{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"deepseek-coder-v2","slug":"deepseek-coder-v2","name":"DeepSeek Coder V2","type":"model","url":"https://github.com/deepseek-ai/DeepSeek-Coder-V2","page_url":"https://unfragile.ai/deepseek-coder-v2","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"deepseek-coder-v2__cap_0","uri":"capability://code.generation.editing.sparse.mixture.of.experts.code.generation.with.selective.parameter.activation","name":"sparse-mixture-of-experts code generation with selective parameter activation","description":"Generates code from natural language descriptions using a DeepSeekMoE sparse architecture that routes input tokens through a gating network to selectively activate only 21B of 236B total parameters during inference. The router network dynamically chooses which expert sub-networks process each token, enabling efficient computation while maintaining GPT-4-Turbo-level code generation quality. This sparse activation pattern reduces memory footprint and latency compared to dense models while preserving multi-language code generation across 338 programming languages.","intents":["Generate production-ready code from natural language specifications without loading full 236B parameters","Build code generation features with lower inference latency and memory requirements than dense models","Create multi-language code generation systems supporting 300+ programming languages","Deploy code generation on resource-constrained hardware while maintaining competitive performance"],"best_for":["teams building code generation features with hardware constraints (edge devices, cost-sensitive cloud deployments)","developers requiring 338-language support in a single model","organizations prioritizing inference speed and memory efficiency over maximum accuracy"],"limitations":["MoE routing adds ~5-10% computational overhead compared to dense models due to gating network evaluation","Sparse activation means some expert knowledge may be underutilized for certain code patterns","Performance gains are most pronounced at batch sizes >1; single-token generation shows minimal speedup","Requires inference frameworks with native MoE support (SGLang, vLLM) for optimal performance; Transformers library shows 15-20% slower inference"],"requires":["GPU with minimum 40GB VRAM for 236B model (16B Lite variant requires 8GB)","SGLang or vLLM inference framework for MoE-optimized routing","Python 3.8+","CUDA 11.8+ for GPU acceleration"],"input_types":["natural language code specifications (string)","partial code snippets for completion (string)","code with bugs for fixing (string)"],"output_types":["generated code (string)","code completions (string)","fixed code (string)"],"categories":["code-generation-editing","model-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_1","uri":"capability://code.generation.editing.128k.token.context.window.for.repository.level.code.understanding","name":"128k-token context window for repository-level code understanding","description":"Processes up to 128,000 tokens of context enabling analysis and generation across entire code repositories, multiple files, and extensive documentation. The extended context is implemented through rotary position embeddings (RoPE) and optimized attention mechanisms that scale efficiently with the longer sequence length. This allows the model to maintain coherence across large codebases, understand cross-file dependencies, and generate code that respects repository-wide patterns and conventions.","intents":["Analyze entire repository structure and generate code consistent with existing patterns","Perform cross-file refactoring by understanding dependencies across multiple source files","Generate code that respects repository-wide architectural patterns and conventions","Understand and fix bugs that span multiple files or require knowledge of distant code context"],"best_for":["developers working on large monorepo codebases (>50K lines)","teams performing repository-wide refactoring or migration tasks","builders creating code analysis tools that need full-project context","organizations with complex inter-file dependencies requiring holistic understanding"],"limitations":["128K context is sufficient for ~30-50 average source files; very large monorepos may still exceed context","Attention computation scales quadratically with sequence length; 128K tokens adds ~4x latency vs 16K context","Model may lose coherence on tasks requiring reasoning across >100K tokens due to attention dilution","Token counting for mixed code/documentation requires accurate tokenizer; off-by-one errors can truncate critical context"],"requires":["GPU with minimum 40GB VRAM (context length requires proportional memory scaling)","SGLang or vLLM with FlashAttention-2 support for efficient long-context inference","Accurate token counter for the model's vocabulary (32K tokens)","Python 3.8+"],"input_types":["concatenated source code files (string)","repository structure with file contents (string)","code with documentation and comments (string)"],"output_types":["repository-aware code generation (string)","cross-file refactoring suggestions (string)","architectural analysis (string)"],"categories":["code-generation-editing","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_10","uri":"capability://text.generation.language.general.language.understanding.and.non.code.reasoning","name":"general language understanding and non-code reasoning","description":"Maintains strong general language understanding capabilities despite specialization in code, enabling the model to handle natural language questions, summarization, translation, and reasoning tasks. This is achieved through training on 6 trillion tokens including both code and natural language data, preserving the base DeepSeek-V2 general capabilities while enhancing code-specific performance. The model can switch between code and natural language tasks without degradation.","intents":["Answer natural language questions about code, architecture, and programming concepts","Summarize code documentation and generate explanatory text","Translate code between programming languages with natural language guidance","Perform general reasoning tasks alongside code generation in single conversation"],"best_for":["developers building conversational code assistants that handle mixed code/natural language","teams creating documentation generation tools","organizations needing single models for both code and general tasks","builders creating educational tools that explain code concepts"],"limitations":["General language performance is slightly lower than general-purpose models (e.g., GPT-4) due to code specialization","Natural language reasoning on non-code topics may be less robust than general-purpose models","Translation quality varies; code-to-code translation is stronger than code-to-natural-language","Summarization may over-emphasize code structure at the expense of high-level concepts"],"requires":["GPU with 8GB+ VRAM (Lite) or 40GB+ (full variant)","Inference framework: SGLang, vLLM, or Transformers","Python 3.8+"],"input_types":["natural language questions (string)","code with natural language context (string)","mixed code and text (string)"],"output_types":["natural language responses (string)","explanations and summaries (string)","translated code (string)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_11","uri":"capability://automation.workflow.quantization.support.for.memory.efficient.deployment","name":"quantization support for memory-efficient deployment","description":"Supports multiple quantization formats (FP8, INT8, INT4) enabling deployment on hardware with limited VRAM through reduced precision representations. Quantization is implemented through frameworks like GPTQ and AWQ that compress model weights while maintaining reasonable performance. The 236B model can be reduced to 8-16GB VRAM requirements through aggressive quantization, enabling deployment on consumer GPUs and edge devices.","intents":["Deploy code generation on consumer GPUs (RTX 3090, RTX 4090) with limited VRAM","Run code generation on edge devices or resource-constrained environments","Reduce inference costs through lower memory requirements and faster computation","Enable local deployment for teams without enterprise GPU infrastructure"],"best_for":["developers with consumer-grade GPUs (8-24GB VRAM)","organizations deploying models on edge devices or mobile","teams prioritizing cost efficiency over maximum accuracy","researchers experimenting with model compression techniques"],"limitations":["Quantization reduces model precision; quality degradation ranges from 5-15% depending on quantization level","INT4 quantization shows noticeable quality loss on complex reasoning tasks","Quantized models require specific inference frameworks; not all frameworks support all quantization formats","Quantization-aware training is not provided; post-training quantization may be suboptimal"],"requires":["Quantization tools: GPTQ, AWQ, or similar","GPU with 8GB+ VRAM (quantized models)","Inference framework supporting quantization (vLLM, SGLang, or Transformers with bitsandbytes)","Python 3.8+"],"input_types":["code generation prompts (string)"],"output_types":["generated code (string)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_12","uri":"capability://code.generation.editing.cross.file.code.refactoring.with.dependency.tracking","name":"cross-file code refactoring with dependency tracking","description":"Performs refactoring across multiple files by understanding inter-file dependencies and maintaining consistency across the codebase. The 128K context window enables loading multiple related files simultaneously, and the model can track variable definitions, function calls, and imports across files to generate refactoring changes that respect dependencies. This is implemented through careful prompt engineering that includes dependency information and cross-file references.","intents":["Rename variables/functions consistently across multiple files","Extract common code into shared utilities while updating all references","Reorganize code structure while maintaining import relationships","Perform API changes that require updates across multiple dependent files"],"best_for":["teams performing large-scale refactoring on monorepos","developers extracting shared libraries from existing code","organizations migrating between architectural patterns","teams managing complex inter-file dependencies"],"limitations":["Refactoring accuracy depends on complete dependency information; missing dependencies lead to broken references","Cannot verify refactoring correctness without running tests; may introduce subtle bugs","Performance degrades on very large refactorings (>50 files) due to context window limits","No built-in rollback or undo; requires version control integration for safety"],"requires":["GPU with 40GB+ VRAM (full model) or 8GB+ (Lite model)","128K context window (requires SGLang or vLLM for efficient processing)","Dependency analysis tool to identify cross-file references","Version control system (Git) for tracking changes","Python 3.8+"],"input_types":["multiple source files with dependency information (array of strings)","refactoring specification (string)","dependency graph (structured)"],"output_types":["refactored code for each file (array of strings)","refactoring summary (string)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_13","uri":"capability://code.generation.editing.programming.language.translation.with.semantic.preservation","name":"programming language translation with semantic preservation","description":"Translates code from one programming language to another while preserving semantic meaning and functionality. The model understands language-specific idioms, standard libraries, and design patterns, enabling it to generate idiomatic code in the target language rather than literal translations. This works through providing source code in one language and requesting translation to another, with optional constraints (preserve performance characteristics, use specific libraries, etc.).","intents":["Migrate codebases between programming languages (Python to Rust, JavaScript to TypeScript, etc.)","Port algorithms across language ecosystems while maintaining performance","Generate language-specific implementations from language-agnostic pseudocode","Create multi-language implementations of the same functionality"],"best_for":["Teams migrating between technology stacks","Organizations supporting multiple language implementations of core algorithms","Developers learning new languages by translating familiar code"],"limitations":["Translation quality varies significantly by language pair — well-supported pairs (Python↔JavaScript) achieve >90% correctness; rare pairs may drop to 60-70%","Cannot automatically translate language-specific features (decorators, macros, generics) without explicit mapping","Performance characteristics may not be preserved — generated code may be slower or use more memory than hand-optimized implementations","Library ecosystem differences may require manual API mapping — model cannot automatically find equivalent libraries","Generated code must be tested thoroughly — semantic equivalence is not guaranteed"],"requires":["GPU with minimum 8GB VRAM","Source code in supported language","Target language specification","Python 3.8+"],"input_types":["source code in source language","target language specification","library or framework constraints","performance or style requirements"],"output_types":["translated source code in target language","library mapping suggestions","idiomatic patterns for target language","migration notes and warnings"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_2","uri":"capability://code.generation.editing.multi.language.code.completion.with.338.language.support","name":"multi-language code completion with 338-language support","description":"Completes partially written code across 338 programming languages by predicting the most probable next tokens based on context. The model was trained on 1.5 trillion code tokens spanning diverse language ecosystems, enabling it to understand syntax, idioms, and conventions for mainstream languages (Python, JavaScript, Java, C++) and niche languages (Rust, Go, Kotlin, Haskell, etc.). Completion works through standard next-token prediction with language-specific tokenization and vocabulary handling.","intents":["Auto-complete code in any of 338 supported languages with context-aware suggestions","Build IDE plugins that provide intelligent code completion across polyglot codebases","Generate boilerplate code and common patterns in less-common languages","Support developers working in emerging or domain-specific languages with limited tooling"],"best_for":["polyglot development teams using 5+ programming languages","IDE/editor plugin developers targeting broad language coverage","organizations using niche or domain-specific languages (Solidity, Verilog, etc.)","developers seeking open-source completion without vendor lock-in"],"limitations":["Completion quality varies significantly across languages; mainstream languages (Python, JavaScript) achieve 85%+ accuracy while niche languages may drop to 60-70%","No language detection; requires explicit language context or prompt engineering to avoid cross-language contamination","Completion suggestions are single-pass; no iterative refinement or ranking of alternatives","Language-specific idioms and best practices may not be consistently applied across all 338 languages due to training data imbalance"],"requires":["GPU with 8GB+ VRAM (Lite variant) or 40GB+ (full variant)","Inference framework: SGLang, vLLM, or Transformers library","Language-specific tokenizer configuration for accurate token boundaries","Python 3.8+"],"input_types":["partial code snippet (string)","code with cursor position indicator (string)","code with language hint/comment (string)"],"output_types":["completed code (string)","multiple completion suggestions (array of strings)","completion with confidence scores (structured)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_3","uri":"capability://code.generation.editing.code.debugging.and.bug.fixing.through.error.pattern.recognition","name":"code debugging and bug-fixing through error pattern recognition","description":"Identifies and fixes bugs in code by analyzing error patterns, exception messages, and logical inconsistencies learned during training on 6 trillion tokens including buggy code examples and fixes. The model uses its 128K context window to understand the full scope of buggy code, trace execution paths, and suggest corrections. Debugging works through prompt engineering (e.g., 'Fix the bug in this code') or instruction-tuned variants that explicitly handle debugging tasks.","intents":["Automatically suggest bug fixes for common programming errors (null pointer exceptions, off-by-one errors, type mismatches)","Analyze error stack traces and generate corrected code","Refactor buggy code patterns across multiple files using repository context","Generate test cases that expose bugs and validate fixes"],"best_for":["developers debugging complex multi-file issues","teams building automated code review and quality assurance tools","organizations seeking to reduce time spent on bug triage and fixing","developers working in languages with limited IDE debugging support"],"limitations":["Debugging accuracy depends on error clarity; vague or missing error messages reduce fix quality","Cannot execute code to verify fixes; suggestions may introduce new bugs or miss edge cases","Performance degrades on domain-specific bugs requiring specialized knowledge (e.g., memory management in C, concurrency in Rust)","No built-in test generation; fixes are not automatically validated against test suites"],"requires":["GPU with 8GB+ VRAM (Lite) or 40GB+ (full variant)","Instruct-tuned variant (DeepSeek-Coder-V2-Instruct) for best debugging performance","Error messages or stack traces as input (optional but improves accuracy)","Python 3.8+"],"input_types":["buggy code snippet (string)","error message or stack trace (string)","code with comments describing the bug (string)"],"output_types":["fixed code (string)","explanation of the bug and fix (string)","multiple fix suggestions (array of strings)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_4","uri":"capability://planning.reasoning.mathematical.reasoning.and.step.by.step.problem.solving","name":"mathematical reasoning and step-by-step problem solving","description":"Solves mathematical problems through step-by-step reasoning by generating intermediate reasoning steps and final answers. The model was trained on mathematical reasoning datasets and code-based mathematical solutions, enabling it to handle both symbolic math and numerical computation. Reasoning is implemented through chain-of-thought prompting where the model generates natural language reasoning steps followed by code or mathematical notation for the solution.","intents":["Solve mathematical problems with step-by-step explanations","Generate code that implements mathematical algorithms (sorting, optimization, linear algebra)","Verify mathematical correctness of code implementations","Teach mathematical concepts by generating explanatory code examples"],"best_for":["educators building tutoring systems with mathematical problem solving","developers implementing mathematical algorithms and needing verification","researchers prototyping mathematical solutions before formal implementation","teams building STEM education tools"],"limitations":["Mathematical reasoning quality degrades on problems requiring >10 reasoning steps or novel mathematical insights","No symbolic math engine; purely text-based reasoning may miss algebraic simplifications or elegant solutions","Numerical precision limited by floating-point representation; high-precision math (>64-bit) requires explicit handling","Cannot solve problems requiring real-time computation or access to external math libraries"],"requires":["GPU with 8GB+ VRAM (Lite) or 40GB+ (full variant)","Instruct-tuned variant for best reasoning performance","Optional: Python environment for executing generated mathematical code","Python 3.8+"],"input_types":["mathematical problem statement (string)","code with mathematical errors (string)","mathematical notation or equations (string)"],"output_types":["step-by-step reasoning (string)","final answer (string or number)","code implementing the solution (string)"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_5","uri":"capability://code.generation.editing.instruction.following.code.generation.with.fine.tuned.response.formatting","name":"instruction-following code generation with fine-tuned response formatting","description":"Generates code in response to natural language instructions through instruction-tuning on the base model. The Instruct variants (DeepSeek-Coder-V2-Instruct) are fine-tuned to follow specific formatting conventions, respect constraints, and generate code that matches user intent more precisely than base models. This is implemented through supervised fine-tuning on instruction-response pairs where the model learns to parse instructions, extract requirements, and generate appropriately formatted code.","intents":["Generate code that strictly follows user specifications and formatting requirements","Build conversational code generation interfaces that understand multi-turn instructions","Create code generation APIs that reliably produce output matching expected formats","Generate code with specific documentation, comments, or style conventions"],"best_for":["developers building code generation APIs or chat interfaces","teams requiring consistent code formatting and style across generated code","organizations using code generation in production with strict output requirements","builders creating code generation features for non-technical users"],"limitations":["Instruction-following quality depends on instruction clarity; ambiguous or conflicting instructions may produce unexpected results","Fine-tuning may reduce raw generation capability on tasks not covered in instruction-tuning data","No built-in constraint validation; generated code may violate specified constraints without explicit checking","Instruction interpretation is probabilistic; same instruction may produce different outputs across runs"],"requires":["Instruct-tuned variant (DeepSeek-Coder-V2-Instruct, not Base)","GPU with 8GB+ VRAM (Lite) or 40GB+ (full variant)","Clear, well-formatted instructions in natural language","Python 3.8+"],"input_types":["natural language instruction (string)","instruction with constraints or requirements (string)","multi-turn conversation history (array of strings)"],"output_types":["generated code (string)","code with documentation (string)","formatted code response (string)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_6","uri":"capability://automation.workflow.efficient.inference.through.sglang.and.vllm.framework.integration","name":"efficient inference through sglang and vllm framework integration","description":"Optimizes inference performance through native integration with SGLang and vLLM frameworks that implement MoE-specific optimizations, FP8 quantization, and FlashAttention-2 for long-context processing. SGLang provides MLA (Multi-head Latent Attention) optimizations specific to DeepSeek architecture, while vLLM offers batching and KV-cache management. These frameworks handle the sparse routing overhead and expert activation scheduling, reducing latency by 30-50% compared to standard Transformers library inference.","intents":["Deploy code generation models with 30-50% lower latency than standard inference","Build high-throughput code generation services handling multiple concurrent requests","Run inference on resource-constrained hardware using FP8 quantization","Optimize long-context inference (128K tokens) with efficient attention mechanisms"],"best_for":["teams deploying code generation in production with latency requirements","organizations running inference at scale with cost constraints","developers building real-time code completion features","teams requiring efficient long-context processing"],"limitations":["SGLang and vLLM add setup complexity; requires framework-specific configuration and knowledge","FP8 quantization reduces model precision; may impact quality on edge cases or specialized tasks","Framework-specific optimizations may not be available on all hardware (e.g., older GPUs)","Batching benefits require multiple concurrent requests; single-request inference shows minimal speedup"],"requires":["SGLang or vLLM framework installed and configured","GPU with compute capability 7.0+ (Volta or newer) for optimal performance","CUDA 11.8+ for GPU acceleration","Python 3.8+","Optional: Quantization tools (GPTQ, AWQ) for FP8 quantization"],"input_types":["code generation prompts (string)","batch of requests (array of strings)"],"output_types":["generated code (string)","batch results (array of strings)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_7","uri":"capability://code.generation.editing.base.model.raw.generation.for.fine.tuning.and.domain.adaptation","name":"base model raw generation for fine-tuning and domain adaptation","description":"Provides base model variants (DeepSeek-Coder-V2-Base and Lite-Base) without instruction-tuning, enabling downstream fine-tuning on domain-specific code or custom instruction sets. Base models preserve the full generative capability without the constraints of instruction-tuning, allowing organizations to adapt the model to proprietary coding standards, domain-specific languages, or specialized tasks. Fine-tuning can be performed using standard techniques (LoRA, QLoRA, full fine-tuning) on custom datasets.","intents":["Fine-tune the model on proprietary codebases to match internal coding standards","Adapt the model to domain-specific languages or frameworks not well-represented in training data","Create specialized code generation models for specific industries (finance, healthcare, embedded systems)","Develop custom instruction-following variants tailored to organizational needs"],"best_for":["organizations with large proprietary codebases wanting domain-specific models","teams building specialized code generation for niche languages or frameworks","researchers experimenting with model adaptation and fine-tuning","companies requiring models that follow proprietary coding standards"],"limitations":["Fine-tuning requires significant computational resources (GPU with 40GB+ VRAM for full fine-tuning)","Quality of fine-tuned models depends heavily on fine-tuning dataset size and quality","Base models lack instruction-following capability; require explicit prompt engineering for good results","Fine-tuning may reduce general-purpose code generation capability if not carefully balanced"],"requires":["Base model variant (DeepSeek-Coder-V2-Base or Lite-Base)","GPU with 40GB+ VRAM for full fine-tuning (8GB+ for LoRA/QLoRA)","Fine-tuning framework: Hugging Face Transformers, DeepSpeed, or similar","Custom fine-tuning dataset (minimum 1000-5000 examples for meaningful adaptation)","Python 3.8+"],"input_types":["fine-tuning dataset (code-instruction pairs)","domain-specific code examples (string)"],"output_types":["fine-tuned model weights (checkpoint)","adapted model for inference (string outputs)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_8","uri":"capability://tool.use.integration.hugging.face.transformers.integration.for.standard.pytorch.workflows","name":"hugging face transformers integration for standard pytorch workflows","description":"Integrates with Hugging Face Transformers library enabling standard PyTorch-based inference and fine-tuning workflows. Models are available on Hugging Face Hub with pre-configured tokenizers, model configs, and example code. This integration allows developers to use familiar Transformers APIs (AutoTokenizer, AutoModelForCausalLM) without framework-specific knowledge, though inference performance is 15-20% slower than SGLang/vLLM due to lack of MoE-specific optimizations.","intents":["Use DeepSeek-Coder-V2 with standard Transformers library without learning new frameworks","Integrate code generation into existing PyTorch-based ML pipelines","Fine-tune the model using Transformers Trainer API with standard configurations","Access pre-configured tokenizers and model weights from Hugging Face Hub"],"best_for":["developers already using Hugging Face Transformers in their workflows","teams prioritizing ease of integration over inference performance","researchers prototyping code generation features quickly","organizations with existing PyTorch infrastructure"],"limitations":["Inference is 15-20% slower than SGLang/vLLM due to lack of MoE-specific optimizations","No built-in batching or KV-cache optimization; requires manual implementation for production use","Memory usage is higher than optimized frameworks; full 236B model requires 40GB+ VRAM","Long-context inference (128K tokens) is significantly slower due to quadratic attention complexity"],"requires":["Hugging Face Transformers library (>=4.36.0)","PyTorch (>=2.0.0)","GPU with 8GB+ VRAM (Lite) or 40GB+ (full variant)","CUDA 11.8+ for GPU acceleration","Python 3.8+"],"input_types":["text prompts (string)","tokenized input (tensor)"],"output_types":["generated text (string)","token logits (tensor)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__cap_9","uri":"capability://tool.use.integration.deepseek.platform.api.access.for.cloud.based.inference","name":"deepseek platform api access for cloud-based inference","description":"Provides cloud-based inference through DeepSeek's managed API platform, eliminating the need for local GPU infrastructure. The API handles model serving, scaling, and optimization transparently, returning generated code via REST/gRPC endpoints. This approach trades local control for operational simplicity and automatic scaling, suitable for teams without GPU infrastructure or variable workload patterns.","intents":["Access code generation capabilities without managing GPU infrastructure","Scale code generation to handle variable workloads automatically","Integrate code generation into applications without local model deployment","Prototype code generation features quickly without infrastructure setup"],"best_for":["startups and small teams without GPU infrastructure","applications with variable or unpredictable code generation workloads","teams prioritizing time-to-market over cost optimization","organizations with data residency constraints allowing cloud deployment"],"limitations":["API latency is higher than local inference (typically 500ms-2s vs 100-500ms locally)","Requires internet connectivity; no offline capability","API costs accumulate with usage; not cost-effective for high-volume applications","Data is sent to DeepSeek servers; not suitable for proprietary code or sensitive data","Rate limiting and quota restrictions may apply"],"requires":["DeepSeek API account and API key","Internet connectivity","HTTP/gRPC client library","Python 3.8+ (for official SDK)"],"input_types":["code generation prompts (string)","JSON request with parameters (structured)"],"output_types":["generated code (string)","JSON response with metadata (structured)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"deepseek-coder-v2__headline","uri":"capability://code.generation.editing.state.of.the.art.code.language.model","name":"state-of-the-art code language model","description":"DeepSeek Coder V2 is a cutting-edge open-source code language model designed for code generation, completion, debugging, and mathematical reasoning across 338 programming languages, utilizing a Mixture-of-Experts architecture for enhanced performance.","intents":["best AI code model","code generation model for multiple languages","open-source code completion tool","AI model for debugging code","best model for mathematical reasoning in code"],"best_for":["developers needing advanced code assistance","teams working with multiple programming languages"],"limitations":["requires substantial computational resources for optimal performance"],"requires":["access to a suitable computing environment"],"input_types":["natural language descriptions","partially written code"],"output_types":["completed code","debugged code","mathematical solutions"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["GPU with minimum 40GB VRAM for 236B model (16B Lite variant requires 8GB)","SGLang or vLLM inference framework for MoE-optimized routing","Python 3.8+","CUDA 11.8+ for GPU acceleration","GPU with minimum 40GB VRAM (context length requires proportional memory scaling)","SGLang or vLLM with FlashAttention-2 support for efficient long-context inference","Accurate token counter for the model's vocabulary (32K tokens)","GPU with 8GB+ VRAM (Lite) or 40GB+ (full variant)","Inference framework: SGLang, vLLM, or Transformers","Quantization tools: GPTQ, AWQ, or similar"],"failure_modes":["MoE routing adds ~5-10% computational overhead compared to dense models due to gating network evaluation","Sparse activation means some expert knowledge may be underutilized for certain code patterns","Performance gains are most pronounced at batch sizes >1; single-token generation shows minimal speedup","Requires inference frameworks with native MoE support (SGLang, vLLM) for optimal performance; Transformers library shows 15-20% slower inference","128K context is sufficient for ~30-50 average source files; very large monorepos may still exceed context","Attention computation scales quadratically with sequence length; 128K tokens adds ~4x latency vs 16K context","Model may lose coherence on tasks requiring reasoning across >100K tokens due to attention dilution","Token counting for mixed code/documentation requires accurate tokenizer; off-by-one errors can truncate critical context","General language performance is slightly lower than general-purpose models (e.g., GPT-4) due to code specialization","Natural language reasoning on non-code topics may be less robust than general-purpose models","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.690Z","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=deepseek-coder-v2","compare_url":"https://unfragile.ai/compare?artifact=deepseek-coder-v2"}},"signature":"5NcgdO0JfH+8AyP0EQvGV9EEPP0ozAyt0tZVQBReW313WAYGfyomNFL+X7cKrIVif96cLlNIexSpCaU2FMXjBQ==","signedAt":"2026-06-21T16:51:01.108Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/deepseek-coder-v2","artifact":"https://unfragile.ai/deepseek-coder-v2","verify":"https://unfragile.ai/api/v1/verify?slug=deepseek-coder-v2","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"}}