{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-meta-llama--llama-cookbook","slug":"meta-llama--llama-cookbook","name":"llama-cookbook","type":"repo","url":"https://www.llama.com","page_url":"https://unfragile.ai/meta-llama--llama-cookbook","categories":["frameworks-sdks","rag-knowledge"],"tags":["ai","finetuning","langchain","llama","llama2","llm","machine-learning","python","pytorch","vllm"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-meta-llama--llama-cookbook__cap_0","uri":"capability://code.generation.editing.single.gpu.fine.tuning.with.peft.parameter.efficient.methods","name":"single-gpu fine-tuning with peft parameter-efficient methods","description":"Provides optimized fine-tuning workflows for Llama models on single GPU hardware using Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA and QLoRA. The implementation leverages HuggingFace's PEFT library integrated with PyTorch to reduce trainable parameters from millions to thousands while maintaining model quality, enabling developers to fine-tune on consumer-grade GPUs (8GB-24GB VRAM) without full model replication in memory.","intents":["Fine-tune Llama models on a single GPU without running out of memory","Reduce training time and computational cost for custom model adaptation","Adapt pre-trained Llama models to domain-specific tasks with limited hardware"],"best_for":["solo developers and small teams with single-GPU setups","researchers prototyping custom Llama adaptations on limited budgets","teams migrating from cloud fine-tuning to on-premise GPU infrastructure"],"limitations":["PEFT methods trade off some model expressiveness for parameter efficiency — typically 0.5-2% accuracy loss vs full fine-tuning depending on task","LoRA rank and alpha hyperparameters require manual tuning; no automated selection provided","Training speed is slower than multi-GPU distributed approaches — expect 2-5x longer wall-clock time for equivalent dataset sizes"],"requires":["Python 3.9+","PyTorch 2.0+","NVIDIA GPU with 8GB+ VRAM (16GB+ recommended for larger models)","HuggingFace transformers library 4.30+","PEFT library (peft>=0.4.0)"],"input_types":["JSON/CSV datasets with text fields","HuggingFace Dataset format","Custom Python iterables with instruction-response pairs"],"output_types":["LoRA adapter weights (safetensors format)","Training metrics (loss, validation accuracy)","Merged model checkpoints (optional full model export)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_1","uri":"capability://code.generation.editing.multi.gpu.distributed.fine.tuning.with.fsdp.orchestration","name":"multi-gpu distributed fine-tuning with fsdp orchestration","description":"Orchestrates fine-tuning across multiple GPUs using Fully Sharded Data Parallel (FSDP) training, a PyTorch native distributed training strategy that shards model parameters, gradients, and optimizer states across GPUs to enable training of large Llama models (70B+) that exceed single-GPU memory. The cookbook provides FSDP configuration templates, launch scripts, and gradient accumulation patterns that abstract away distributed training complexity while maintaining training stability and convergence.","intents":["Fine-tune large Llama models (70B parameters) across multi-GPU clusters","Scale fine-tuning from 2 GPUs to 8+ GPUs without rewriting training code","Reduce per-GPU memory footprint to enable larger batch sizes and faster convergence"],"best_for":["enterprise teams with multi-GPU infrastructure (A100, H100 clusters)","research labs training custom Llama variants on proprietary datasets","organizations requiring sub-24-hour fine-tuning turnaround for large models"],"limitations":["FSDP introduces 15-25% communication overhead due to all-gather operations between GPUs — requires high-bandwidth interconnect (NVLink preferred)","Debugging distributed training failures is significantly harder than single-GPU; requires understanding of NCCL error codes and rank-specific logging","FSDP checkpointing produces sharded weights that require special merging logic before inference — standard HuggingFace model loading won't work directly"],"requires":["Python 3.9+","PyTorch 2.0+ with NCCL backend","2+ NVIDIA GPUs (A100/H100 recommended for 70B models)","torchrun or torch.distributed launcher","CUDA 11.8+ and cuDNN 8.6+"],"input_types":["Distributed dataset shards (one per GPU or data-parallel replicas)","HuggingFace Dataset with streaming support","Custom IterableDataset implementations for dynamic batching"],"output_types":["FSDP sharded checkpoints (requires merging for inference)","Consolidated model weights (after post-training merge step)","Distributed training logs with per-rank metrics"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_10","uri":"capability://tool.use.integration.third.party.provider.integration.and.deployment","name":"third-party provider integration and deployment","description":"Provides integration patterns for deploying Llama models on managed inference platforms (vLLM, TGI, Replicate, Together AI) and frameworks (LangChain, LlamaIndex). The cookbook includes configuration templates for each provider, API client examples, and guidance on selecting providers based on cost, latency, and feature requirements. This enables developers to run Llama inference without managing infrastructure while maintaining code portability across providers.","intents":["Deploy Llama models on managed inference platforms without infrastructure management","Switch between inference providers (vLLM, TGI, cloud APIs) with minimal code changes","Integrate Llama with application frameworks (LangChain, LlamaIndex) for rapid development"],"best_for":["teams wanting managed Llama inference without DevOps overhead","developers building applications that need provider flexibility","organizations evaluating multiple inference platforms before committing"],"limitations":["Managed inference platforms add 50-200ms latency vs self-hosted due to network overhead — not suitable for sub-100ms SLAs","Provider-specific APIs differ in parameter naming and response formats — code portability requires abstraction layers","Cost per token varies significantly across providers (0.5-5x difference) — requires benchmarking for production workloads"],"requires":["Python 3.9+","API credentials for chosen provider (Together AI, Replicate, etc.)","Framework libraries (langchain, llama-index) for integration examples","HTTP client (requests, httpx) for direct API calls"],"input_types":["Text prompts","Model identifiers (provider-specific model names)","Inference parameters (temperature, max_tokens, top_p)","Chat messages (for chat-based APIs)"],"output_types":["Generated text","Token usage metrics (input/output tokens)","Latency measurements","Provider-specific metadata (model version, etc.)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_11","uri":"capability://safety.moderation.safety.guardrails.and.content.moderation.with.llama.guard","name":"safety guardrails and content moderation with llama guard","description":"Integrates Llama Guard, a specialized safety classifier, to filter unsafe inputs and outputs in Llama-powered applications. The cookbook provides patterns for input validation (detecting harmful requests before processing), output filtering (removing unsafe generated content), and safety policy configuration. Llama Guard uses a taxonomy of unsafe categories (violence, illegal activity, etc.) to classify content and enable developers to enforce safety policies without external moderation APIs.","intents":["Prevent harmful or unsafe requests from reaching Llama models","Filter unsafe generated content before returning to users","Implement content moderation policies without external API dependencies"],"best_for":["teams deploying Llama in production requiring safety compliance","organizations building public-facing Llama applications","developers implementing content policies without external moderation services"],"limitations":["Llama Guard classification accuracy is ~90% — false negatives (unsafe content classified as safe) occur in ~10% of cases","Safety taxonomy is predefined and not easily customizable — organizations with unique safety requirements may need fine-tuning","Llama Guard adds 50-100ms latency per request (input + output checking) — impacts response time SLAs"],"requires":["Python 3.9+","transformers 4.30+","Llama Guard model weights (meta-llama/LlamaGuard-7b)","8GB+ VRAM for Llama Guard inference"],"input_types":["User input text (to validate before processing)","Generated text from Llama (to validate before returning)","Safety policy configuration (category thresholds)"],"output_types":["Safety classification (safe/unsafe with category)","Confidence scores for classification","Filtered content (with unsafe portions removed or redacted)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_12","uri":"capability://text.generation.language.multilingual.inference.and.cross.lingual.understanding","name":"multilingual inference and cross-lingual understanding","description":"Demonstrates using Llama models for multilingual tasks including translation, cross-lingual question answering, and language-specific fine-tuning. The cookbook provides examples for prompting Llama in multiple languages, handling language detection, and evaluating multilingual performance. Llama models trained on diverse language corpora enable reasonable performance across 100+ languages without language-specific fine-tuning, though quality varies by language.","intents":["Build multilingual chatbots or assistants that handle user input in any language","Translate content between languages using Llama without external translation APIs","Answer questions in user's native language by leveraging Llama's multilingual capabilities"],"best_for":["teams building global applications requiring multilingual support","organizations reducing translation costs by using Llama instead of translation APIs","developers exploring cross-lingual transfer learning with Llama"],"limitations":["Llama multilingual performance degrades significantly for low-resource languages (e.g., Swahili, Tagalog) — 20-40% lower quality vs English","Translation quality is lower than specialized translation models (Google Translate, DeepL) — suitable for rough translations but not professional use","Language detection requires separate model or heuristics — Llama doesn't reliably identify language from text alone"],"requires":["Python 3.9+","transformers 4.30+","Language detection library (langdetect, textblob) for automatic language identification","Llama model with multilingual training (Llama 2, Llama 3 support 100+ languages)"],"input_types":["Text in any language","Language code (ISO 639-1) for explicit language specification","Multilingual prompts (code-switching examples)"],"output_types":["Generated text in target language","Detected language (if input language is unknown)","Translation results (source and target language)"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_2","uri":"capability://text.generation.language.local.inference.with.hardware.aware.model.loading.and.quantization","name":"local inference with hardware-aware model loading and quantization","description":"Enables running Llama models locally on consumer hardware (CPU, single GPU, or multi-GPU) with automatic hardware detection and quantization strategy selection. The implementation uses transformers library's device_map='auto' for memory-efficient loading, integrates bitsandbytes for 8-bit and 4-bit quantization, and provides fallback strategies (CPU offloading, Flash Attention) when VRAM is insufficient. Developers specify target hardware constraints and the system automatically selects optimal loading strategy without manual memory calculations.","intents":["Run Llama 7B-70B models on laptops or consumer GPUs without cloud inference costs","Deploy Llama models on edge devices with limited VRAM by applying quantization","Benchmark inference latency and throughput on local hardware before cloud deployment"],"best_for":["individual developers building local AI assistants or prototypes","teams evaluating Llama model quality before committing to cloud inference spend","edge deployment scenarios where cloud connectivity is unreliable or latency-sensitive"],"limitations":["Quantized models (4-bit, 8-bit) show 5-15% quality degradation vs full precision depending on task complexity and model size","Inference throughput on consumer GPUs (RTX 4090) is 10-50x slower than cloud TPU/A100 clusters — expect 1-5 tokens/second for 70B models","No built-in batching or request queuing — single-request inference only without external orchestration (vLLM, TGI)"],"requires":["Python 3.9+","transformers library 4.30+","torch 2.0+ with CUDA 11.8+ (for GPU) or CPU-only variant","bitsandbytes 0.40+ (for quantization)","8GB+ RAM for 7B models, 16GB+ for 13B, 40GB+ for 70B (unquantized)"],"input_types":["Text prompts (string)","Structured chat messages (list of dicts with role/content)","System prompts for instruction-following"],"output_types":["Generated text (string)","Token logits (optional, for sampling strategies)","Inference timing metrics (tokens/second)"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_3","uri":"capability://image.visual.multi.modal.inference.with.llama.3.2.vision.image.understanding","name":"multi-modal inference with llama 3.2 vision image understanding","description":"Extends text inference to support image inputs using Llama 3.2 Vision models, which embed vision encoders (CLIP-like architecture) alongside language models to process images and text jointly. The cookbook provides image loading utilities, prompt formatting for vision tasks (image captioning, visual question answering, document OCR), and integration patterns with common image sources (URLs, local files, base64 encoding). Inference handles variable image resolutions through dynamic patching and produces text outputs grounded in visual content.","intents":["Build image captioning or visual question-answering systems using Llama models","Extract structured data from documents, screenshots, or diagrams via vision-language understanding","Create multimodal chatbots that reason over both text and image inputs"],"best_for":["developers building document processing or OCR pipelines","teams creating visual search or image understanding features","researchers exploring vision-language model capabilities on Llama architecture"],"limitations":["Vision models require significantly more VRAM than text-only models — Llama 3.2 Vision needs 20GB+ for full precision vs 8GB for 7B text model","Image resolution affects inference latency non-linearly — high-res images (4K) can increase latency 3-5x vs standard 1024x1024","No built-in image preprocessing (cropping, resizing) — developers must handle image normalization and format conversion manually"],"requires":["Python 3.9+","transformers 4.40+","Llama 3.2 Vision model weights (requires Meta access or HuggingFace gated model)","Pillow 9.0+ for image loading","16GB+ VRAM for inference (24GB+ recommended)"],"input_types":["Image files (JPEG, PNG, WebP)","Image URLs (with automatic download)","Base64-encoded images","PIL Image objects","Text prompts describing vision tasks"],"output_types":["Text descriptions or answers grounded in image content","Structured extraction results (JSON for document parsing)","Confidence scores or reasoning traces (optional)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_4","uri":"capability://memory.knowledge.retrieval.augmented.generation.rag.with.vector.store.integration","name":"retrieval-augmented generation (rag) with vector store integration","description":"Implements RAG pipelines that augment Llama model generation with external knowledge by retrieving relevant documents from vector databases before generation. The cookbook provides patterns for document chunking, embedding generation (using Llama embeddings or third-party models), vector store integration (Chroma, Pinecone, Weaviate), and prompt augmentation that injects retrieved context into the LLM input. This enables Llama models to answer questions grounded in custom knowledge bases without fine-tuning.","intents":["Build question-answering systems over custom documents or knowledge bases","Reduce hallucination by grounding Llama responses in retrieved facts","Enable knowledge updates without retraining — add new documents to vector store dynamically"],"best_for":["teams building customer support chatbots with company-specific knowledge","organizations deploying Llama for document analysis or research assistance","developers creating fact-grounded AI assistants that cite sources"],"limitations":["Retrieval quality directly impacts generation quality — poor chunking or embedding models degrade RAG performance by 20-40% vs optimal setup","Vector store latency adds 100-500ms per query depending on database size and network — not suitable for sub-100ms response SLAs","No automatic handling of document updates — requires manual re-embedding and re-indexing when knowledge base changes"],"requires":["Python 3.9+","Vector database (Chroma, Pinecone, Weaviate, or Milvus)","Embedding model (sentence-transformers, Llama embeddings, or OpenAI API)","Document loader library (langchain, llama-index, or custom)","transformers 4.30+ for Llama inference"],"input_types":["Raw documents (PDF, TXT, Markdown)","Structured data (JSON, CSV with text fields)","Web URLs for crawling","User queries (natural language questions)"],"output_types":["Generated answers with source citations","Retrieved document chunks (for transparency)","Relevance scores for retrieved documents","Structured extraction from documents (optional)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_5","uri":"capability://data.processing.analysis.dataset.preparation.and.evaluation.for.fine.tuning","name":"dataset preparation and evaluation for fine-tuning","description":"Provides utilities and patterns for preparing training datasets and evaluating fine-tuned models, including data loading from multiple formats (JSON, CSV, HuggingFace Datasets), instruction-response pair formatting, train/validation splitting, and evaluation metrics (BLEU, ROUGE, perplexity). The cookbook includes dataset validation checks (duplicate detection, length distribution analysis) and integration with evaluation frameworks (lm-eval-harness) to benchmark fine-tuned models against standard benchmarks and baselines.","intents":["Convert raw data into instruction-response pairs suitable for Llama fine-tuning","Validate dataset quality and identify issues before training","Evaluate fine-tuned models on standard benchmarks to measure improvement"],"best_for":["data engineers preparing datasets for ML teams","researchers benchmarking Llama fine-tuning on custom tasks","teams implementing data quality gates before training expensive fine-tuning jobs"],"limitations":["No automated data cleaning — requires manual inspection and curation for domain-specific issues (e.g., PII removal, format inconsistencies)","Evaluation metrics (BLEU, ROUGE) are task-dependent and may not correlate with human judgment for open-ended generation tasks","Large-scale evaluation (>10K examples) requires significant compute — lm-eval-harness can take hours to complete on consumer hardware"],"requires":["Python 3.9+","pandas 1.5+","datasets library 2.10+","lm-eval-harness (for benchmark evaluation)","rouge-score, nltk (for metrics)"],"input_types":["JSON files with instruction/input/output fields","CSV files with text columns","HuggingFace Dataset objects","Custom Python iterables"],"output_types":["Formatted training datasets (JSON or HuggingFace format)","Data quality reports (duplicates, length statistics)","Evaluation metrics (BLEU, ROUGE, perplexity scores)","Benchmark comparison tables"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_6","uri":"capability://code.generation.editing.quantization.strategies.for.model.compression.and.deployment","name":"quantization strategies for model compression and deployment","description":"Demonstrates multiple quantization approaches (4-bit, 8-bit, GPTQ, AWQ) to reduce model size and inference latency while maintaining quality. The cookbook provides quantization configuration templates, post-training quantization workflows, and guidance on selecting quantization strategies based on hardware constraints and quality requirements. Quantized models are 4-8x smaller and enable inference on consumer GPUs or edge devices that cannot fit full-precision models.","intents":["Reduce model size from 140GB (70B fp32) to 17GB (70B 4-bit) for deployment on resource-constrained hardware","Speed up inference latency by 2-3x through reduced memory bandwidth requirements","Enable Llama model deployment on edge devices or mobile platforms"],"best_for":["teams deploying Llama models on edge devices or embedded systems","organizations optimizing inference costs by reducing GPU memory requirements","developers building latency-sensitive applications requiring sub-100ms response times"],"limitations":["4-bit quantization causes 5-15% accuracy loss on reasoning tasks — not suitable for math or code generation without fine-tuning","Quantization is typically post-training and irreversible — requires retraining to recover lost quality","Different quantization methods (GPTQ vs AWQ vs bitsandbytes) are not interchangeable — model selection is hardware-specific"],"requires":["Python 3.9+","transformers 4.30+","bitsandbytes 0.40+ (for 4-bit/8-bit) or auto-gptq (for GPTQ)","Original model weights (fp32 or fp16)","Calibration dataset (optional, for post-training quantization)"],"input_types":["Full-precision model weights (safetensors or PyTorch format)","Quantization configuration (bits, group_size, desc_act)","Calibration dataset (optional, for better accuracy)"],"output_types":["Quantized model weights (4-bit or 8-bit)","Quantization metadata (scale factors, zero points)","Quality metrics (perplexity before/after quantization)"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_7","uri":"capability://planning.reasoning.end.to.end.chatbot.and.agent.applications","name":"end-to-end chatbot and agent applications","description":"Provides complete working examples of chatbot and agentic systems built with Llama, including multi-turn conversation management, tool calling for function execution, and integration with external services (email, messaging platforms, APIs). The cookbook includes prompt engineering patterns for agent reasoning, memory management for conversation history, and deployment templates for platforms like WhatsApp, Messenger, and Slack. These examples demonstrate how to compose Llama inference with orchestration logic to build autonomous agents.","intents":["Build customer service chatbots that handle multi-turn conversations with context awareness","Create autonomous agents that call external tools (APIs, databases) to complete tasks","Deploy Llama-powered assistants on messaging platforms (WhatsApp, Slack, Discord)"],"best_for":["teams building customer support automation with Llama","developers creating AI agents for internal tools or workflows","organizations deploying conversational AI on messaging platforms"],"limitations":["Multi-turn conversation management requires external state storage (database, Redis) — no built-in persistence in cookbook examples","Tool calling reliability depends on prompt quality and model instruction-following — hallucinated function calls require validation and error handling","Conversation context grows unbounded — requires manual context windowing or summarization to prevent token limit exceeded errors"],"requires":["Python 3.9+","transformers 4.30+ for Llama inference","FastAPI or Flask for API endpoints","External service credentials (WhatsApp Business API, Slack API, etc.)","Database for conversation history (PostgreSQL, MongoDB, or in-memory for prototypes)"],"input_types":["User messages (text from chat platforms)","Conversation history (previous turns)","Tool definitions (function signatures for agent to call)","System prompts (agent behavior instructions)"],"output_types":["Agent responses (text messages)","Tool calls (function names and arguments)","Conversation state (updated context for next turn)","Structured actions (API calls, database queries)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_8","uri":"capability://code.generation.editing.text.to.sql.and.code.generation.with.llama","name":"text-to-sql and code generation with llama","description":"Demonstrates using Llama models to generate SQL queries from natural language questions and code from specifications. The cookbook provides prompt engineering patterns for SQL generation (schema context, query validation), code generation (language-specific formatting, syntax checking), and integration with execution environments for validation. These examples show how to use Llama as a code/SQL generator with feedback loops that validate generated code before execution.","intents":["Build natural language interfaces to databases — convert user questions to SQL queries","Generate code snippets or functions from natural language specifications","Create code assistants that understand context and generate syntactically correct code"],"best_for":["teams building natural language database query interfaces","developers creating code generation features for IDEs or documentation tools","organizations automating code generation from specifications"],"limitations":["Generated SQL/code requires validation before execution — Llama frequently generates syntactically correct but semantically incorrect queries (e.g., wrong table joins)","Complex schema understanding requires extensive prompt engineering — large schemas (100+ tables) exceed context windows or degrade quality","Code generation quality degrades significantly for languages outside training data (e.g., Rust, Go) — Python/JavaScript generation is most reliable"],"requires":["Python 3.9+","transformers 4.30+ for Llama inference","Database connection library (sqlalchemy, psycopg2) for SQL validation","Code parser/linter (ast, pylint) for code validation","Database schema information (DDL statements or introspection)"],"input_types":["Natural language questions or specifications","Database schema (DDL or introspection results)","Code context (existing functions, imports, type hints)","Execution feedback (error messages from failed queries/code)"],"output_types":["Generated SQL queries","Generated code (Python, JavaScript, SQL, etc.)","Validation results (syntax errors, type mismatches)","Execution results (query output or code execution traces)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-meta-llama--llama-cookbook__cap_9","uri":"capability://planning.reasoning.github.issue.triage.and.automation.with.llama.agents","name":"github issue triage and automation with llama agents","description":"Provides an end-to-end example of using Llama agents to automatically triage GitHub issues by analyzing issue descriptions, assigning labels, suggesting assignees, and generating responses. The implementation uses GitHub API integration, issue text analysis with Llama, and tool calling to perform actions (label assignment, comment posting). This demonstrates how to build autonomous agents that interact with external platforms and make decisions based on LLM reasoning.","intents":["Automate GitHub issue triage to reduce manual labeling and routing overhead","Generate intelligent issue responses or summaries using Llama understanding","Build autonomous agents that interact with GitHub API based on LLM reasoning"],"best_for":["open-source maintainers managing high-volume issue streams","teams automating internal issue tracking workflows","developers building GitHub automation tools powered by LLMs"],"limitations":["Issue classification accuracy depends on training data — custom label schemes require fine-tuning or few-shot examples","GitHub API rate limits (60 requests/hour unauthenticated) constrain batch processing — requires careful request batching","Agent decisions (label assignment, assignee suggestion) may be incorrect — requires human review or confidence thresholds before automation"],"requires":["Python 3.9+","transformers 4.30+ for Llama inference","PyGithub or github3.py for GitHub API integration","GitHub personal access token with repo:read and issues:write permissions","Llama model with instruction-following capability (7B+ recommended)"],"input_types":["GitHub issue objects (title, description, labels, author)","Issue history (comments, state changes)","Repository context (README, contributing guidelines)","Label taxonomy (available labels for assignment)"],"output_types":["Suggested labels (with confidence scores)","Suggested assignees (from team members)","Generated issue responses or summaries","Triage decisions (priority, category)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","PyTorch 2.0+","NVIDIA GPU with 8GB+ VRAM (16GB+ recommended for larger models)","HuggingFace transformers library 4.30+","PEFT library (peft>=0.4.0)","PyTorch 2.0+ with NCCL backend","2+ NVIDIA GPUs (A100/H100 recommended for 70B models)","torchrun or torch.distributed launcher","CUDA 11.8+ and cuDNN 8.6+","API credentials for chosen provider (Together AI, Replicate, etc.)"],"failure_modes":["PEFT methods trade off some model expressiveness for parameter efficiency — typically 0.5-2% accuracy loss vs full fine-tuning depending on task","LoRA rank and alpha hyperparameters require manual tuning; no automated selection provided","Training speed is slower than multi-GPU distributed approaches — expect 2-5x longer wall-clock time for equivalent dataset sizes","FSDP introduces 15-25% communication overhead due to all-gather operations between GPUs — requires high-bandwidth interconnect (NVLink preferred)","Debugging distributed training failures is significantly harder than single-GPU; requires understanding of NCCL error codes and rank-specific logging","FSDP checkpointing produces sharded weights that require special merging logic before inference — standard HuggingFace model loading won't work directly","Managed inference platforms add 50-200ms latency vs self-hosted due to network overhead — not suitable for sub-100ms SLAs","Provider-specific APIs differ in parameter naming and response formats — code portability requires abstraction layers","Cost per token varies significantly across providers (0.5-5x difference) — requires benchmarking for production workloads","Llama Guard classification accuracy is ~90% — false negatives (unsafe content classified as safe) occur in ~10% of cases","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7362336200355962,"quality":0.5,"ecosystem":0.75,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-05-24T12:16:22.062Z","last_scraped_at":"2026-05-03T13:58:24.502Z","last_commit":"2026-04-21T21:24:26Z"},"community":{"stars":18312,"forks":2724,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=meta-llama--llama-cookbook","compare_url":"https://unfragile.ai/compare?artifact=meta-llama--llama-cookbook"}},"signature":"vnGvCpWRzxdNYnbqNcmA4fLQzVui3Nmo0aflWBlLhrIxBvv0r5Xj9aH9rUn5gWyogp8FD3GymqLonqx6fTuECA==","signedAt":"2026-06-23T00:35:27.996Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama--llama-cookbook","artifact":"https://unfragile.ai/meta-llama--llama-cookbook","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama--llama-cookbook","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"}}