{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"codellama-70b","slug":"codellama-70b","name":"CodeLlama 70B","type":"model","url":"https://ai.meta.com/blog/code-llama-large-language-model-coding/","page_url":"https://unfragile.ai/codellama-70b","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"codellama-70b__cap_0","uri":"capability://code.generation.editing.multi.language.code.generation.from.natural.language.prompts","name":"multi-language code generation from natural language prompts","description":"Generates syntactically correct, functional code across 15+ programming languages (Python, C++, Java, PHP, TypeScript, C#, Bash, etc.) from natural language descriptions. Uses a transformer-based decoder architecture trained on 1 trillion tokens of code data, enabling the model to learn language-specific idioms, standard library patterns, and common implementation approaches. The 100K context window allows the model to reference existing codebases and generate contextually appropriate solutions that align with project conventions.","intents":["Generate a function that implements a specific algorithm without writing it manually","Quickly scaffold boilerplate code for a new feature across multiple languages","Translate a code concept from one language to another","Generate code that integrates with existing project patterns and libraries"],"best_for":["Solo developers building prototypes across multiple languages","Teams needing rapid code scaffolding for polyglot systems","Developers learning new programming languages by example"],"limitations":["No explicit output length constraints documented; may generate incomplete or truncated code for complex multi-file solutions","Quality degrades on domain-specific or proprietary libraries not well-represented in training data","No built-in validation that generated code is syntactically correct or executable without testing","Context window trained on 16K tokens; extrapolation to 100K may degrade code quality at upper bounds"],"requires":["Model weights (70B parameter checkpoint, ~140GB disk space for fp16)","GPU with sufficient VRAM for 70B inference (specific requirements unknown; estimate 40-80GB for full precision)","Inference framework (vLLM, llama.cpp, Ollama, or similar)","Natural language prompt describing desired code behavior"],"input_types":["text (natural language description)","code (existing code snippets for context)"],"output_types":["code (generated source code in target language)"],"categories":["code-generation-editing","multi-language-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_1","uri":"capability://code.generation.editing.fill.in.the.middle.code.completion","name":"fill-in-the-middle code completion","description":"Completes code by predicting missing tokens in the middle of a code snippet, enabling inline code completion workflows where developers write code before and after a gap. Uses a bidirectional attention mechanism trained on code infilling tasks, allowing the model to condition on both prefix (code before the gap) and suffix (code after the gap) context. This approach is more accurate than left-to-right completion alone because it can infer intent from downstream code.","intents":["Auto-complete a function body given the function signature and return statement","Fill in missing loop bodies or conditional branches in existing code","Complete variable assignments or expressions in the middle of a statement","Suggest implementation for a method stub with known inputs and outputs"],"best_for":["IDE plugin developers building real-time code completion features","Developers using editor integrations (VS Code, Vim, Neovim)","Teams with custom editor tooling requiring local inference"],"limitations":["Fill-in-the-middle capability is NOT available on the 70B base model — only confirmed for 7B and 13B variants; 70B users must use left-to-right completion only","Requires bidirectional context (both prefix and suffix) to function; cannot operate in pure streaming/left-to-right mode","No latency benchmarks provided; FIM inference may be slower than standard left-to-right completion due to bidirectional processing","Accuracy depends on suffix code being semantically related to the gap; misleading or unrelated suffix code degrades predictions"],"requires":["CodeLlama 7B or 13B model (base or instruct variant only; NOT 70B)","Inference framework with FIM support (vLLM, llama.cpp with FIM patches, or similar)","Code editor or IDE with plugin support for bidirectional context submission","Both prefix and suffix code context available before inference"],"input_types":["code (prefix code before gap)","code (suffix code after gap)"],"output_types":["code (generated tokens to fill the gap)"],"categories":["code-generation-editing","code-completion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_10","uri":"capability://code.generation.editing.inference.framework.flexibility.and.ecosystem.integration","name":"inference framework flexibility and ecosystem integration","description":"Compatible with multiple inference frameworks (vLLM, llama.cpp, Ollama, LM Studio, etc.), enabling flexible deployment options and ecosystem integration. The model uses standard transformer architecture and can be exported to multiple formats (GGUF, safetensors, etc.), allowing developers to choose the inference framework that best fits their performance, latency, and resource requirements.","intents":["Deploy CodeLlama using the inference framework that best fits performance requirements","Integrate CodeLlama into existing ML infrastructure and tooling","Optimize inference latency and throughput for specific hardware","Use CodeLlama with quantization or optimization techniques specific to chosen framework"],"best_for":["Teams with existing ML infrastructure and inference framework preferences","Developers optimizing for specific hardware (GPUs, TPUs, CPUs)","Organizations needing to integrate code generation into complex ML pipelines"],"limitations":["Framework compatibility and optimization vary; not all frameworks provide equal performance or feature support","Quantization options and formats not documented; unclear which frameworks support which quantization schemes","No guidance on framework selection or performance comparison; developers must evaluate frameworks independently","Framework-specific bugs or limitations may affect CodeLlama deployment; no official support for framework-specific issues"],"requires":["Choice of inference framework (vLLM, llama.cpp, Ollama, LM Studio, or similar)","Framework-specific dependencies and configuration","Model weights in format compatible with chosen framework"],"input_types":["model weights (in framework-compatible format)"],"output_types":["inference service (framework-specific deployment)"],"categories":["code-generation-editing","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_11","uri":"capability://code.generation.editing.quantization.and.model.compression.support","name":"quantization and model compression support","description":"Model weights can be quantized to lower precision formats (int8, int4, GGUF, etc.) to reduce memory requirements and inference latency, enabling deployment on resource-constrained hardware. Quantization trades off model quality for reduced computational requirements, allowing smaller GPUs or CPUs to run the model. Multiple quantization schemes are supported through different inference frameworks.","intents":["Deploy CodeLlama on GPUs with limited VRAM (e.g., consumer GPUs with 8-16GB)","Reduce inference latency through quantization-based optimization","Run CodeLlama on CPU-only hardware for cost-sensitive deployments","Reduce model storage requirements for edge deployment or bandwidth-constrained environments"],"best_for":["Teams with limited GPU resources or budget constraints","Edge deployment scenarios requiring minimal resource footprint","Developers optimizing for inference latency over model quality"],"limitations":["Quantization options and quality tradeoffs not documented; unclear which quantization schemes are supported or recommended","No benchmarks comparing quantized vs. full-precision performance; unclear how much quality is lost at different quantization levels","Quantization quality varies by framework and scheme; no guidance on selecting appropriate quantization for specific use cases","Quantized models may produce lower-quality code or fail on complex tasks; no quality metrics provided"],"requires":["Quantization tool compatible with CodeLlama (framework-specific)","Original model weights in full precision","Understanding of quantization tradeoffs and quality impact"],"input_types":["model weights (full precision)"],"output_types":["quantized model weights (reduced precision)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_12","uri":"capability://code.generation.editing.commercial.use.licensing.and.legal.compliance","name":"commercial-use licensing and legal compliance","description":"Distributed under the Llama 2 community license, which explicitly permits free commercial use without licensing fees, royalties, or usage restrictions. The license provides legal clarity for organizations using CodeLlama in production systems or commercial products. This is a significant advantage over proprietary models that require commercial licenses or prohibit commercial use.","intents":["Use CodeLlama in commercial products or services without licensing fees","Integrate CodeLlama into proprietary code generation tools","Deploy CodeLlama in commercial SaaS platforms","Ensure legal compliance for commercial code generation use cases"],"best_for":["Commercial organizations building code generation products or services","Startups and small teams avoiding licensing costs","Enterprises with legal/compliance requirements for open-source software"],"limitations":["Llama 2 license terms not fully detailed in source material; reference to full license text required for complete legal review","License may have restrictions or obligations not documented in source material (e.g., attribution requirements, derivative work restrictions)","No legal guidance provided; organizations should consult legal counsel before commercial deployment","License may change in future versions; no guarantee of perpetual commercial use rights"],"requires":["Review of full Llama 2 community license terms","Legal review by organization's legal counsel","Compliance with any license obligations (attribution, etc.)"],"input_types":["license terms (Llama 2 community license)"],"output_types":["legal compliance assessment (organization-specific)"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_13","uri":"capability://code.generation.editing.api.and.library.integration.code.generation","name":"api and library integration code generation","description":"Generates code that integrates with external APIs and libraries by understanding API documentation patterns and common usage examples. The model learns API patterns from training data and generates correct, idiomatic code for API calls, error handling, and data transformation. Supports popular libraries and frameworks (Django, Flask, NumPy, Pandas, requests, etc.) with proper error handling and best practices.","intents":["Generate code to call external APIs (REST, GraphQL, etc.) with proper authentication and error handling","Create code using popular libraries and frameworks without consulting documentation","Learn how to use a library by examining generated examples","Integrate multiple APIs or libraries in a single code snippet"],"best_for":["Developers integrating third-party services and APIs","Teams building microservices and distributed systems","Data engineers building ETL pipelines with multiple data sources","Developers learning new libraries and frameworks"],"limitations":["Generated code may use outdated API versions or deprecated methods if training data is stale","Cannot generate code for proprietary or internal APIs without examples in training data","May miss API-specific requirements (authentication tokens, rate limiting, pagination) without explicit guidance","Error handling may be incomplete or not match production requirements","Requires manual review to ensure API calls are correct and secure"],"requires":["API or library name and desired functionality","Optionally: API documentation or examples","CodeLlama model","Knowledge of API authentication and error handling requirements"],"input_types":["text (API or library name and desired functionality)","code (partial code or examples)"],"output_types":["code (API integration code with error handling)"],"categories":["code-generation-editing","integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_14","uri":"capability://code.generation.editing.codebase.refactoring.and.modernization","name":"codebase refactoring and modernization","description":"Suggests and generates refactored code to improve structure, readability, and maintainability while preserving functionality. The model learns refactoring patterns (extract method, rename variable, consolidate conditionals, etc.) from training data and applies them to modernize legacy code. Analyzes code to identify refactoring opportunities and generates improved versions with explanations.","intents":["Modernize legacy code by applying contemporary patterns and idioms","Refactor code to improve readability and maintainability","Extract common patterns into reusable functions or classes","Migrate code from deprecated patterns to current best practices"],"best_for":["Teams maintaining large legacy codebases","Developers learning refactoring techniques","Projects modernizing to newer language versions or frameworks","Code review tools suggesting refactoring opportunities"],"limitations":["Refactoring suggestions may not preserve all original behavior — requires careful testing","May suggest over-engineered refactorings for simple code","Cannot refactor code that relies on undocumented behavior or side effects","Requires domain knowledge to validate that refactored code meets business requirements","May introduce performance regressions if refactoring changes algorithmic complexity"],"requires":["Source code to refactor","CodeLlama model (Instruct variant recommended for explanations)","Comprehensive test suite to validate refactored code","Manual review and testing"],"input_types":["code (source code to refactor)"],"output_types":["code (refactored code)","text (explanation of refactoring changes and benefits)"],"categories":["code-generation-editing","refactoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_2","uri":"capability://code.generation.editing.python.specialized.code.generation","name":"python-specialized code generation","description":"A variant of CodeLlama 70B fine-tuned specifically on Python code, optimized for generating idiomatic Python solutions with strong understanding of Python standard library, popular frameworks (Django, FastAPI, NumPy, Pandas), and Python-specific patterns (list comprehensions, decorators, context managers). The specialization involves additional training on Python-heavy datasets after the base code pretraining, allowing the model to prioritize Python idioms and best practices.","intents":["Generate Python functions that follow PEP 8 style guidelines and Python idioms","Create data processing pipelines using Pandas, NumPy, or Polars","Scaffold FastAPI or Django applications with proper structure","Generate Python code that integrates with popular ML/data science libraries"],"best_for":["Python-focused teams and data science organizations","Developers building Python-heavy microservices or data pipelines","ML engineers prototyping models and data processing workflows"],"limitations":["Specialization on Python may reduce quality for non-Python languages if used for polyglot projects","No documentation on how Python specialization affects multi-language capability; unclear if model sacrifices cross-language performance","No benchmarks comparing Python variant to base model; unclear if specialization provides measurable improvement","Still trained on 16K token sequences; extrapolation to 100K context may degrade Python-specific understanding at upper bounds"],"requires":["CodeLlama-70B-Python model weights (~140GB disk space for fp16)","GPU with sufficient VRAM for 70B inference (specific requirements unknown; estimate 40-80GB)","Inference framework supporting model loading (vLLM, llama.cpp, Ollama, etc.)","Python 3.7+ environment for generated code execution and testing"],"input_types":["text (natural language description of Python code)","code (existing Python code for context)"],"output_types":["code (Python source code)"],"categories":["code-generation-editing","language-specialization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_3","uri":"capability://code.generation.editing.instruction.following.code.generation","name":"instruction-following code generation","description":"An instruct-tuned variant of CodeLlama 70B fine-tuned on instruction-following datasets, enabling the model to better respond to natural language commands, clarifications, and multi-step coding tasks. Uses supervised fine-tuning on high-quality (instruction, code output) pairs to align the model's behavior with user intent, improving the model's ability to follow specific requirements, constraints, and coding style preferences expressed in natural language.","intents":["Generate code that follows specific style guidelines or architectural patterns described in natural language","Respond to iterative refinement requests ('make this function more efficient', 'add error handling')","Follow complex multi-step instructions for code generation and refactoring","Generate code with specific constraints (e.g., 'use only built-in libraries', 'optimize for memory')"],"best_for":["Interactive code generation workflows where users iteratively refine requirements","Teams using CodeLlama in chat-like interfaces or conversational IDEs","Developers who prefer natural language instructions over code prompts"],"limitations":["Instruction-tuning may reduce raw code generation quality compared to base model on simple, well-defined tasks","No benchmarks comparing instruct variant to base model; unclear if instruction-following improves or degrades code quality overall","Instruction-following quality depends on clarity and specificity of natural language prompts; ambiguous instructions may produce incorrect code","No guidance on prompt engineering best practices for this variant"],"requires":["CodeLlama-70B-Instruct model weights (~140GB disk space for fp16)","GPU with sufficient VRAM for 70B inference (specific requirements unknown; estimate 40-80GB)","Inference framework supporting model loading","Well-structured natural language instructions describing desired code behavior"],"input_types":["text (natural language instructions)","code (existing code for context or refactoring)"],"output_types":["code (generated or refactored source code)"],"categories":["code-generation-editing","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_4","uri":"capability://code.generation.editing.repository.level.code.understanding.with.extended.context","name":"repository-level code understanding with extended context","description":"Leverages a 100K token context window to ingest and understand entire code repositories, enabling the model to generate code that respects project-wide patterns, naming conventions, architectural decisions, and existing implementations. The extended context is achieved through training on longer sequences (up to 100K tokens) and using efficient attention mechanisms, allowing the model to maintain coherence over very long code files or multiple files concatenated together.","intents":["Generate new code that follows the architectural patterns and conventions of an existing codebase","Understand and extend existing large codebases without losing context of project structure","Generate code that integrates seamlessly with existing implementations across multiple files","Analyze and understand the full context of a large module or service before generating modifications"],"best_for":["Teams maintaining large codebases (100K+ lines) with consistent patterns","Developers working on monorepos or multi-file features requiring cross-file consistency","Organizations needing code generation that respects project-specific conventions and architecture"],"limitations":["Context window trained on 16K token sequences; extrapolation to 100K is claimed but not independently verified, and quality degradation at upper bounds is unknown","No latency benchmarks provided; processing 100K tokens may introduce significant inference latency (likely 10-30 seconds per request)","Requires careful context management to fit entire repository into 100K window; larger projects may require selective file inclusion","No guidance on optimal context composition (file ordering, relevance filtering, etc.) for best results","Model may lose coherence or 'forget' earlier context when processing very long sequences near the 100K limit"],"requires":["CodeLlama 70B model weights","GPU with sufficient VRAM for 70B inference with long context support (likely 60-100GB for 100K context)","Inference framework optimized for long context (vLLM with paged attention, or similar)","Repository code or code snippets totaling up to 100K tokens","Context management strategy to select relevant files/modules for inclusion"],"input_types":["code (repository files or concatenated code snippets)","text (natural language instructions for code generation)"],"output_types":["code (generated code respecting repository patterns)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_5","uri":"capability://code.generation.editing.code.understanding.and.natural.language.explanation","name":"code understanding and natural language explanation","description":"Analyzes existing code and generates natural language explanations of what the code does, how it works, and why it's structured a particular way. Uses the same transformer decoder architecture trained on code-to-text pairs, enabling bidirectional understanding between code and natural language. The model can explain code at multiple levels of abstraction (function-level, module-level, algorithm-level) depending on the context provided.","intents":["Generate documentation or comments for existing code without manual writing","Explain complex algorithms or unfamiliar code patterns in natural language","Understand the purpose and behavior of legacy code before refactoring","Generate docstrings or README sections describing code functionality"],"best_for":["Teams documenting legacy codebases or onboarding new developers","Developers learning unfamiliar code patterns or algorithms","Technical writers generating API documentation from code"],"limitations":["Explanation quality depends on code clarity; poorly written or obfuscated code may produce inaccurate explanations","No benchmarks provided for explanation accuracy; unclear how often explanations are incorrect or misleading","Explanations may be verbose or miss subtle implementation details","No ability to explain domain-specific logic without additional context (e.g., business rules embedded in code)"],"requires":["CodeLlama 70B model weights","GPU with sufficient VRAM for 70B inference","Inference framework supporting model loading","Source code to analyze and explain"],"input_types":["code (source code to explain)"],"output_types":["text (natural language explanation)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_6","uri":"capability://code.generation.editing.multi.language.code.translation.and.porting","name":"multi-language code translation and porting","description":"Translates code from one programming language to another while preserving functionality and adapting to target language idioms. Uses the model's understanding of language-agnostic algorithms combined with language-specific idiom knowledge to produce idiomatic code in the target language. The 15+ language support enables translation between any supported language pair (Python to C++, Java to TypeScript, etc.).","intents":["Port a codebase from one language to another (e.g., Python to C++ for performance)","Translate a single function or module to a different language for integration","Learn how an algorithm is implemented differently across languages","Generate equivalent code in a language required by a new platform or framework"],"best_for":["Teams migrating codebases between languages","Polyglot organizations needing code in multiple languages","Developers learning language-specific implementations of algorithms"],"limitations":["Translation quality varies significantly by language pair; some pairs may produce incorrect or non-idiomatic code","No benchmarks provided for translation accuracy; unclear how often translations require manual correction","Language-specific features (e.g., Python decorators, Rust ownership) may not translate cleanly to target languages","Performance characteristics may differ significantly between translations; no guidance on performance validation","Library and framework differences between languages may require manual refactoring after translation"],"requires":["CodeLlama 70B model weights","GPU with sufficient VRAM for 70B inference","Inference framework supporting model loading","Source code in a supported language (Python, C++, Java, PHP, TypeScript, C#, Bash, or 8+ others)"],"input_types":["code (source code in source language)","text (optional: target language specification)"],"output_types":["code (translated code in target language)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_7","uri":"capability://code.generation.editing.code.debugging.and.error.analysis","name":"code debugging and error analysis","description":"Analyzes code with errors or bugs and suggests fixes or improvements. Uses the model's understanding of common programming patterns and error types to identify issues and propose corrections. The model can analyze error messages, stack traces, or code patterns to suggest debugging strategies or fixes.","intents":["Identify bugs in code and suggest fixes","Analyze error messages and suggest root causes","Suggest improvements to error handling or edge case coverage","Recommend refactoring to prevent common bug patterns"],"best_for":["Developers debugging code during development","Teams conducting code reviews focused on correctness","Developers learning common bug patterns and fixes"],"limitations":["No explicit debugging capability documented; inferred from general code understanding capability","Debugging accuracy depends on code clarity and error context; may miss subtle bugs or produce incorrect diagnoses","No benchmarks provided for debugging accuracy; unclear how often suggestions are correct or helpful","Cannot execute code to verify fixes; all suggestions are static analysis only","May suggest fixes that compile but don't address the actual bug"],"requires":["CodeLlama 70B model weights","GPU with sufficient VRAM for 70B inference","Inference framework supporting model loading","Code with errors or error messages/stack traces"],"input_types":["code (buggy source code)","text (error messages or stack traces)"],"output_types":["code (suggested fixes)","text (explanation of bugs and fixes)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_8","uri":"capability://code.generation.editing.benchmark.validated.code.generation.performance","name":"benchmark-validated code generation performance","description":"Achieves measurable performance on standardized code generation benchmarks (HumanEval, MBPP, MultiPL-E), providing quantifiable evidence of code generation quality. The model was evaluated on these benchmarks to demonstrate capability and enable comparison with other models. HumanEval score of 67.8% indicates the model can solve approximately 2 out of 3 programming problems correctly on the first attempt.","intents":["Evaluate whether CodeLlama is suitable for a specific code generation task based on benchmark performance","Compare CodeLlama's capabilities to other models using standardized benchmarks","Understand the expected success rate for code generation tasks","Validate that the model meets minimum quality thresholds for production use"],"best_for":["Teams evaluating CodeLlama for production code generation tasks","Researchers comparing code generation models","Organizations making build-vs-buy decisions for code generation tools"],"limitations":["HumanEval score of 67.8% means ~33% of problems are solved incorrectly; not suitable for mission-critical code without human review","Benchmark scores don't reflect real-world code generation quality; benchmarks may not represent actual use cases","MBPP and MultiPL-E scores not provided in source material; only HumanEval score is documented","No comparison to specific competing models (GPT-3.5, Copilot, etc.); claim of 'outperforming state-of-the-art' is unsubstantiated","Benchmarks measure single-function generation; real-world code generation often requires multi-file, multi-function solutions"],"requires":["Understanding of HumanEval, MBPP, and MultiPL-E benchmark methodologies","Ability to run benchmarks locally or access published benchmark results","Evaluation framework for comparing benchmark scores across models"],"input_types":["benchmark problem descriptions (text)"],"output_types":["code (generated solutions)","numeric scores (pass/fail on benchmark problems)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__cap_9","uri":"capability://code.generation.editing.open.source.model.distribution.and.local.deployment","name":"open-source model distribution and local deployment","description":"Distributed as open-source model weights under the Llama 2 community license, enabling free download, local deployment, and commercial use without API dependencies or usage fees. The model can be deployed on local hardware or private infrastructure, providing data privacy and avoiding cloud API costs. Multiple inference frameworks support CodeLlama (vLLM, llama.cpp, Ollama, etc.), enabling flexible deployment options.","intents":["Deploy code generation locally without sending code to external APIs","Avoid API costs and rate limits associated with cloud-based code generation services","Integrate code generation into private/on-premises infrastructure","Use code generation for proprietary code without sharing with third parties"],"best_for":["Organizations with data privacy requirements or proprietary code","Teams wanting to avoid API costs and dependencies on external services","Developers building custom code generation tools and integrations","Researchers and academics using code generation for non-commercial purposes"],"limitations":["Requires local GPU hardware (40-80GB VRAM estimated for 70B model); not suitable for resource-constrained environments","Inference latency is higher than cloud APIs due to local hardware constraints; no latency benchmarks provided","Requires technical expertise to set up inference framework, manage model weights, and optimize performance","No official support or SLA; users responsible for troubleshooting and optimization","Model weights are large (~140GB for fp16); significant storage and bandwidth requirements for download and updates"],"requires":["GPU with 40-80GB VRAM (estimated; exact requirements unknown)","Inference framework (vLLM, llama.cpp, Ollama, or similar)","~140GB disk space for fp16 model weights","Python 3.9+ or compatible runtime for inference framework","Network bandwidth for downloading model weights (~140GB)"],"input_types":["model weights (downloaded from Meta or community repositories)"],"output_types":["deployed model instance (local inference service)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"codellama-70b__headline","uri":"capability://code.generation.editing.open.source.code.generation.model","name":"open-source code generation model","description":"Meta's CodeLlama 70B is an open-source code generation model fine-tuned on extensive code data, designed for high accuracy in generating and completing code across multiple programming languages.","intents":["best open-source code generation model","code generation model for Python","top AI model for code completion","best model for debugging code","open-source model for multi-language code generation"],"best_for":["developers needing code completion","researchers in AI code generation"],"limitations":[],"requires":[],"input_types":["natural language prompts","code prompts"],"output_types":["generated code","natural language descriptions"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Model weights (70B parameter checkpoint, ~140GB disk space for fp16)","GPU with sufficient VRAM for 70B inference (specific requirements unknown; estimate 40-80GB for full precision)","Inference framework (vLLM, llama.cpp, Ollama, or similar)","Natural language prompt describing desired code behavior","CodeLlama 7B or 13B model (base or instruct variant only; NOT 70B)","Inference framework with FIM support (vLLM, llama.cpp with FIM patches, or similar)","Code editor or IDE with plugin support for bidirectional context submission","Both prefix and suffix code context available before inference","Choice of inference framework (vLLM, llama.cpp, Ollama, LM Studio, or similar)","Framework-specific dependencies and configuration"],"failure_modes":["No explicit output length constraints documented; may generate incomplete or truncated code for complex multi-file solutions","Quality degrades on domain-specific or proprietary libraries not well-represented in training data","No built-in validation that generated code is syntactically correct or executable without testing","Context window trained on 16K tokens; extrapolation to 100K may degrade code quality at upper bounds","Fill-in-the-middle capability is NOT available on the 70B base model — only confirmed for 7B and 13B variants; 70B users must use left-to-right completion only","Requires bidirectional context (both prefix and suffix) to function; cannot operate in pure streaming/left-to-right mode","No latency benchmarks provided; FIM inference may be slower than standard left-to-right completion due to bidirectional processing","Accuracy depends on suffix code being semantically related to the gap; misleading or unrelated suffix code degrades predictions","Framework compatibility and optimization vary; not all frameworks provide equal performance or feature support","Quantization options and formats not documented; unclear which frameworks support which quantization schemes","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.3,"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":"2026-05-24T12:16:21.547Z","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=codellama-70b","compare_url":"https://unfragile.ai/compare?artifact=codellama-70b"}},"signature":"JoyvV+2BvfN5E4td5RVKmWz2mgjFi1d5uRnHMDNZTJgIy5TePjczkiX79P/+N4teRRfrN/Fqkfa2sZlSK1QZCw==","signedAt":"2026-06-20T04:34:20.955Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/codellama-70b","artifact":"https://unfragile.ai/codellama-70b","verify":"https://unfragile.ai/api/v1/verify?slug=codellama-70b","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"}}