{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ollama-codellama","slug":"codellama","name":"CodeLlama (7B, 13B, 34B, 70B)","type":"model","url":"https://ollama.com/library/codellama","page_url":"https://unfragile.ai/codellama","categories":["code-editors"],"tags":["ollama","open-source","meta","code"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ollama-codellama__cap_0","uri":"capability://code.generation.editing.multi.size.code.generation.with.parameter.tuned.inference","name":"multi-size code generation with parameter-tuned inference","description":"Generates code from natural language prompts using Transformer-based architecture with four parameter variants (7B, 13B, 34B, 70B) allowing trade-offs between inference speed and code quality. Each variant is independently optimized for different hardware constraints and latency requirements, with the 7B model targeting edge devices and 70B targeting maximum code understanding. Inference is performed via Ollama's local execution engine or cloud API, with streaming token output for real-time code generation.","intents":["Generate a function implementation from a natural language specification","Choose between faster inference (7B) vs higher quality code (70B) based on hardware constraints","Stream code generation results to a user interface in real-time","Run code generation locally without sending code to external servers"],"best_for":["developers building local-first code generation tools","teams with strict data privacy requirements","resource-constrained environments (edge devices, embedded systems)"],"limitations":["70B variant has severely reduced 2K token context window vs 16K for smaller variants, limiting ability to generate code for large functions or maintain conversation history","No published benchmark scores (HumanEval, MBPP) — actual code quality vs GPT-4 or Claude unknown","Model trained 2+ years ago — may not understand recent language features, frameworks, or libraries released after training cutoff","Inference speed and hardware requirements not documented — latency depends entirely on user's hardware or cloud tier selection"],"requires":["Ollama runtime (any version supporting CodeLlama)","For local: 3.8GB+ VRAM for 7B variant, 39GB+ for 70B variant (exact requirements undocumented)","For cloud: Ollama cloud account with Free/Pro/Max tier subscription","Python 3.7+ or Node.js 14+ for SDK usage"],"input_types":["natural language prompts (e.g., 'write a function that sorts an array')","code snippets with context","multi-turn conversation history"],"output_types":["generated code (Python, JavaScript, C++, Java, PHP, C#, Bash, etc.)","streaming token sequences","raw text output"],"categories":["code-generation-editing","language-models"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-codellama__cap_1","uri":"capability://code.generation.editing.fill.in.the.middle.code.completion.with.prefix.suffix.context","name":"fill-in-the-middle code completion with prefix-suffix context","description":"Implements bidirectional code infill using a special prompt format (<PRE>{prefix}<SUF>{suffix}<MID>) that allows the model to generate code between two existing code blocks. This capability leverages the model's ability to understand both preceding and following context simultaneously, enabling inline code completion within existing functions or methods. The FIM format is natively supported across all CodeLlama variants and works through standard API endpoints.","intents":["Complete a function body given its signature and surrounding code context","Fill in missing lines within an existing code block without regenerating the entire function","Suggest variable assignments or intermediate steps between two code sections","Enable IDE-like autocomplete that understands full function scope"],"best_for":["IDE plugin developers building inline code completion features","developers integrating CodeLlama into text editors (VS Code, Vim, Neovim)","teams building code review tools that suggest missing implementations"],"limitations":["FIM quality depends on context window size — 70B's 2K token limit severely restricts how much prefix/suffix context can be provided","No documentation on FIM-specific training data or how many tokens were dedicated to FIM vs standard left-to-right generation","Requires manual prompt formatting — no built-in abstraction layer in Ollama SDK, developers must construct <PRE>/<SUF>/<MID> strings themselves","No benchmark data on FIM accuracy vs alternatives (Copilot's FIM, Codex)"],"requires":["Ollama runtime with CodeLlama model loaded","Manual prompt construction with <PRE>, <SUF>, <MID> tokens","Ability to extract prefix and suffix from source code (requires AST parsing or regex for accurate boundaries)"],"input_types":["code prefix (text before cursor)","code suffix (text after cursor)","optional natural language context"],"output_types":["generated code snippet (infilled middle section)","streaming tokens for real-time display"],"categories":["code-generation-editing","code-completion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-codellama__cap_10","uri":"capability://code.generation.editing.code.specific.pretraining.with.llama.2.foundation","name":"code-specific pretraining with llama 2 foundation","description":"Builds on Llama 2's general-purpose Transformer architecture and applies code-specific pretraining to specialize the model for code understanding and generation. The exact composition of code-specific training data is undocumented, but the model learns code syntax, semantics, and common patterns from large-scale code repositories. The code-specialized weights are then fine-tuned into separate variants (base, instruct, python) for different use cases.","intents":["Generate code with better syntax accuracy and semantic understanding than general-purpose LLMs","Leverage code-specific patterns learned during pretraining without explicit prompt engineering","Use a model optimized for code tasks without paying for general-purpose model capabilities"],"best_for":["developers building code-specific applications where general-purpose models are overkill","teams with code-heavy workloads that benefit from specialized model optimization"],"limitations":["Code-specific training data composition unknown — unclear what percentage of pretraining was code vs general text","No ablation studies or comparative analysis showing code-specific pretraining benefit vs base Llama 2","Training data cutoff 2+ years old — model may not understand recent code patterns, frameworks, or language features","No documentation of code-specific architectural modifications — unclear if using standard Transformer or specialized code-aware attention mechanisms"],"requires":["Ollama runtime with CodeLlama model"],"input_types":["code prompts","natural language code instructions"],"output_types":["generated code","code explanations"],"categories":["code-generation-editing","domain-adaptation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-codellama__cap_2","uri":"capability://text.generation.language.instruction.tuned.code.discussion.and.explanation","name":"instruction-tuned code discussion and explanation","description":"Provides a specialized `-instruct` variant fine-tuned on instruction-following data to enable natural language discussion about code, answering programming questions, and explaining code behavior. This variant is optimized for chat-style interactions rather than raw code generation, using instruction-tuning techniques to align model outputs with helpful, safe responses. Accessed via the `/api/chat` endpoint with multi-turn conversation support.","intents":["Ask the model to explain what a code snippet does","Get debugging help by describing a problem and showing relevant code","Have a multi-turn conversation about programming concepts","Request code review feedback or suggestions for improvement"],"best_for":["developers building chatbot interfaces for code assistance","educational tools teaching programming concepts","code review automation systems that need to explain suggestions"],"limitations":["Instruction-tuning data composition unknown — unclear what percentage of training was code-specific vs general instruction-following","No safety benchmarks or alignment metrics published — 'helpful, safe' claims unverified","Multi-turn conversation quality degrades with context length due to 2K-16K token limits depending on variant","No documented guardrails against generating insecure code patterns or harmful instructions"],"requires":["Ollama runtime with codellama:instruct variant","Chat API endpoint (`/api/chat`)","Message history management on client side for multi-turn conversations"],"input_types":["natural language questions about code","code snippets with context","multi-turn conversation messages with role (user/assistant)"],"output_types":["natural language explanations","code suggestions with commentary","streaming text responses"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-codellama__cap_3","uri":"capability://code.generation.editing.python.specialized.code.generation.with.100b.token.domain.adaptation","name":"python-specialized code generation with 100b token domain adaptation","description":"Provides a `codellama:python` variant fine-tuned on 100 billion tokens of Python-specific code, enabling superior Python code generation compared to the base model. This domain-adapted variant uses continued pretraining on Python code repositories to specialize the model's weights for Python syntax, idioms, and common patterns. The specialization improves both code quality and inference efficiency for Python-only use cases.","intents":["Generate Python functions with higher accuracy than base CodeLlama","Leverage Python-specific idioms and best practices in generated code","Build Python-focused development tools without supporting other languages","Reduce inference latency for Python tasks by using a smaller, specialized model"],"best_for":["Python-only development teams and tools","data science and ML teams building code generation for data pipelines","educational platforms teaching Python programming"],"limitations":["Specialized only for Python — cannot generate code in other languages effectively","100B token training data composition unknown — unclear if it includes modern Python frameworks (FastAPI, Pydantic v2, async patterns)","No comparative benchmarks showing Python-specific variant vs base model on Python tasks","Training data cutoff 2+ years old — may not understand recent Python features (match statements, type hints improvements, etc.)"],"requires":["Ollama runtime with codellama:python variant","Python 3.7+ for generated code compatibility (model may generate older syntax)"],"input_types":["natural language prompts for Python code","Python code snippets with context","docstrings and type hints"],"output_types":["Python code (functions, classes, scripts)","streaming tokens"],"categories":["code-generation-editing","domain-adaptation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-codellama__cap_4","uri":"capability://code.generation.editing.local.first.inference.with.ollama.runtime.and.quantization","name":"local-first inference with ollama runtime and quantization","description":"Executes CodeLlama models entirely on user hardware via Ollama's quantized GGUF format, eliminating cloud API calls and enabling offline code generation. The Ollama runtime handles model loading, quantization (format unspecified but typically 4-bit or 8-bit), memory management, and inference optimization. Models are downloaded once and cached locally, with inference latency determined by local hardware rather than network round-trips or cloud queue times.","intents":["Run code generation without sending code to external servers (data privacy)","Generate code offline without internet connectivity","Avoid API rate limits and cloud service costs for high-volume code generation","Integrate code generation into CI/CD pipelines without external dependencies"],"best_for":["enterprises with strict data privacy/compliance requirements","developers in regions with poor internet connectivity","teams building high-volume code generation (refactoring, linting, testing)","offline development environments (aircraft, submarines, remote locations)"],"limitations":["Hardware requirements not documented — developers must estimate VRAM/RAM based on model size (7B ≈ 3.8GB, 70B ≈ 39GB) with unknown overhead","Inference speed completely dependent on user hardware — no SLA or performance guarantees; a 7B model on CPU will be orders of magnitude slower than on GPU","Quantization method and quality loss unknown — Ollama documentation does not specify if using 4-bit, 8-bit, or other quantization schemes","No built-in model versioning or update mechanism — users must manually re-download models when updates are released","Single-machine deployment only — no distributed inference or model sharding across multiple GPUs documented"],"requires":["Ollama runtime (any recent version)","Sufficient local storage: 3.8GB for 7B, 7.4GB for 13B, 19GB for 34B, 39GB for 70B","GPU with sufficient VRAM (exact requirements undocumented) or CPU with sufficient RAM","Linux, macOS, or Windows with Ollama support"],"input_types":["text prompts via CLI or REST API","code snippets","chat messages"],"output_types":["generated code","streaming tokens","JSON responses (via REST API)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-codellama__cap_5","uri":"capability://tool.use.integration.rest.api.and.sdk.based.model.access.with.streaming.support","name":"rest api and sdk-based model access with streaming support","description":"Exposes CodeLlama inference through standardized REST API endpoints (`/api/generate` for text generation, `/api/chat` for conversation) and official SDKs (Python `ollama` library, JavaScript/TypeScript `ollama` library) with streaming token support. The API abstracts away model loading and quantization details, allowing developers to integrate code generation without understanding Ollama internals. Streaming responses enable real-time token-by-token output for UI responsiveness.","intents":["Integrate CodeLlama into a web application via REST API without installing Ollama locally","Build IDE plugins that call CodeLlama via HTTP without managing model lifecycle","Stream code generation results to a frontend for real-time display","Use Python or JavaScript SDKs for simplified integration vs raw HTTP calls"],"best_for":["web application developers building code generation features","IDE plugin developers (VS Code, JetBrains, Vim)","teams deploying CodeLlama on shared infrastructure (single server, multiple clients)"],"limitations":["REST API is HTTP-only — no WebSocket support documented, limiting real-time bidirectional communication","Streaming implementation details unknown — unclear if using Server-Sent Events (SSE), chunked transfer encoding, or newline-delimited JSON","No authentication or rate-limiting built into Ollama — developers must implement their own API gateway for multi-user deployments","SDK documentation minimal — no examples for error handling, timeout configuration, or streaming consumption patterns","Cloud deployment via Ollama cloud has usage metering by GPU time (not tokens) with session/weekly limits — unpredictable costs for variable-length generations"],"requires":["Ollama runtime running on localhost:11434 (default) or configured remote host","Python 3.7+ for Python SDK, Node.js 14+ for JavaScript SDK","HTTP client library (requests, fetch, curl, etc.)","For cloud: Ollama cloud account with Free/Pro/Max tier"],"input_types":["JSON request bodies with model name, prompt, and optional parameters","streaming request bodies for long-running generations"],"output_types":["JSON responses with generated text","streaming newline-delimited JSON (NDJSON) for token-by-token output","HTTP status codes for error handling"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-codellama__cap_6","uri":"capability://code.generation.editing.multi.language.code.generation.with.language.agnostic.architecture","name":"multi-language code generation with language-agnostic architecture","description":"Generates code across multiple programming languages (Python, C++, Java, PHP, TypeScript/JavaScript, C#, Bash, and others) using a single unified Transformer model trained on polyglot code data. The model learns language-agnostic code patterns and syntax rules during pretraining, enabling it to switch between languages based on prompt context without separate language-specific models (except the Python variant). Language selection is implicit in the prompt — developers specify the target language in natural language instructions.","intents":["Generate code in any supported language from a single model without language selection logic","Build polyglot development tools that support multiple languages","Translate code between languages by prompting for target language","Generate code for unfamiliar languages by providing examples in the prompt"],"best_for":["full-stack development teams using multiple languages","polyglot code generation tools and IDEs","teams building language-agnostic refactoring or linting tools"],"limitations":["Exact language support list unknown — documentation states 'many of the most popular programming languages' without enumeration","No per-language quality metrics — unclear if C++ code quality equals Python quality or if some languages are undertrained","Language detection from prompt is implicit — no explicit language tagging in API, relying on prompt clarity","Polyglot training may dilute language-specific performance vs dedicated language models","No support for domain-specific languages (SQL, GraphQL, Terraform, etc.) documented"],"requires":["Ollama runtime with CodeLlama model","Clear language specification in natural language prompt (e.g., 'write a JavaScript function')"],"input_types":["natural language prompts with explicit language name","code snippets in any supported language","language-tagged prompts (optional)"],"output_types":["code in requested language","syntax-highlighted output (if client-side rendering)"],"categories":["code-generation-editing","multi-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-codellama__cap_7","uri":"capability://code.generation.editing.context.aware.code.generation.with.16k.token.context.window.7b.13b.34b.variants","name":"context-aware code generation with 16k token context window (7b/13b/34b variants)","description":"Maintains up to 16,000 token context window for the 7B, 13B, and 34B variants, enabling the model to condition code generation on substantial surrounding code, documentation, and conversation history. The context window allows developers to provide full function signatures, class definitions, imports, and multi-turn conversation history, improving code relevance and consistency. Context is managed by the client — developers must construct prompts that fit within the token limit.","intents":["Generate code that respects existing function signatures and class structures in a codebase","Maintain conversation history across multiple code generation requests","Provide full file context (imports, class definitions) to improve code consistency","Generate code that follows established patterns from surrounding code examples"],"best_for":["developers building context-aware code completion within IDEs","teams using CodeLlama for multi-turn code review or refactoring workflows","systems that need to maintain conversation history across multiple generations"],"limitations":["16K token limit is insufficient for large codebases — a typical Python file with 500 lines of code consumes ~2000 tokens, leaving only 14K for context and generation","70B variant has only 2K token context — severe limitation making it unsuitable for any context-aware use case","Token counting not exposed in API — developers must manually estimate token usage or use external tokenizers","No automatic context pruning or summarization — developers must manually select relevant context to fit within limit","Context window size not configurable — fixed at 16K for smaller variants"],"requires":["Ollama runtime with 7B, 13B, or 34B variant (not 70B)","Token counting mechanism (external library or manual estimation)","Prompt construction logic that respects token limits"],"input_types":["code context (surrounding functions, classes, imports)","conversation history (previous prompts and responses)","natural language instructions"],"output_types":["generated code conditioned on context","contextually-aware completions"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-codellama__cap_8","uri":"capability://automation.workflow.cloud.based.inference.with.usage.based.pricing.and.concurrency.limits","name":"cloud-based inference with usage-based pricing and concurrency limits","description":"Executes CodeLlama on Ollama's cloud infrastructure with usage-based pricing metered by GPU time (not token count) and configurable concurrency limits. Three pricing tiers (Free: 1 concurrent model, Pro: 3 concurrent models at $20/mo, Max: 10 concurrent models at $100/mo) control how many simultaneous inference requests are allowed. Usage is tracked per session (5-hour reset) and per week (7-day reset), with requests exceeding concurrency limits queued or rejected.","intents":["Run CodeLlama without managing local hardware or Ollama installation","Scale code generation across multiple concurrent users without infrastructure setup","Pay only for actual GPU time used rather than fixed monthly costs","Prototype code generation features without upfront hardware investment"],"best_for":["startups and small teams prototyping code generation features","variable-load applications with unpredictable code generation demand","developers without GPU hardware or expertise to optimize local inference"],"limitations":["Usage metering by GPU time (not tokens) — unpredictable costs for variable-length generations; a 10-token generation and 1000-token generation may have similar GPU time cost","Session limits reset every 5 hours and weekly limits reset every 7 days — unclear how limits are enforced or what happens when limits are exceeded","Concurrency limits are hard caps — requests exceeding limits are queued or rejected with no automatic scaling","No published SLA or uptime guarantees — cloud infrastructure reliability unknown","Pricing is per-tier subscription, not per-request — developers pay for unused concurrency slots","No cost visibility or usage dashboard documented — developers cannot predict monthly bills"],"requires":["Ollama cloud account (free tier available)","API key for authentication (if required)","Internet connectivity for cloud API calls"],"input_types":["same as local REST API (JSON requests with prompts)"],"output_types":["same as local REST API (JSON responses, streaming NDJSON)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ollama-codellama__cap_9","uri":"capability://automation.workflow.cli.based.model.execution.and.management","name":"cli-based model execution and management","description":"Provides command-line interface for downloading, running, and managing CodeLlama models via `ollama` command (e.g., `ollama run codellama`, `ollama pull codellama:70b`). The CLI abstracts model downloading, quantization, and inference, allowing developers to run code generation from the terminal without writing code. Models are cached locally after first download, and the CLI manages model lifecycle (loading, unloading, memory management).","intents":["Quickly test CodeLlama code generation from the command line without writing integration code","Download and manage multiple CodeLlama variants locally","Integrate CodeLlama into shell scripts and CI/CD pipelines","Prototype code generation features before building full applications"],"best_for":["developers experimenting with CodeLlama before committing to integration","DevOps engineers building code generation into CI/CD pipelines","system administrators managing CodeLlama deployments across teams"],"limitations":["CLI is interactive only — no batch processing or scripting support documented","No output formatting options — responses are plain text without structured output (JSON, XML, etc.)","Model selection is implicit in command name — no explicit model parameter in CLI","No streaming support in CLI — full response must be generated before display","Error handling and exit codes not documented — difficult to integrate into error-handling shell scripts"],"requires":["Ollama runtime installed and running","Shell/terminal access (bash, zsh, PowerShell, cmd.exe)","Model downloaded via `ollama pull` before running"],"input_types":["command-line arguments (prompts)","stdin piping (for multi-line prompts)"],"output_types":["plain text responses printed to stdout","error messages to stderr"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Ollama runtime (any version supporting CodeLlama)","For local: 3.8GB+ VRAM for 7B variant, 39GB+ for 70B variant (exact requirements undocumented)","For cloud: Ollama cloud account with Free/Pro/Max tier subscription","Python 3.7+ or Node.js 14+ for SDK usage","Ollama runtime with CodeLlama model loaded","Manual prompt construction with <PRE>, <SUF>, <MID> tokens","Ability to extract prefix and suffix from source code (requires AST parsing or regex for accurate boundaries)","Ollama runtime with CodeLlama model","Ollama runtime with codellama:instruct variant","Chat API endpoint (`/api/chat`)"],"failure_modes":["70B variant has severely reduced 2K token context window vs 16K for smaller variants, limiting ability to generate code for large functions or maintain conversation history","No published benchmark scores (HumanEval, MBPP) — actual code quality vs GPT-4 or Claude unknown","Model trained 2+ years ago — may not understand recent language features, frameworks, or libraries released after training cutoff","Inference speed and hardware requirements not documented — latency depends entirely on user's hardware or cloud tier selection","FIM quality depends on context window size — 70B's 2K token limit severely restricts how much prefix/suffix context can be provided","No documentation on FIM-specific training data or how many tokens were dedicated to FIM vs standard left-to-right generation","Requires manual prompt formatting — no built-in abstraction layer in Ollama SDK, developers must construct <PRE>/<SUF>/<MID> strings themselves","No benchmark data on FIM accuracy vs alternatives (Copilot's FIM, Codex)","Code-specific training data composition unknown — unclear what percentage of pretraining was code vs general text","No ablation studies or comparative analysis showing code-specific pretraining benefit vs base Llama 2","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.32,"ecosystem":0.42,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:24.483Z","last_scraped_at":"2026-05-03T15:20:48.403Z","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=codellama","compare_url":"https://unfragile.ai/compare?artifact=codellama"}},"signature":"wqZsq4RQn7JGY8Fl3+TNXqFjNwkEgerINbdMaQ7FzfU70dnZUqpgJKjejO5Wgyvh4N1SMcGVu4YbJQYpyFmuCQ==","signedAt":"2026-06-21T14:19:25.329Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/codellama","artifact":"https://unfragile.ai/codellama","verify":"https://unfragile.ai/api/v1/verify?slug=codellama","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"}}