `.config`) may not be detected correctly","No language-specific context awareness — cannot leverage language-specific type systems, package managers, or build configurations for smarter suggestions","Human language support is undocumented; unclear if multilingual documentation generation is equally effective across languages"],"requires":["Visual Studio Code with file extension recognition","Ollama runtime with CodeLlama model (trained on 80+ programming languages)"],"input_types":["source code in any programming language","natural language text for comments and documentation"],"output_types":["code suggestions in the detected language","documentation and comment suggestions in natural language"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ex3ndr-llama-coder__cap_10","uri":"capability://code.generation.editing.model.quantization.strategy.with.hardware.aware.recommendations","name":"model quantization strategy with hardware-aware recommendations","description":"Provides guidance on selecting appropriate quantization levels (q4, q6_K, fp16) based on available hardware, with documented performance characteristics for different GPU and CPU configurations. The extension documents that q4 is 'optimal' for most use cases, q6_K is slower on macOS, and fp16 is slow on pre-30xx NVIDIA GPUs. This enables developers to make informed trade-offs between model quality (higher quantization = better quality) and inference speed (lower quantization = faster).","intents":["Choose the right model quantization for available hardware","Understand trade-offs between model quality and inference speed","Optimize inference latency on specific hardware (Mac M1/M2, RTX 4090, etc.)","Avoid slow quantizations on incompatible hardware (e.g., q6_K on macOS)"],"best_for":["developers optimizing inference performance on specific hardware","teams with heterogeneous hardware configurations (Macs, Windows, Linux)","builders experimenting with quantization strategies for LLM inference"],"limitations":["Quantization recommendations are generic and not automatically applied; users must manually select quantizations based on documentation","No automatic hardware detection; users must manually identify their GPU model and VRAM capacity","No performance benchmarks provided; unclear how much slower q6_K is on macOS or fp16 on pre-30xx NVIDIA","Quantization guidance is based on CodeLlama; unclear if recommendations apply to other models or future model updates"],"requires":["Knowledge of available GPU model and VRAM capacity","Ollama runtime supporting multiple quantization formats"],"input_types":["hardware configuration (GPU model, VRAM, CPU)"],"output_types":["quantization recommendations (q4, q6_K, fp16)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ex3ndr-llama-coder__cap_2","uri":"capability://code.generation.editing.configurable.inference.parameters.with.runtime.temperature.and.sampling.control","name":"configurable inference parameters with runtime temperature and sampling control","description":"Exposes temperature and top-p sampling parameters (added in v0.0.7) through VS Code settings, allowing developers to tune the randomness and diversity of code suggestions without restarting the extension or Ollama runtime. Temperature controls output randomness (lower = deterministic, higher = creative), while top-p controls nucleus sampling (lower = focused, higher = diverse). These parameters are passed directly to the Ollama inference API on each completion request, enabling real-time experimentation with suggestion quality.","intents":["Reduce hallucinations and get more deterministic suggestions by lowering temperature","Increase code diversity and creativity for exploratory coding by raising temperature","Fine-tune suggestion quality without restarting the IDE or switching models","Experiment with sampling strategies to find optimal settings for specific coding tasks"],"best_for":["developers optimizing completion quality for their specific coding style and domain","teams experimenting with different inference strategies for different project types","researchers and builders prototyping LLM-based code generation systems"],"limitations":["No preset configurations or recommended values provided; users must manually experiment to find optimal settings","Parameter changes apply globally to all completions; no per-file or per-language parameter overrides","No guidance on how temperature/top-p interact with model size or quantization; unclear which combinations are optimal","No A/B testing or metrics to measure impact of parameter changes on suggestion quality"],"requires":["VS Code settings panel access","Ollama runtime supporting temperature and top-p parameters"],"input_types":["temperature value (typically 0.0-1.0)","top-p value (typically 0.0-1.0)"],"output_types":["modified code suggestions with adjusted randomness/diversity"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ex3ndr-llama-coder__cap_3","uri":"capability://code.generation.editing.remote.ollama.inference.with.bearer.token.authentication","name":"remote ollama inference with bearer token authentication","description":"Supports connecting to a remote Ollama server (added in v0.0.14) instead of running inference locally, enabling distributed inference across machines and shared GPU resources. The extension sends completion requests to a configurable remote endpoint (default: `127.0.0.1:11434`, overridable in settings) and supports bearer token authentication for secured remote servers. This pattern allows teams to run a centralized Ollama instance on a high-end GPU machine and have multiple developers connect to it, reducing per-developer hardware requirements.","intents":["Share a single high-end GPU across multiple developers to reduce hardware costs","Run inference on a dedicated server while developing on a laptop or low-power machine","Centralize model management and updates across a team","Secure remote inference with authentication tokens for enterprise deployments"],"best_for":["small teams and startups sharing GPU resources to reduce infrastructure costs","enterprises deploying centralized LLM inference infrastructure","developers working on laptops or machines without dedicated GPUs"],"limitations":["Network latency adds 50-500ms per completion request depending on network quality and server load; no built-in latency monitoring or optimization","Remote Ollama server requires manual setup with `OLLAMA_HOST=0.0.0.0` environment variable to accept non-localhost connections; no automated server provisioning or health checks","Bearer token authentication is basic HTTP authentication; no support for mTLS, OAuth, or advanced security protocols","No load balancing or failover support; if remote server goes down, all connected developers lose completion functionality","No rate limiting or quota management; shared server can be overwhelmed by multiple simultaneous completion requests"],"requires":["Remote Ollama server running with `OLLAMA_HOST=0.0.0.0` (or specific IP binding)","Network connectivity to remote server (HTTP/HTTPS)","Bearer token if remote server requires authentication (optional)"],"input_types":["remote Ollama endpoint URL","bearer token (optional)"],"output_types":["code suggestions from remote inference"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ex3ndr-llama-coder__cap_4","uri":"capability://code.generation.editing.jupyter.notebook.code.completion.with.cell.aware.context","name":"jupyter notebook code completion with cell-aware context","description":"Extends code completion to Jupyter notebooks (added in v0.0.12) by analyzing individual notebook cells and generating suggestions that respect notebook execution order and cell dependencies. The extension detects when the user is editing a Jupyter notebook and adapts its context extraction to include relevant code from previous cells in the execution sequence, enabling suggestions that reference variables and functions defined earlier in the notebook.","intents":["Get code completions in Jupyter notebooks without switching to external editors","Generate suggestions that reference variables and functions from previous cells","Maintain IDE-integrated completion experience across notebooks and regular code files","Accelerate data science and exploratory coding workflows in notebooks"],"best_for":["data scientists and ML engineers using Jupyter notebooks for exploratory analysis","teams mixing notebook-based prototyping with production code","developers using VS Code's Jupyter extension for notebook editing"],"limitations":["Cell execution order is inferred from notebook structure, not actual execution history; if cells are run out of order, suggestions may reference undefined variables","Context window is limited to surrounding cells; unclear how many previous cells are analyzed for context","No support for notebook-specific features like magic commands (`%matplotlib`, `!pip install`, etc.); suggestions may not account for these","Notebook metadata and kernel information are not used; suggestions are language-agnostic and may not account for kernel-specific libraries"],"requires":["VS Code with Jupyter extension installed","Ollama runtime with CodeLlama model"],"input_types":["Jupyter notebook cells (Python, R, or other kernel languages)"],"output_types":["code suggestions for notebook cells"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ex3ndr-llama-coder__cap_5","uri":"capability://code.generation.editing.remote.file.editing.support.with.extension.compatibility","name":"remote file editing support with extension compatibility","description":"Enables code completion on remote files accessed through VS Code's Remote Development extension (added in v0.0.13), allowing developers to edit code on SSH servers, containers, or WSL environments while receiving local inference suggestions. The extension detects when a file is opened from a remote context and adapts its file reading and context extraction to work with remote file systems, maintaining completion functionality across local and remote editing scenarios.","intents":["Get code completions while editing code on remote servers via SSH","Use completions in Docker containers and WSL environments without local code copies","Maintain consistent completion experience across local and remote development workflows","Reduce friction when switching between local and remote development"],"best_for":["developers using VS Code Remote SSH for server-based development","teams using containerized development environments (Dev Containers)","Windows developers using WSL for Linux development"],"limitations":["Remote file context extraction may be slower than local file access due to network I/O; no caching or optimization for repeated context reads","Unclear how much remote file context is read for each completion; large remote files may cause network overhead","No support for remote Ollama inference in combination with remote file editing; inference still runs locally, creating a hybrid local-remote architecture","Remote file system permissions and access controls are not explicitly handled; may fail silently if extension lacks read permissions"],"requires":["VS Code Remote Development extension (SSH, Dev Containers, or WSL)","Network connectivity to remote server","Ollama runtime running locally (inference does not run remotely)"],"input_types":["remote files accessed through VS Code Remote extension"],"output_types":["code suggestions for remote files"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ex3ndr-llama-coder__cap_6","uri":"capability://code.generation.editing.pausable.completion.generation.with.manual.control","name":"pausable completion generation with manual control","description":"Allows developers to pause active code completion generation (added in v0.0.14) via a UI control or keybinding, stopping the inference process mid-stream and discarding partial suggestions. This enables developers to interrupt slow or unwanted completions without waiting for the model to finish, reducing latency and improving responsiveness in scenarios where the initial suggestion is clearly incorrect or irrelevant.","intents":["Stop slow completions that are taking too long to generate","Discard unwanted suggestions without waiting for full generation","Improve IDE responsiveness when inference is blocking user input","Manually control when suggestions are generated instead of always auto-completing"],"best_for":["developers using slower models or hardware where inference latency is noticeable","teams with strict latency requirements for IDE responsiveness","developers who prefer manual control over automatic suggestion generation"],"limitations":["Pause mechanism is undocumented; unclear if it's a UI button, keybinding, or command palette action","No resume capability; paused completions cannot be resumed; user must trigger a new completion","Pause only affects the current completion; does not disable future completions or change inference settings","No metrics on pause frequency or reasons; unclear if pause is addressing a real performance problem or just a convenience feature"],"requires":["Ollama runtime supporting cancellation of in-flight requests"],"input_types":["pause signal (keybinding or UI action)"],"output_types":["cancellation of active completion generation"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ex3ndr-llama-coder__cap_7","uri":"capability://code.generation.editing.configurable.completion.trigger.delay.with.debouncing","name":"configurable completion trigger delay with debouncing","description":"Allows developers to configure a delay (added in v0.0.12) before code completion is triggered after typing, reducing unnecessary inference requests and improving IDE responsiveness. The extension debounces completion requests by waiting for the specified delay after the last keystroke before sending a completion request to Ollama, preventing rapid-fire inference calls during fast typing. This pattern reduces computational load and network overhead while allowing developers to tune the delay based on their typing speed and hardware performance.","intents":["Reduce unnecessary inference requests during fast typing","Improve IDE responsiveness by delaying completion until typing pauses","Tune completion latency based on personal typing speed and hardware","Reduce computational load on shared or resource-constrained machines"],"best_for":["developers on slower hardware or shared GPU resources","teams optimizing for IDE responsiveness and reduced latency","developers with fast typing speeds who want to avoid excessive completions"],"limitations":["Delay is global; no per-language or per-context trigger delay customization","No adaptive delay based on inference latency; users must manually tune the delay value","Unclear what the default delay value is or what range is recommended","No metrics on how trigger delay affects completion quality or user satisfaction"],"requires":["VS Code settings panel access"],"input_types":["trigger delay value (milliseconds, default unknown)"],"output_types":["debounced completion requests to Ollama"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ex3ndr-llama-coder__cap_8","uri":"capability://automation.workflow.automatic.model.download.and.management.with.quantization.selection","name":"automatic model download and management with quantization selection","description":"Automatically downloads CodeLlama models from Ollama's model registry (or prompts the user to download) if the selected model is not already present on the system. The extension guides users through quantization selection (q4, q6_K, fp16) based on available hardware, with documentation recommending q4 as the optimal balance between quality and performance. Users can pause downloads (added in v0.0.11) and switch models at runtime without restarting the extension, with the extension managing model lifecycle and storage.","intents":["Automatically set up models without manual Ollama CLI commands","Choose the right model quantization for available hardware","Switch between models at runtime for different coding tasks","Pause long downloads and resume later without losing progress"],"best_for":["developers new to local LLM inference who want frictionless setup","teams managing multiple model variants for different hardware configurations","developers experimenting with different model sizes and quantizations"],"limitations":["Model selection guidance is generic ('pick the biggest model and quantization'); no automatic hardware detection or recommendation engine","Download progress and pause/resume functionality are undocumented; unclear if pause state persists across extension restarts","No model versioning or update management; unclear how to upgrade to newer CodeLlama versions","Model storage location is not configurable; unclear where downloaded models are stored or how to manage disk space","No built-in model validation or integrity checking; unclear if corrupted downloads are detected"],"requires":["Ollama runtime with model registry access","Sufficient disk space for selected model (3GB-32GB depending on quantization)","Network connectivity to download models from Ollama registry"],"input_types":["model selection (e.g., codellama:7b-code-q4_K_M)","quantization preference (q4, q6_K, fp16)"],"output_types":["downloaded and cached models in Ollama"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"vscode-ex3ndr-llama-coder__cap_9","uri":"capability://safety.moderation.zero.telemetry.local.first.architecture.with.no.external.api.calls","name":"zero-telemetry local-first architecture with no external api calls","description":"Explicitly implements a no-telemetry, local-first architecture where all inference runs locally or on a configured remote machine, with no data transmission to external cloud services or GitHub. The extension does not collect usage metrics, error logs, or code samples; all processing stays within the developer's control. This is a fundamental architectural choice that differentiates Llama Coder from GitHub Copilot, which sends code context to GitHub's servers for inference and telemetry.","intents":["Use code completion without sending code to GitHub or cloud services","Maintain code privacy and IP protection for proprietary projects","Comply with data residency and privacy regulations (GDPR, HIPAA, etc.)","Avoid GitHub Copilot's data collection and telemetry"],"best_for":["enterprises with strict data privacy and IP protection requirements","teams working on proprietary or regulated code (healthcare, finance, government)","developers in jurisdictions with strict data residency laws","organizations that distrust cloud-based AI services"],"limitations":["No usage analytics or error reporting; developers cannot see completion quality metrics or usage patterns","No telemetry means no automatic bug reporting; issues must be manually reported to the extension maintainers","No cloud backup or sync of settings; configuration is local to each machine","No usage-based model improvements; the extension cannot learn from aggregate user behavior to improve suggestions"],"requires":["Local Ollama runtime or self-hosted remote Ollama server","No external API keys or cloud service accounts required"],"input_types":["source code (stays local)"],"output_types":["code suggestions (generated locally)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Visual Studio Code (minimum version not specified in documentation)","Ollama runtime installed and running (version compatibility unknown)","16GB RAM minimum; 5GB+ VRAM for smallest models (stable-code:3b), up to 32GB for largest (codellama:34b-q6_K)","One of: Apple Silicon Mac (M1/M2/M3+), NVIDIA GPU with CUDA support, or CPU-only inference (significantly slower)","Visual Studio Code with file extension recognition","Ollama runtime with CodeLlama model (trained on 80+ programming languages)","Knowledge of available GPU model and VRAM capacity","Ollama runtime supporting multiple quantization formats","VS Code settings panel access","Ollama runtime supporting temperature and top-p parameters"],"failure_modes":["Inference latency varies 500ms-5s per completion depending on model size and hardware; no built-in latency metrics or performance monitoring","Context window size is undocumented — unclear how much surrounding code is analyzed for suggestions, potentially limiting multi-file awareness","Requires 16GB+ RAM minimum and 3-32GB VRAM depending on model selection; consumer GPUs and older NVIDIA cards (pre-30xx) experience significant slowdown","No built-in project structure analysis — cannot leverage type information, imports, or dependency graphs for context-aware suggestions","Model selection is manual; no automatic hardware detection or recommendation engine to guide users toward optimal model-hardware pairing","Remote inference adds network latency and requires manual Ollama server setup with `OLLAMA_HOST=0.0.0.0` environment variable configuration","Specific list of supported languages is not documented; unclear which languages receive optimal training coverage vs. degraded performance","Language detection relies on file extension and syntax heuristics; ambiguous file types (e.g., `.txt`, `.config`) may not be detected correctly","No language-specific context awareness — cannot leverage language-specific type systems, package managers, or build configurations for smarter suggestions","Human language support is undocumented; unclear if multilingual documentation generation is equally effective across languages","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.57,"quality":0.32,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:34.118Z","last_scraped_at":"2026-05-03T15:20:33.198Z","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-coder","compare_url":"https://unfragile.ai/compare?artifact=llama-coder"}},"signature":"RGWoBrZEjckY8mgM+QhjsFrMZpCRiIl+VQMQSao6CAI5KhbgUSCchqwnKoZUO5FGyxmMAVy+ha2khPuBHUMDAg==","signedAt":"2026-06-22T21:13:22.208Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/llama-coder","artifact":"https://unfragile.ai/llama-coder","verify":"https://unfragile.ai/api/v1/verify?slug=llama-coder","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"}}