{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-code-llama-open-foundation-models-for-code-code-llama","slug":"code-llama-open-foundation-models-for-code-code-llama","name":"Code Llama: Open Foundation Models for Code (Code Llama)","type":"product","url":"https://arxiv.org/abs/2308.12950","page_url":"https://unfragile.ai/code-llama-open-foundation-models-for-code-code-llama","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-code-llama-open-foundation-models-for-code-code-llama__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 multiple programming languages from natural language descriptions or partial code context. Built on Llama 2 transformer architecture with code-specific pretraining, the model learns to map semantic intent to language-specific syntax and idioms. Supports zero-shot generation without task-specific fine-tuning, enabling developers to describe what they want and receive working code implementations.","intents":["Generate boilerplate code from a description without writing it manually","Quickly prototype functions or algorithms in multiple languages","Convert pseudocode or requirements into executable implementations","Generate code for unfamiliar programming languages based on description"],"best_for":["Solo developers building prototypes across multiple languages","Teams needing rapid code generation for common patterns","Developers learning new programming languages"],"limitations":["Native context window of 16k tokens limits generation for large codebases or complex multi-file requirements","No built-in awareness of project-specific conventions, libraries, or architectural patterns unless explicitly provided in prompt","Language-specific performance varies; Python specialization available but other languages rely on general model","No guarantee of security best practices or optimization for production use"],"requires":["Model weights (7B, 13B, 34B, or 70B parameter variants)","Inference framework supporting transformer models (vLLM, llama.cpp, Ollama, or similar)","Sufficient GPU VRAM or CPU memory for model loading (varies by parameter count)"],"input_types":["natural language description","partial code with context","code comments describing intent","pseudocode or algorithm descriptions"],"output_types":["source code in target language","code snippets","complete function or class definitions"],"categories":["code-generation-editing","multi-language-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-code-llama-open-foundation-models-for-code-code-llama__cap_1","uri":"capability://code.generation.editing.fill.in.the.middle.code.completion.with.bidirectional.context","name":"fill-in-the-middle code completion with bidirectional context","description":"Completes code by predicting missing content between existing code segments (prefix and suffix), using bidirectional context awareness. The model learns to understand both what comes before and after the gap, enabling accurate completion of function bodies, loop implementations, or intermediate logic. This capability is implemented through special training procedures that teach the model to condition on both left and right context simultaneously.","intents":["Auto-complete function bodies given the signature and return statement","Fill in missing loop or conditional logic between setup and usage","Complete intermediate steps in multi-step algorithms","Suggest implementations for partially-written code"],"best_for":["IDE integration for real-time code completion","Developers working with incomplete or skeleton code","Code review and refactoring workflows"],"limitations":["Only available in 7B, 13B, and 70B parameter variants; 34B variant does not support infilling","Infilling mechanism details not publicly documented; specific algorithm (e.g., span corruption, bidirectional masking) unknown","Performance degrades with very long gaps or complex multi-statement completions","Requires explicit prefix/suffix formatting; not all inference frameworks support infilling out-of-the-box"],"requires":["Code Llama 7B, 13B, or 70B variant (NOT 34B)","Inference framework with infilling support (llama.cpp with infilling, vLLM, or similar)","Clear separation of prefix and suffix context in prompt formatting"],"input_types":["code prefix (text before gap)","code suffix (text after gap)","optional context about language or function signature"],"output_types":["code completion filling the gap","single or multiple statement suggestions"],"categories":["code-generation-editing","code-completion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-code-llama-open-foundation-models-for-code-code-llama__cap_2","uri":"capability://code.generation.editing.python.specialized.code.generation.with.domain.optimized.performance","name":"python-specialized code generation with domain-optimized performance","description":"A dedicated Code Llama variant fine-tuned specifically on Python code, achieving superior performance on Python-specific benchmarks compared to the general-purpose variants. This specialization involves additional training on Python-heavy datasets and optimization for Python idioms, syntax patterns, and standard library usage. The Python variant outperforms even the 70B general model on Python tasks despite being available in smaller sizes.","intents":["Generate Python code with higher accuracy than general models","Leverage Python-specific idioms and best practices in generated code","Achieve better performance on Python benchmarks with smaller, faster models","Develop Python applications with domain-optimized code generation"],"best_for":["Python-focused development teams","Data science and ML engineers building Python pipelines","Projects where Python is the primary language and performance matters"],"limitations":["Specialization to Python means reduced capability for other languages compared to general Code Llama","Training data and fine-tuning procedures for Python specialization not documented","Performance gains on Python tasks come at potential cost to multi-language flexibility"],"requires":["Code Llama - Python variant (available in 7B, 13B, 34B, 70B sizes)","Inference framework supporting transformer models","Python 3.6+ for generated code execution"],"input_types":["Python code descriptions","Python function signatures","Python pseudocode or algorithm descriptions"],"output_types":["Python source code","Python function implementations","Python class definitions"],"categories":["code-generation-editing","domain-specialization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-code-llama-open-foundation-models-for-code-code-llama__cap_3","uri":"capability://code.generation.editing.instruction.following.code.generation.with.task.specific.adaptation","name":"instruction-following code generation with task-specific adaptation","description":"An instruction-tuned variant of Code Llama trained to follow explicit programming task instructions and multi-step directives. This variant learns to interpret natural language instructions describing what code should do, how it should be structured, and what constraints it should satisfy. The instruction-tuning process (likely using supervised fine-tuning on instruction-code pairs) enables the model to handle more complex, nuanced requests than raw code generation.","intents":["Generate code following specific requirements or constraints","Implement code based on detailed task descriptions","Handle multi-step programming tasks with explicit instructions","Generate code with specific style, structure, or architectural requirements"],"best_for":["Developers using natural language to specify complex code requirements","Teams building code generation systems that need instruction-following","Educational contexts where detailed task specifications are provided"],"limitations":["Instruction-tuning dataset and procedures not documented","Performance depends heavily on instruction clarity and specificity","May struggle with ambiguous or contradictory instructions","No guarantee that generated code follows all specified constraints"],"requires":["Code Llama - Instruct variant","Clear, well-formed instructions describing the code task","Inference framework supporting transformer models"],"input_types":["natural language instructions","task descriptions with requirements","code examples showing desired style","constraints or specifications"],"output_types":["code implementations following instructions","structured code with specified patterns","code with documented constraints"],"categories":["code-generation-editing","instruction-following"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-code-llama-open-foundation-models-for-code-code-llama__cap_4","uri":"capability://code.generation.editing.extended.context.window.reasoning.up.to.100k.tokens","name":"extended context window reasoning up to 100k tokens","description":"While the native training context is 16k tokens, Code Llama demonstrates improved performance on inputs up to 100k tokens, suggesting capability for processing very large codebases, extensive documentation, or multi-file contexts. The mechanism for this extension (e.g., RoPE interpolation, ALiBi, or other positional encoding techniques) is not documented in the abstract, but the capability enables analysis and generation within much larger code repositories than the native window.","intents":["Analyze and generate code within large multi-file codebases","Process extensive documentation or specification alongside code generation","Maintain context across large files or multiple related files","Generate code with awareness of large existing codebases"],"best_for":["Large-scale code generation projects with extensive context","Teams working with monorepos or large interconnected codebases","Scenarios requiring codebase-wide awareness for generation"],"limitations":["Extension mechanism not documented; specific technique unknown","Performance improvements at 100k tokens not quantified; may degrade gracefully","Requires significantly more computational resources than 16k context","Inference latency increases substantially with longer contexts","No guarantee of accuracy or coherence at maximum extended context"],"requires":["Code Llama model (any variant)","Inference framework supporting long context (e.g., vLLM with long context support)","Sufficient GPU VRAM or system memory for 100k token sequences","Careful prompt engineering to fit relevant context within window"],"input_types":["large code files","multiple concatenated source files","code with extensive documentation","large specification documents with code"],"output_types":["code generation with codebase awareness","analysis of large code structures","refactoring suggestions for large files"],"categories":["code-generation-editing","context-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-code-llama-open-foundation-models-for-code-code-llama__cap_5","uri":"capability://automation.workflow.open.source.model.distribution.with.permissive.licensing","name":"open-source model distribution with permissive licensing","description":"Code Llama is released as fully open-source models under a permissive license allowing both research and commercial use, with weights available for download and local deployment. This contrasts with proprietary API-only models, enabling developers to run models locally, fine-tune on private data, and integrate into commercial products without licensing restrictions. The open distribution includes multiple parameter sizes (7B, 13B, 34B, 70B) enabling deployment flexibility.","intents":["Deploy code generation locally without cloud API dependencies","Fine-tune models on proprietary codebases or domain-specific code","Integrate code generation into commercial products without licensing fees","Maintain data privacy by running models on-premises"],"best_for":["Organizations requiring on-premises deployment for compliance or privacy","Teams building commercial products with code generation","Researchers fine-tuning models on specialized code domains","Developers avoiding cloud API costs and latency"],"limitations":["Permissive license type not specified (Apache 2.0, MIT, etc. unknown)","Specific license restrictions and attribution requirements not detailed","Commercial use permitted but exact scope and limitations unclear","Deployment and infrastructure costs fall on user (no managed service)"],"requires":["Model weights (downloadable from Meta or Hugging Face)","Inference framework (llama.cpp, vLLM, Ollama, or similar)","Hardware for local deployment (GPU or CPU with sufficient memory)","Understanding of model licensing terms"],"input_types":["model weights files","inference framework configuration"],"output_types":["locally-deployed model instance","code generation via local inference"],"categories":["automation-workflow","open-source-distribution"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-code-llama-open-foundation-models-for-code-code-llama__cap_6","uri":"capability://automation.workflow.multi.size.model.variants.for.performance.efficiency.tradeoffs","name":"multi-size model variants for performance-efficiency tradeoffs","description":"Code Llama is available in four parameter sizes (7B, 13B, 34B, 70B) enabling developers to choose models based on inference speed, memory constraints, and accuracy requirements. Smaller models (7B, 13B) enable deployment on consumer hardware or edge devices with acceptable latency, while larger models (34B, 70B) provide superior code generation quality for scenarios where accuracy is prioritized. This size flexibility is built into the model family architecture.","intents":["Deploy code generation on resource-constrained devices or edge hardware","Balance code generation quality against inference latency and cost","Run models locally on developer machines without high-end GPUs","Scale inference infrastructure based on quality vs. performance requirements"],"best_for":["Developers with limited hardware resources","Teams optimizing for inference latency in production","Edge deployment scenarios requiring small model footprints","Cost-sensitive deployments where smaller models suffice"],"limitations":["Smaller models (7B, 13B) have lower code generation accuracy than larger variants","34B variant does not support infilling capability","Performance characteristics and accuracy metrics for each size not fully documented","Trade-offs between size and quality require empirical testing for specific use cases"],"requires":["Selection of appropriate model size (7B, 13B, 34B, or 70B)","Hardware matching model size requirements (7B: ~14GB VRAM, 13B: ~26GB, 34B: ~68GB, 70B: ~140GB for full precision)","Inference framework supporting chosen model size"],"input_types":["code generation prompts","infilling requests (7B, 13B, 70B only)"],"output_types":["code generation output","inference latency and throughput metrics"],"categories":["automation-workflow","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-code-llama-open-foundation-models-for-code-code-llama__cap_7","uri":"capability://code.generation.editing.state.of.the.art.performance.on.public.code.generation.benchmarks","name":"state-of-the-art performance on public code generation benchmarks","description":"Code Llama achieves state-of-the-art results among publicly available models on standard code generation benchmarks including HumanEval (67% pass rate), MBPP (65% pass rate), and MultiPL-E. These benchmarks measure functional correctness of generated code across multiple programming languages and problem types. The model's performance is achieved through code-specific pretraining and instruction-tuning, outperforming previous open-source models and matching or exceeding some proprietary baselines.","intents":["Evaluate code generation quality against industry-standard benchmarks","Assess model suitability for production code generation tasks","Compare Code Llama performance against alternative models","Validate that generated code meets functional correctness standards"],"best_for":["Teams evaluating code generation models for production use","Researchers benchmarking code generation capabilities","Organizations comparing Code Llama against proprietary alternatives"],"limitations":["Benchmark performance does not guarantee real-world code quality or security","HumanEval and MBPP test relatively simple programming tasks; performance on complex enterprise code unknown","Benchmarks measure functional correctness but not code efficiency, readability, or maintainability","Performance varies significantly by programming language (Python specialization shows higher scores)","Benchmark results are point-in-time; model updates may change performance"],"requires":["Code Llama model (any variant)","Benchmark evaluation framework (HumanEval, MBPP, MultiPL-E)","Execution environment for testing generated code"],"input_types":["benchmark problem descriptions","test cases for validation"],"output_types":["generated code solutions","pass/fail results on benchmarks","performance metrics (pass rate, accuracy)"],"categories":["code-generation-editing","evaluation-benchmarking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-code-llama-open-foundation-models-for-code-code-llama__cap_8","uri":"capability://planning.reasoning.reinforcement.learning.from.ai.feedback.rlaif.optimization","name":"reinforcement learning from ai feedback (rlaif) optimization","description":"Code Llama incorporates reinforcement learning from AI feedback (RLAIF) as mentioned in the artifact description, a technique where AI-generated feedback (rather than human feedback) is used to optimize model behavior. This approach enables scaling of model improvement beyond human annotation capacity by using other AI systems to evaluate and provide feedback on code generation quality. The specific implementation details and impact on Code Llama's performance are referenced but not detailed in the abstract.","intents":["Improve code generation quality through AI-driven feedback optimization","Scale model training beyond human annotation constraints","Optimize for code correctness and quality metrics using automated evaluation"],"best_for":["Organizations fine-tuning Code Llama on proprietary code","Researchers exploring RLAIF techniques for code generation","Teams implementing continuous model improvement pipelines"],"limitations":["RLAIF implementation details not documented in abstract","Specific feedback mechanisms and reward signals unknown","Impact on final model performance not quantified","Requires access to AI feedback systems and evaluation infrastructure","Potential for feedback loop biases or error propagation unknown"],"requires":["Code Llama model with RLAIF optimization applied","Understanding of RLAIF techniques (see referenced RLAIF paper arXiv:2309.00267)","Infrastructure for implementing AI feedback loops if fine-tuning"],"input_types":["code generation outputs","evaluation criteria for feedback"],"output_types":["optimized model weights","improved code generation quality"],"categories":["planning-reasoning","model-optimization"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["Model weights (7B, 13B, 34B, or 70B parameter variants)","Inference framework supporting transformer models (vLLM, llama.cpp, Ollama, or similar)","Sufficient GPU VRAM or CPU memory for model loading (varies by parameter count)","Code Llama 7B, 13B, or 70B variant (NOT 34B)","Inference framework with infilling support (llama.cpp with infilling, vLLM, or similar)","Clear separation of prefix and suffix context in prompt formatting","Code Llama - Python variant (available in 7B, 13B, 34B, 70B sizes)","Inference framework supporting transformer models","Python 3.6+ for generated code execution","Code Llama - Instruct variant"],"failure_modes":["Native context window of 16k tokens limits generation for large codebases or complex multi-file requirements","No built-in awareness of project-specific conventions, libraries, or architectural patterns unless explicitly provided in prompt","Language-specific performance varies; Python specialization available but other languages rely on general model","No guarantee of security best practices or optimization for production use","Only available in 7B, 13B, and 70B parameter variants; 34B variant does not support infilling","Infilling mechanism details not publicly documented; specific algorithm (e.g., span corruption, bidirectional masking) unknown","Performance degrades with very long gaps or complex multi-statement completions","Requires explicit prefix/suffix formatting; not all inference frameworks support infilling out-of-the-box","Specialization to Python means reduced capability for other languages compared to general Code Llama","Training data and fine-tuning procedures for Python specialization not documented","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.33,"ecosystem":0.25,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:27.894Z","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=code-llama-open-foundation-models-for-code-code-llama","compare_url":"https://unfragile.ai/compare?artifact=code-llama-open-foundation-models-for-code-code-llama"}},"signature":"gUUbHYVYQ2UPCM4hIfH93oaKvnfIcVJaGqhTLBlc8wjTvs15OnbOyIyXjiFsqhEq+zes7uzjiypFQnIUPswKCw==","signedAt":"2026-06-20T01:01:58.420Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/code-llama-open-foundation-models-for-code-code-llama","artifact":"https://unfragile.ai/code-llama-open-foundation-models-for-code-code-llama","verify":"https://unfragile.ai/api/v1/verify?slug=code-llama-open-foundation-models-for-code-code-llama","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"}}