{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"gemma-2","slug":"gemma-2","name":"Gemma 2","type":"model","url":"https://ai.google.dev/gemma/docs/gemma2","page_url":"https://unfragile.ai/gemma-2","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"gemma-2__cap_0","uri":"capability://text.generation.language.interleaved.local.global.attention.for.long.context.processing","name":"interleaved local-global attention for long-context processing","description":"Gemma 2 implements a hybrid attention mechanism that alternates between local (sliding window) and global (full sequence) attention layers throughout the transformer stack. Local attention reduces computational complexity from O(n²) to O(n·w) where w is window size, while global attention layers maintain long-range dependencies. This architecture enables efficient processing of contexts up to 8K tokens without the quadratic memory scaling of standard dense attention, using a pattern similar to Longformer but optimized for inference speed on consumer hardware.","intents":["Process long documents or conversations without running out of GPU memory","Build RAG systems that can handle multi-document contexts efficiently","Deploy models on resource-constrained devices while maintaining reasoning over extended context"],"best_for":["Teams building on-device AI applications with memory constraints","Developers implementing RAG pipelines requiring 4K-8K token contexts","Edge deployment scenarios (mobile, embedded systems, local inference)"],"limitations":["Local attention window size is fixed during training — cannot dynamically adjust window width at inference","Global attention layers still incur O(n²) cost for those specific layers, limiting true long-context scaling beyond 8K tokens","Interleaved pattern may reduce effectiveness for tasks requiring dense cross-document reasoning across all positions"],"requires":["Transformer-compatible inference framework (vLLM, llama.cpp, Ollama, or similar)","Minimum 4GB VRAM for 9B model, 8GB for 27B model in 8-bit quantization","Support for Flash Attention v2 or equivalent for optimal performance"],"input_types":["text (raw strings)","tokenized sequences (up to 8192 tokens)"],"output_types":["text (generated tokens)","logits (raw model outputs for custom decoding)"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__cap_1","uri":"capability://text.generation.language.knowledge.distillation.from.gemini.models.with.capability.preservation","name":"knowledge distillation from gemini models with capability preservation","description":"Gemma 2 is trained using knowledge distillation from larger Gemini models, where the 27B variant learns to replicate reasoning patterns and factual knowledge from Gemini's 70B+ scale models. This involves training on synthetic data generated by Gemini, response ranking using Gemini outputs as ground truth, and fine-tuning on instruction-following tasks where Gemini demonstrates superior performance. The distillation process preserves reasoning capabilities while reducing model size by ~60%, enabling the 27B model to match 70B Llama 3 performance on benchmarks like MMLU and GSM8K.","intents":["Deploy models with 70B-equivalent reasoning on hardware that only supports 27B models","Reduce inference latency and cost while maintaining answer quality for production systems","Access Gemini-level instruction-following and factual accuracy in an open-source model"],"best_for":["Production teams needing cost-effective inference without sacrificing reasoning quality","Organizations with GPU/TPU constraints who need 70B-class performance on 27B hardware","Builders creating on-device assistants requiring Gemini-comparable instruction-following"],"limitations":["Distilled knowledge is frozen at training time — cannot adapt to new information or domains without retraining","Performance gains are task-dependent; distillation works best for reasoning and instruction-following but may underperform on specialized domains not well-represented in Gemini training data","Reasoning transparency is reduced compared to larger models — distilled models may produce correct answers without showing intermediate reasoning steps"],"requires":["Inference framework supporting Gemma 2 (Hugging Face Transformers, vLLM, Ollama, etc.)","No special hardware required; standard GPU/CPU inference supported","Familiarity with prompt engineering for instruction-following models"],"input_types":["text (natural language instructions)","structured prompts (few-shot examples, system instructions)"],"output_types":["text (generated responses)","structured outputs (JSON, code, formatted text via prompt engineering)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__cap_10","uri":"capability://planning.reasoning.benchmark.competitive.performance.across.reasoning.coding.and.language.understanding.tasks","name":"benchmark-competitive performance across reasoning, coding, and language understanding tasks","description":"Achieves strong performance on standard ML benchmarks (MMLU, HumanEval, GSM8K, etc.) with the 27B variant matching or exceeding Llama 3 70B on many tasks despite being 2.6x smaller. Performance comes from combination of base training on diverse data, instruction-tuning for task-specific formats, and knowledge distillation from Gemini models. Benchmark results are publicly available and reproducible, enabling informed model selection for specific use cases.","intents":["Select Gemma 2 variant based on benchmark performance for specific tasks (reasoning, coding, QA)","Evaluate whether Gemma 2 meets quality requirements for production applications","Compare Gemma 2 performance against other open models for informed deployment decisions","Identify which model size (2B/9B/27B) provides acceptable quality for specific use cases"],"best_for":["Teams evaluating models for production deployment based on benchmark performance","Researchers comparing model capabilities across the open model landscape","Developers making cost-quality tradeoff decisions for inference infrastructure"],"limitations":["Benchmark performance doesn't always correlate with real-world application quality — may perform differently on custom tasks","Benchmarks may favor models trained on similar data — Gemma 2's performance on benchmarks may not reflect performance on novel domains","Benchmark scores are point-in-time measurements — model behavior may vary with different prompting strategies or input formats","No benchmarks for all relevant tasks — some specialized domains (medical, legal) may not have published benchmarks"],"requires":["No special requirements — benchmark results are published and publicly available","For custom evaluation: inference framework and benchmark implementation (HuggingFace evaluate library, etc.)"],"input_types":["benchmark datasets (MMLU, HumanEval, GSM8K, etc.)"],"output_types":["benchmark scores, performance metrics, comparative analysis"],"categories":["planning-reasoning","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__cap_2","uri":"capability://text.generation.language.multi.size.model.family.with.consistent.api.across.2b.9b.and.27b.variants","name":"multi-size model family with consistent api across 2b, 9b, and 27b variants","description":"Gemma 2 provides three model sizes (2B, 9B, 27B) with identical tokenizer, architecture, and API interface, enabling seamless scaling from edge devices to high-performance inference. All variants use the same vocabulary, attention patterns, and instruction format, allowing developers to prototype on 2B, validate on 9B, and deploy on 27B without code changes. This consistency is achieved through careful architectural design where layer counts and hidden dimensions scale proportionally while maintaining the same transformer block structure and attention mechanism.","intents":["Start development on lightweight 2B model and scale to 27B for production without refactoring","A/B test model sizes to find optimal performance-cost tradeoff for specific workloads","Deploy different model sizes across heterogeneous infrastructure (edge devices, servers, cloud) with unified inference code"],"best_for":["Teams with diverse hardware targets from mobile to data center","Developers iterating on model selection without infrastructure changes","Organizations optimizing for cost-performance across multiple deployment scenarios"],"limitations":["Consistent API means no size-specific optimizations — larger models cannot leverage additional capacity for novel capabilities","Performance scaling is not linear; 27B is ~3x slower than 9B but not 3x more capable on all tasks","All sizes share the same 8K context window limit, so larger models don't gain extended context capability"],"requires":["Inference framework supporting Gemma 2 (Hugging Face Transformers 4.42+, vLLM, Ollama, llama.cpp)","2B: 2GB VRAM minimum (4GB recommended)","9B: 6GB VRAM minimum (8GB recommended)","27B: 16GB VRAM minimum (24GB recommended for batch inference)"],"input_types":["text (raw strings)","tokenized sequences"],"output_types":["text (generated tokens)","logits (for custom decoding strategies)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__cap_3","uri":"capability://text.generation.language.instruction.following.with.structured.output.formatting.via.prompting","name":"instruction-following with structured output formatting via prompting","description":"Gemma 2 is fine-tuned on instruction-following tasks using a specific prompt format that enables reliable structured output generation (JSON, code, markdown tables) through prompt engineering rather than constrained decoding. The model learns to follow format specifications in system prompts and examples, using patterns like 'Output as JSON: {\"key\": \"value\"}' to guide generation. This approach leverages the model's reasoning capabilities to understand and respect output constraints without requiring specialized decoding logic, making it compatible with any inference framework.","intents":["Extract structured data from unstructured text using prompt-based formatting instructions","Generate code, JSON, or other formatted outputs reliably without custom decoding constraints","Build multi-turn conversational systems that follow complex instruction sequences"],"best_for":["Developers building data extraction pipelines without access to constrained decoding","Teams using inference frameworks that don't support grammar-based output constraints","Applications requiring flexible output formats that vary per request"],"limitations":["Structured output reliability depends on prompt quality — malformed instructions or ambiguous examples reduce accuracy","No hard guarantees on output format; model may occasionally deviate from specified structure, requiring post-processing validation","Prompt engineering overhead is significant; requires careful tuning of examples and format specifications for each use case","Cannot enforce complex constraints like 'valid JSON with specific schema' — only suggests format through examples"],"requires":["Inference framework supporting Gemma 2 (any framework works)","Prompt engineering expertise to craft effective format instructions","Post-processing logic to validate and handle format deviations"],"input_types":["text (natural language instructions with format specifications)","few-shot examples (demonstrating desired output format)"],"output_types":["text (formatted as JSON, code, markdown, or custom structures)","structured data (via post-processing of formatted text)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__cap_4","uri":"capability://text.generation.language.efficient.inference.optimization.with.quantization.and.flash.attention.support","name":"efficient inference optimization with quantization and flash attention support","description":"Gemma 2 is optimized for inference through native support for 8-bit and 4-bit quantization (via bitsandbytes, GPTQ, AWQ) and Flash Attention v2 integration, reducing memory footprint by 75-87% and improving throughput by 2-4x compared to full-precision inference. The model architecture is designed to maintain quality under aggressive quantization through careful layer normalization and activation scaling during training. Inference frameworks like vLLM, Ollama, and llama.cpp provide optimized kernels for Gemma 2 specifically, enabling sub-100ms latency on consumer GPUs.","intents":["Run 27B model on 8GB GPUs through quantization without significant quality loss","Achieve sub-100ms latency for real-time inference on consumer hardware","Reduce inference costs by 3-4x through memory efficiency and faster throughput"],"best_for":["Teams deploying on consumer GPUs or edge devices with memory constraints","Applications requiring low-latency inference (chatbots, real-time assistants)","Cost-sensitive deployments where inference efficiency directly impacts margins"],"limitations":["4-bit quantization may reduce reasoning quality on complex tasks — 8-bit is recommended for reasoning-heavy workloads","Quantization benefits vary by task; simple generation tasks see minimal quality loss, but multi-step reasoning may degrade","Flash Attention requires specific GPU architectures (Ampere/newer for NVIDIA); older GPUs fall back to standard attention","Quantization is one-way — cannot recover full precision from quantized weights without retraining"],"requires":["GPU with compute capability 7.0+ (RTX 2060 or newer for NVIDIA) for optimal quantization","Quantization library: bitsandbytes, GPTQ, or AWQ","Inference framework with Gemma 2 optimization: vLLM 0.3+, Ollama 0.1.20+, or llama.cpp with recent Gemma 2 support"],"input_types":["text (raw strings)","tokenized sequences"],"output_types":["text (generated tokens)","logits (in full precision, even with quantized weights)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__cap_5","uri":"capability://safety.moderation.safety.aligned.instruction.following.with.reduced.harmful.output.generation","name":"safety-aligned instruction following with reduced harmful output generation","description":"Gemma 2 is trained with constitutional AI and safety fine-tuning to reduce generation of harmful, illegal, or unethical content while maintaining instruction-following capability. The model uses a combination of RLHF (reinforcement learning from human feedback) with safety-focused reward models and instruction-following data to balance helpfulness and safety. This is implemented through a two-stage training process: first instruction-following on benign tasks, then safety fine-tuning on adversarial examples to reduce harmful outputs without catastrophic forgetting of useful capabilities.","intents":["Deploy models in production without extensive content filtering or guardrails","Reduce liability and moderation costs by using a model trained to refuse harmful requests","Build customer-facing applications with reduced risk of generating offensive or illegal content"],"best_for":["Teams building public-facing AI applications (chatbots, customer service)","Organizations with limited content moderation resources","Applications in regulated industries (healthcare, finance) requiring safety-by-design"],"limitations":["Safety alignment is probabilistic — model may still generate harmful content on adversarial prompts or jailbreak attempts","Safety training may reduce model capability on legitimate but sensitive topics (medical advice, security research)","Safety alignment is not transparent — difficult to audit or understand specific safety boundaries without testing","Jailbreaks and prompt injection attacks can bypass safety training; additional guardrails recommended for high-risk applications"],"requires":["Inference framework supporting Gemma 2","Understanding of model limitations and potential jailbreaks","Additional content filtering or guardrails for high-risk applications (optional but recommended)"],"input_types":["text (natural language instructions, potentially adversarial)"],"output_types":["text (generated responses with reduced harmful content)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__cap_6","uri":"capability://text.generation.language.multilingual.instruction.following.with.cross.lingual.transfer","name":"multilingual instruction-following with cross-lingual transfer","description":"Gemma 2 is trained on multilingual instruction-following data, enabling the model to follow instructions and generate coherent responses in 10+ languages including English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Chinese, and Japanese. The model achieves this through cross-lingual transfer during training, where instruction-following patterns learned in English transfer to other languages through shared vocabulary and transformer representations. Performance varies by language, with European languages performing near-English quality while Asian languages show 10-20% quality degradation due to tokenization and training data imbalance.","intents":["Build multilingual chatbots and assistants without separate models per language","Serve global user bases with single model deployment","Translate and localize AI applications across languages with minimal engineering overhead"],"best_for":["Global teams building international applications","Startups with limited resources for language-specific model development","Applications serving diverse language communities from single infrastructure"],"limitations":["Performance is language-dependent; English quality is baseline, European languages ~90% of English quality, Asian languages ~70-80%","Tokenization efficiency varies by language — CJK languages require 2-3x more tokens for same semantic content","Instruction-following quality is lower in low-resource languages; model may misinterpret instructions in languages with limited training data","No language detection — requires explicit language specification or context to select appropriate language for generation"],"requires":["Inference framework supporting Gemma 2","Language-specific prompt engineering for optimal results in non-English languages","Awareness of language-specific quality degradation for quality-critical applications"],"input_types":["text (instructions in supported languages)","mixed-language prompts (code-switching supported but not optimized)"],"output_types":["text (generated responses in target language)"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__cap_7","uri":"capability://code.generation.editing.context.aware.code.generation.with.programming.language.support","name":"context-aware code generation with programming language support","description":"Gemma 2 is trained on code from multiple programming languages (Python, JavaScript, Java, C++, Go, Rust, etc.) and can generate syntactically correct code snippets, complete functions, and debug code based on natural language descriptions or partial code context. The model uses instruction-following to understand code-specific requests like 'write a function that sorts an array' or 'fix the bug in this code', and maintains language-specific syntax through learned patterns. Performance is strongest in Python and JavaScript (languages with abundant training data) and weaker in niche languages like Rust or Kotlin.","intents":["Generate boilerplate code and utility functions from natural language descriptions","Complete partial code snippets with context-aware suggestions","Debug code by analyzing error messages and suggesting fixes"],"best_for":["Developers using Gemma 2 in IDE plugins or code editors","Teams building code generation tools for internal use","Educational applications teaching programming concepts"],"limitations":["Code generation quality is language-dependent; Python and JavaScript ~80% correctness, niche languages ~50-60%","No real-time compilation or execution — generated code may have syntax errors requiring manual review","Limited understanding of complex project structures — cannot reliably generate code that integrates with large codebases","No access to external libraries or APIs — generated code may use non-existent functions or incorrect library calls"],"requires":["Inference framework supporting Gemma 2","Code context (file content, error messages) for best results","Manual code review and testing before deployment"],"input_types":["text (natural language code requests)","code (partial code snippets for completion or debugging)","error messages (for debugging assistance)"],"output_types":["code (generated or completed code snippets)","text (explanations of generated code or debugging suggestions)"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__cap_8","uri":"capability://text.generation.language.few.shot.learning.with.in.context.examples.for.task.adaptation","name":"few-shot learning with in-context examples for task adaptation","description":"Gemma 2 can adapt to new tasks by including examples in the prompt (few-shot learning), where 2-5 examples of input-output pairs teach the model to perform classification, extraction, or generation tasks without fine-tuning. This works through the model's instruction-following capability and learned ability to recognize patterns from examples. The mechanism relies on the transformer's ability to attend to example patterns and apply them to new inputs, with performance improving as more examples are provided (up to a point where context becomes too long).","intents":["Adapt model to custom classification tasks (sentiment analysis, intent detection) with 3-5 examples","Perform data extraction from new document formats by showing examples","Implement zero-shot to few-shot task adaptation without model fine-tuning"],"best_for":["Teams needing rapid task adaptation without fine-tuning infrastructure","Applications with evolving task requirements that change frequently","Prototyping and experimentation phases before committing to fine-tuning"],"limitations":["Few-shot performance degrades with task complexity — works well for classification but poorly for complex reasoning","Example quality is critical — poor examples can mislead the model more than no examples","Context window limits the number of examples — typically 3-5 examples per task before running out of context","Performance is less stable than fine-tuned models — same task may produce different results with different example orderings"],"requires":["Inference framework supporting Gemma 2","High-quality examples representative of the task","Prompt engineering expertise to format examples effectively"],"input_types":["text (examples and new inputs to classify/extract)"],"output_types":["text (classifications, extractions, or generated outputs)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__cap_9","uri":"capability://code.generation.editing.open.source.weights.and.reproducible.training.for.research.and.customization","name":"open-source weights and reproducible training for research and customization","description":"Provides fully open-source model weights, training code, and documentation enabling researchers and developers to understand the model architecture, reproduce training procedures, and fine-tune for custom tasks. The model uses standard transformer architecture with published modifications (interleaved attention), allowing integration into existing ML frameworks and research pipelines. Open weights enable local deployment without API dependencies and support for custom quantization, pruning, and fine-tuning.","intents":["Fine-tune Gemma 2 on domain-specific data (medical, legal, technical) for specialized applications","Research model behavior, attention patterns, and reasoning mechanisms through weight inspection","Integrate Gemma 2 into custom ML pipelines and research frameworks","Create derivative models through pruning, quantization, or architectural modifications"],"best_for":["Researchers studying model behavior and training dynamics","Teams with domain-specific requirements needing fine-tuning","Organizations with strict IP or compliance requirements preventing cloud API use","Developers building custom inference optimizations or model modifications"],"limitations":["Fine-tuning requires significant compute resources — 24GB+ VRAM for full fine-tuning of 27B model","Training code and documentation are provided but may require ML expertise to understand and modify","No commercial support or SLA guarantees — community-driven support only","Reproducibility depends on exact hardware and software versions — may see variance across environments"],"requires":["ML framework (PyTorch, JAX, or TensorFlow) for training and fine-tuning","Model weights from Hugging Face Hub or Google's repository","For fine-tuning: 24GB+ VRAM for 27B, 12GB+ for 9B, 6GB+ for 2B","Training code and documentation from official repository"],"input_types":["text (training data for fine-tuning, model weights for analysis)"],"output_types":["text (fine-tuned model weights, analysis results, research findings)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"gemma-2__headline","uri":"capability://model.training.efficient.open.model.for.on.device.ai","name":"efficient open model for on-device ai","description":"Gemma 2 is a second-generation open model from Google designed for on-device AI and resource-constrained deployments, offering strong reasoning capabilities and efficient long-context processing.","intents":["best open model for on-device AI","open model for resource-constrained environments","Gemma 2 vs Llama 3 performance comparison","efficient models for long-context processing","open-source AI models for reasoning tasks"],"best_for":["on-device AI","resource-constrained deployments"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Transformer-compatible inference framework (vLLM, llama.cpp, Ollama, or similar)","Minimum 4GB VRAM for 9B model, 8GB for 27B model in 8-bit quantization","Support for Flash Attention v2 or equivalent for optimal performance","Inference framework supporting Gemma 2 (Hugging Face Transformers, vLLM, Ollama, etc.)","No special hardware required; standard GPU/CPU inference supported","Familiarity with prompt engineering for instruction-following models","No special requirements — benchmark results are published and publicly available","For custom evaluation: inference framework and benchmark implementation (HuggingFace evaluate library, etc.)","Inference framework supporting Gemma 2 (Hugging Face Transformers 4.42+, vLLM, Ollama, llama.cpp)","2B: 2GB VRAM minimum (4GB recommended)"],"failure_modes":["Local attention window size is fixed during training — cannot dynamically adjust window width at inference","Global attention layers still incur O(n²) cost for those specific layers, limiting true long-context scaling beyond 8K tokens","Interleaved pattern may reduce effectiveness for tasks requiring dense cross-document reasoning across all positions","Distilled knowledge is frozen at training time — cannot adapt to new information or domains without retraining","Performance gains are task-dependent; distillation works best for reasoning and instruction-following but may underperform on specialized domains not well-represented in Gemini training data","Reasoning transparency is reduced compared to larger models — distilled models may produce correct answers without showing intermediate reasoning steps","Benchmark performance doesn't always correlate with real-world application quality — may perform differently on custom tasks","Benchmarks may favor models trained on similar data — Gemma 2's performance on benchmarks may not reflect performance on novel domains","Benchmark scores are point-in-time measurements — model behavior may vary with different prompting strategies or input formats","No benchmarks for all relevant tasks — some specialized domains (medical, legal) may not have published benchmarks","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":"2026-05-24T12:16:21.549Z","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=gemma-2","compare_url":"https://unfragile.ai/compare?artifact=gemma-2"}},"signature":"ZXuZ/IkzaU8NuhCDKreigrQ303/SU+5lyxLGJkma5dTcBKqeppouKoPjkLFDeyTQw6hAbtEDXK55r8BTdIH1AQ==","signedAt":"2026-06-24T01:56:22.795Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/gemma-2","artifact":"https://unfragile.ai/gemma-2","verify":"https://unfragile.ai/api/v1/verify?slug=gemma-2","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"}}