{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-mlabonne--llm-course","slug":"mlabonne--llm-course","name":"llm-course","type":"model","url":"https://mlabonne.github.io/blog/","page_url":"https://unfragile.ai/mlabonne--llm-course","categories":["chatbots-assistants","research-search"],"tags":["course","large-language-models","llm","machine-learning","roadmap"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"pending_review","verified":false},"capabilities":[{"id":"github-mlabonne--llm-course__cap_0","uri":"capability://memory.knowledge.structured.learning.roadmap.navigation","name":"structured-learning-roadmap-navigation","description":"Organizes LLM education into three progressive learning tracks (Fundamentals, Scientist, Engineer) with explicit entry points and dependency mapping, implemented as a single markdown hub that links to ~150+ external resources. Users navigate via a hierarchical section structure that maps learning paths to specific topics, with each topic following a consistent pattern of curated articles, videos, and tools. The architecture uses a documentation-first approach where the README.md acts as a central knowledge graph rather than containing executable code.","intents":["I want to learn LLMs but don't know where to start based on my background","I need a structured curriculum that shows prerequisites and dependencies between topics","I'm looking for a single source of truth that aggregates the best LLM learning materials"],"best_for":["self-taught developers transitioning into LLM engineering","ML practitioners wanting a structured path from theory to production","teams building internal LLM knowledge bases"],"limitations":["No interactive quizzes or progress tracking — purely reference-based navigation","Requires external tool access (Colab, GitHub) to execute notebooks","Content updates depend on manual curation; no automated resource discovery"],"requires":["Web browser to access GitHub README","Google account for Colab notebook execution","Basic familiarity with markdown navigation"],"input_types":["user intent (e.g., 'I want to fine-tune models')","background level (fundamentals vs. advanced)"],"output_types":["curated reading list","notebook links","external resource URLs"],"categories":["memory-knowledge","learning-curriculum"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_1","uri":"capability://memory.knowledge.theoretical.topic.curation.with.external.linking","name":"theoretical-topic-curation-with-external-linking","description":"Aggregates 24 theoretical topics across three learning paths and embeds curated external references (articles, papers, videos, tools) directly within each topic section. Implementation uses a consistent topic section pattern where each topic links to 3-8 external resources selected for pedagogical value. The curation layer filters and organizes content from diverse sources (research papers, blog posts, YouTube, GitHub projects) into a single navigable structure without duplicating content.","intents":["I need to understand transformer architecture but want multiple explanations from different sources","I'm researching quantization techniques and want curated papers plus practical tools","I want to learn about RAG but need both theory and implementation examples"],"best_for":["researchers wanting a curated bibliography for LLM topics","educators building course materials from vetted sources","practitioners needing quick access to both theory and tools"],"limitations":["No quality scoring or difficulty ratings for external resources — all links treated equally","Curation is manual and static; no dynamic ranking based on community feedback","External links may break or become outdated without automated monitoring"],"requires":["Internet access to follow external links","Ability to parse markdown and navigate GitHub","Familiarity with academic paper formats (arXiv, PDF)"],"input_types":["topic name (e.g., 'Pre-Training Models')","learning level (fundamentals vs. advanced)"],"output_types":["curated resource list","external URLs","tool/framework recommendations"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_10","uri":"capability://memory.knowledge.rag.and.vector.storage.architecture.guidance","name":"rag-and-vector-storage-architecture-guidance","description":"Provides educational content on Retrieval Augmented Generation (RAG) and vector storage systems, covering vector databases (Pinecone, Weaviate, Milvus), embedding models, retrieval strategies, and advanced RAG techniques (re-ranking, query expansion, hybrid search). Content is organized as two dedicated sections within the LLM Engineer track and links to vector database documentation, embedding model resources, and RAG frameworks (LangChain, LlamaIndex). This capability enables practitioners to build knowledge-grounded LLM applications without fine-tuning.","intents":["I want to build a RAG system that retrieves relevant documents before generating responses","I need to choose a vector database for my application and understand trade-offs","I'm implementing advanced RAG techniques like re-ranking and query expansion"],"best_for":["teams building knowledge-grounded chatbots and Q&A systems","practitioners wanting to add domain knowledge to LLMs without fine-tuning","engineers optimizing retrieval quality and latency"],"limitations":["RAG quality depends heavily on embedding model and retrieval strategy; no one-size-fits-all solution","Vector databases add operational complexity; requires monitoring and maintenance","Advanced RAG techniques (re-ranking, query expansion) add latency; trade-off with quality must be tuned"],"requires":["Understanding of embeddings and vector similarity","Access to vector database (cloud or self-hosted)","Document corpus to index","Embedding model (open-source or API-based)"],"input_types":["document corpus (PDF, text, web pages)","query (user question or prompt)","retrieval strategy preference (dense, sparse, hybrid)"],"output_types":["retrieved documents","augmented prompt for LLM","final generated response","retrieval metrics (precision, recall)"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_11","uri":"capability://planning.reasoning.llm.agents.and.tool.orchestration.guidance","name":"llm-agents-and-tool-orchestration-guidance","description":"Provides educational content on building LLM agents that can plan, reason, and use tools to accomplish complex tasks. Content covers agent architectures (ReAct, Chain-of-Thought), tool calling and function schemas, planning strategies, and agent frameworks (LangChain, AutoGPT, CrewAI). This capability is organized as a dedicated section within the LLM Engineer track and links to agent research papers, framework documentation, and implementation examples. Enables practitioners to build autonomous systems that go beyond simple prompt-response interactions.","intents":["I want to build an agent that can break down complex tasks and use tools to solve them","I need to understand how to implement tool calling and function schemas for agent-LLM interaction","I'm building a multi-agent system and need guidance on orchestration and communication patterns"],"best_for":["teams building autonomous AI systems and workflows","practitioners implementing complex task automation","engineers building multi-agent systems for enterprise applications"],"limitations":["Agent reliability depends on LLM reasoning quality; hallucinations and planning errors are common","Tool calling requires careful schema design and error handling; poorly designed tools lead to agent failures","Multi-agent systems add complexity; debugging and monitoring become challenging"],"requires":["Understanding of LLM capabilities and limitations","Familiarity with tool/function calling APIs","Knowledge of planning and reasoning algorithms","Agent framework experience (LangChain, AutoGPT, etc.)"],"input_types":["task description (e.g., 'research and summarize a topic')","available tools (function schemas, API endpoints)","agent constraints (max steps, timeout)"],"output_types":["agent action plan","tool calls and results","final response","execution trace and reasoning steps"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_12","uri":"capability://automation.workflow.inference.optimization.and.serving.strategies","name":"inference-optimization-and-serving-strategies","description":"Provides educational content on optimizing LLM inference for latency and throughput, covering techniques like batching, caching, quantization, and serving frameworks (vLLM, TensorRT-LLM, Ollama). Content is organized as a dedicated section within the LLM Engineer track and links to optimization papers, serving framework documentation, and performance benchmarks. This capability enables practitioners to deploy models efficiently and meet production latency/throughput requirements.","intents":["I need to reduce inference latency for my LLM application to meet SLA requirements","I want to understand batching, caching, and other optimization techniques for serving","I'm choosing a serving framework and need to understand trade-offs (vLLM vs. TensorRT vs. Ollama)"],"best_for":["DevOps/MLOps engineers optimizing model serving","teams deploying LLMs at scale","practitioners needing sub-second latency for real-time applications"],"limitations":["Optimization techniques are often framework-specific; no universal approach","Batching and caching add complexity; requires careful tuning for different workloads","Hardware-specific optimizations (GPU, TPU) may not transfer across platforms"],"requires":["Understanding of inference pipelines and bottlenecks","Familiarity with serving frameworks (vLLM, TensorRT, Ollama)","Access to production hardware for benchmarking","Knowledge of performance profiling tools"],"input_types":["model size and architecture","target latency and throughput SLAs","hardware specifications","workload characteristics (batch size, sequence length)"],"output_types":["optimization recommendations","performance benchmarks","serving configuration","cost-benefit analysis"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_13","uri":"capability://automation.workflow.llm.deployment.and.infrastructure.patterns","name":"llm-deployment-and-infrastructure-patterns","description":"Provides educational content on deploying LLMs to production, covering containerization (Docker), orchestration (Kubernetes), cloud platforms (AWS, GCP, Azure), monitoring, and operational considerations. Content is organized as a dedicated section within the LLM Engineer track and links to deployment frameworks, cloud documentation, and best practices. This capability enables practitioners to move models from development to production with proper infrastructure, monitoring, and reliability patterns.","intents":["I need to deploy an LLM to production and want to understand containerization and orchestration","I'm choosing a cloud platform for LLM deployment and need to understand trade-offs","I want to set up monitoring and alerting for my deployed LLM application"],"best_for":["DevOps/MLOps engineers deploying LLMs to production","teams building LLM-powered products","organizations migrating from API-based LLMs to self-hosted models"],"limitations":["Deployment patterns are cloud/infrastructure-specific; no universal approach","Monitoring and observability add operational overhead; requires expertise in logging and metrics","Cost optimization requires careful tuning of compute resources; over-provisioning is common"],"requires":["Docker and container experience","Kubernetes or cloud platform knowledge","Understanding of CI/CD pipelines","Monitoring and observability tools experience"],"input_types":["model size and serving framework","target deployment environment (cloud, on-prem)","scalability and reliability requirements","budget constraints"],"output_types":["deployment architecture","Dockerfile and Kubernetes manifests","monitoring and alerting configuration","cost estimates"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_14","uri":"capability://safety.moderation.llm.security.and.safety.considerations","name":"llm-security-and-safety-considerations","description":"Provides educational content on securing LLM applications and addressing safety concerns, covering prompt injection attacks, data privacy, model poisoning, adversarial robustness, and compliance considerations. Content is organized as a dedicated section within the LLM Engineer track and links to security research, safety frameworks, and best practices. This capability enables practitioners to build LLM applications with appropriate security and safety guardrails.","intents":["I need to protect my LLM application from prompt injection and adversarial attacks","I want to understand data privacy and compliance requirements for LLM applications","I'm building a safety-critical LLM application and need to implement guardrails"],"best_for":["security engineers building LLM applications","teams handling sensitive data with LLMs","organizations with compliance requirements (HIPAA, GDPR, SOC2)"],"limitations":["LLM security is an emerging field; best practices are still evolving","No universal security solution; threat model depends on specific application","Safety guardrails may reduce model capability; trade-off between safety and utility must be tuned"],"requires":["Understanding of LLM capabilities and limitations","Security and threat modeling knowledge","Familiarity with compliance frameworks (HIPAA, GDPR, etc.)","Experience with security testing and red-teaming"],"input_types":["application type (chatbot, Q&A, code generation)","threat model (prompt injection, data leakage, etc.)","compliance requirements","user base (internal, external, sensitive)"],"output_types":["security architecture","threat model and mitigation strategies","guardrail implementation","compliance checklist"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_15","uri":"capability://data.processing.analysis.evaluation.and.benchmarking.frameworks","name":"evaluation-and-benchmarking-frameworks","description":"Provides educational content on evaluating LLM quality and performance, covering automatic metrics (BLEU, ROUGE, BERTScore), human evaluation, benchmarks (MMLU, HellaSwag, TruthfulQA), and evaluation frameworks. Content is organized as a dedicated section within the LLM Scientist track and links to evaluation papers, benchmark datasets, and evaluation tools. This capability enables practitioners to measure model quality and compare different models or training approaches.","intents":["I need to evaluate my fine-tuned model and want to understand which metrics are appropriate","I want to benchmark my model against standard LLM benchmarks (MMLU, HellaSwag, etc.)","I'm comparing different models and need a framework for fair evaluation"],"best_for":["ML researchers evaluating model quality","teams comparing different models or training approaches","practitioners wanting to measure progress during model development"],"limitations":["Automatic metrics don't always correlate with human perception; human evaluation is often necessary","Benchmarks may not reflect real-world performance; domain-specific evaluation is important","Evaluation is time-consuming and expensive; requires careful selection of metrics and benchmarks"],"requires":["Understanding of evaluation metrics and their limitations","Access to benchmark datasets (MMLU, HellaSwag, etc.)","Evaluation framework (e.g., EleutherAI Harness, LM Evaluation Harness)","Compute for running evaluations"],"input_types":["model to evaluate","benchmark selection (MMLU, HellaSwag, TruthfulQA, etc.)","evaluation metrics (automatic, human, custom)"],"output_types":["evaluation scores and metrics","benchmark results","comparison reports","analysis and insights"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_16","uri":"capability://memory.knowledge.new.trends.and.emerging.techniques.curation","name":"new-trends-and-emerging-techniques-curation","description":"Provides curated content on emerging LLM techniques and research trends, covering recent advances in model architecture, training methods, and applications. Content is organized as a dedicated section within the LLM Scientist track and links to recent research papers, blog posts, and tools implementing new techniques. This capability enables practitioners to stay current with rapidly evolving LLM field and understand cutting-edge approaches.","intents":["I want to understand recent LLM advances and how they might apply to my work","I'm researching emerging techniques like mixture of experts, sparse models, or multimodal LLMs","I need to stay current with LLM research and understand what's new in the field"],"best_for":["researchers staying current with LLM advances","practitioners evaluating new techniques for their applications","teams wanting to adopt cutting-edge approaches"],"limitations":["New trends change rapidly; content may become outdated quickly","Emerging techniques may not be production-ready; adoption requires careful evaluation","Curation is manual; no automated discovery of new trends"],"requires":["Understanding of LLM fundamentals","Ability to read and understand research papers","Time to evaluate and experiment with new techniques"],"input_types":["research interest (e.g., 'mixture of experts', 'multimodal LLMs')","application domain"],"output_types":["research papers on new techniques","implementation examples","tool and framework recommendations","adoption guidance"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_2","uri":"capability://code.generation.editing.hands.on.colab.notebook.integration","name":"hands-on-colab-notebook-integration","description":"Provides 23 executable Jupyter notebooks hosted on Google Colab that implement theoretical concepts from the course, organized into four categories: Automated Tools (8), Fine-tuning (6), Quantization (4), and Advanced Techniques (5). Notebooks are embedded as links within relevant course sections, creating a tight coupling between theory and practice. Each notebook implements specific techniques (e.g., LoRA fine-tuning, GGUF quantization, model merging) with runnable code that requires only a Google account and GPU access.","intents":["I want to fine-tune a model but need a working code example I can run immediately","I need to quantize a model for deployment and want a step-by-step notebook","I'm learning about model merging and want to see it in action without setting up local infrastructure"],"best_for":["practitioners wanting quick-start implementations without local setup","students learning by doing with GPU-free execution (Colab free tier)","teams prototyping LLM techniques before production implementation"],"limitations":["Colab free tier has GPU time limits and session timeouts; long-running training may fail","Notebooks are static snapshots; library updates may break code without maintenance","No version control or branching for notebook variants; all users see same implementation"],"requires":["Google account for Colab access","GPU quota (free tier: 12-24 hours/week)","Familiarity with Jupyter notebook interface","HuggingFace API key for model access (optional, for some notebooks)"],"input_types":["model identifier (e.g., 'meta-llama/Llama-2-7b')","dataset (CSV, JSON, or HuggingFace dataset ID)","hyperparameters (learning rate, batch size, epochs)"],"output_types":["fine-tuned model weights","quantized model (GGUF, GPTQ format)","evaluation metrics","merged model checkpoint"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_3","uri":"capability://memory.knowledge.llm.fundamentals.prerequisite.track","name":"llm-fundamentals-prerequisite-track","description":"Provides an optional foundational learning path covering Mathematics for Machine Learning, Python for Machine Learning, Neural Networks, and Natural Language Processing. This track is marked as optional (not required for advanced learners) and spans lines 74-157 of the README, serving as a prerequisite for both Scientist and Engineer tracks. Implementation uses a modular topic structure where each fundamental topic links to external resources (textbooks, courses, tutorials) rather than providing original content.","intents":["I'm new to ML and need to build foundational math and coding skills before learning LLMs","I want to refresh my understanding of neural networks before diving into transformers","I need to learn Python for ML but want resources curated for LLM practitioners"],"best_for":["career changers without ML background entering LLM field","students wanting to strengthen fundamentals before advanced topics","self-taught developers needing structured math and coding prerequisites"],"limitations":["Optional track means no enforcement of prerequisites; learners may skip and struggle later","Content is entirely external links; no original explanations or examples","No assessment mechanism to verify prerequisite mastery before advancing"],"requires":["High school level math (algebra, calculus basics)","Any programming experience (not necessarily Python)","Time commitment: 40-80 hours for full fundamentals track"],"input_types":["learner background (e.g., 'I know Python but not math')","time availability"],"output_types":["curated resource list for each fundamental topic","external course links","practice problem recommendations"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_4","uri":"capability://memory.knowledge.llm.scientist.research.and.training.track","name":"llm-scientist-research-and-training-track","description":"Provides a core learning path (8 topics, lines 159-304) focused on building and training LLMs from scratch, covering LLM Architecture, Pre-Training Models, Post-Training Datasets, Supervised Fine-Tuning, Preference Alignment, Evaluation, Quantization, and New Trends. This track is designed for researchers and practitioners wanting to understand model internals and training pipelines. Implementation uses the same topic-curation pattern as Fundamentals but with deeper technical content (papers, research blogs, training frameworks).","intents":["I want to understand how to pre-train an LLM from scratch and what datasets are needed","I need to implement preference alignment (RLHF, DPO) and want research papers plus code examples","I'm researching quantization techniques and want both theoretical understanding and practical notebooks"],"best_for":["ML researchers building custom LLMs","teams training proprietary models","practitioners wanting deep understanding of model internals before deployment"],"limitations":["Requires significant ML background; assumes familiarity with neural networks and transformers","Pre-training resources focus on theory; actual pre-training requires massive compute (100+ GPUs)","Some advanced topics (e.g., new trends) may become outdated quickly"],"requires":["Completion of Fundamentals track (or equivalent ML knowledge)","Understanding of transformer architecture","Access to research papers (arXiv, academic databases)","GPU compute for hands-on experiments (optional but recommended)"],"input_types":["research question (e.g., 'How do I implement DPO?')","model architecture choice (e.g., decoder-only vs. encoder-decoder)"],"output_types":["research papers and technical blogs","training code examples","evaluation frameworks","quantization notebooks"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_5","uri":"capability://automation.workflow.llm.engineer.production.and.deployment.track","name":"llm-engineer-production-and-deployment-track","description":"Provides a core learning path (8 topics, lines 305-440) focused on deploying and operating LLMs in production, covering Running LLMs, Vector Storage, Retrieval Augmented Generation (RAG), Advanced RAG, Agents, Inference Optimization, Deployment, and Security. This track is designed for engineers building LLM applications and systems. Implementation uses the same topic-curation pattern but emphasizes tools, frameworks, and operational concerns over research papers.","intents":["I need to deploy an LLM to production and want to understand inference optimization and serving options","I'm building a RAG system and need to understand vector storage, retrieval, and advanced techniques","I want to build LLM agents and need to understand tool calling, planning, and orchestration"],"best_for":["full-stack engineers building LLM applications","DevOps/MLOps teams deploying models to production","startups building LLM-powered products","teams migrating from API-based LLMs to self-hosted models"],"limitations":["Assumes understanding of LLM fundamentals; not suitable for complete beginners","Deployment resources are tool-specific; no universal deployment patterns","Security resources may not cover all compliance requirements (HIPAA, SOC2, etc.)"],"requires":["Understanding of LLM basics (from Fundamentals track)","Familiarity with Docker, Kubernetes, or cloud platforms (AWS/GCP/Azure)","Experience with Python and backend development","Access to deployment infrastructure (cloud account or on-prem servers)"],"input_types":["model size and latency requirements","deployment environment (cloud, on-prem, edge)","application type (chatbot, RAG, agent)"],"output_types":["deployment architecture recommendations","tool/framework selections","optimization strategies","security checklists"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_6","uri":"capability://memory.knowledge.transformer.architecture.educational.content","name":"transformer-architecture-educational-content","description":"Provides comprehensive educational material on transformer architecture fundamentals, covering decoder-only architectures (GPT, Llama, Mistral), tokenization methods, attention mechanism variants, and text generation strategies. Content is organized as a dedicated section within the LLM Scientist track and links to foundational papers (Attention is All You Need), implementation guides, and visual explanations. This capability serves as the architectural foundation for understanding all downstream topics (pre-training, fine-tuning, quantization).","intents":["I need to understand how transformers work before learning about fine-tuning or quantization","I want to learn the difference between encoder-decoder and decoder-only architectures","I'm implementing a custom LLM and need to understand attention mechanisms and tokenization"],"best_for":["ML engineers building LLM systems","researchers implementing custom transformer variants","students learning deep learning fundamentals"],"limitations":["Content is curated links; no original implementations or interactive visualizations","Assumes familiarity with neural networks and linear algebra","Advanced topics (e.g., sparse attention, mixture of experts) may be underrepresented"],"requires":["Understanding of neural networks and backpropagation","Linear algebra knowledge (matrices, attention computation)","Familiarity with Python and PyTorch (for implementation examples)"],"input_types":["architecture question (e.g., 'How does attention work?')","use case (e.g., 'I'm building a decoder-only model')"],"output_types":["research papers","implementation guides","visual explanations","code examples"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_7","uri":"capability://memory.knowledge.pre.training.and.dataset.curation.guidance","name":"pre-training-and-dataset-curation-guidance","description":"Provides educational content on pre-training LLMs from scratch and curating post-training datasets, covering model initialization, training objectives (next-token prediction, masked language modeling), dataset composition, and scaling laws. Content is organized as two dedicated sections within the LLM Scientist track and links to research papers (Chinchilla, Scaling Laws), dataset resources (Common Crawl, Wikipedia), and training frameworks (Hugging Face Transformers, Megatron). This capability bridges architecture understanding with practical training considerations.","intents":["I want to pre-train a model from scratch and need to understand dataset requirements and scaling","I'm building a domain-specific LLM and need guidance on dataset composition and quality","I need to understand the trade-offs between model size, training data, and compute budget"],"best_for":["teams building proprietary LLMs","researchers studying scaling laws and training dynamics","organizations with domain-specific data wanting custom models"],"limitations":["Pre-training requires massive compute (100+ GPUs, weeks of training); not accessible to individuals","Dataset curation is highly domain-specific; general guidance may not apply to specialized use cases","Content focuses on theory; actual implementation requires distributed training expertise"],"requires":["Understanding of transformer architecture","Access to large-scale compute infrastructure","Expertise in distributed training and data engineering","Large, high-quality datasets (billions of tokens)"],"input_types":["target model size (7B, 13B, 70B parameters)","domain (general, code, domain-specific)","compute budget (GPU hours, cost)"],"output_types":["dataset composition recommendations","scaling law calculations","training pipeline architecture","research papers on training dynamics"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_8","uri":"capability://code.generation.editing.fine.tuning.and.preference.alignment.implementation","name":"fine-tuning-and-preference-alignment-implementation","description":"Provides educational content and 6 executable notebooks on supervised fine-tuning (SFT) and preference alignment techniques (RLHF, DPO, IPO). Content covers fine-tuning methodologies, dataset preparation, and alignment algorithms, with notebooks implementing LoRA fine-tuning, full fine-tuning, and preference alignment on Colab. This capability enables practitioners to adapt pre-trained models to specific tasks and align outputs with human preferences without requiring massive compute.","intents":["I want to fine-tune a model on my custom dataset and need a working implementation","I need to align a model with human preferences using RLHF or DPO and want step-by-step guidance","I'm building a domain-specific assistant and need to understand fine-tuning trade-offs (LoRA vs. full)"],"best_for":["practitioners adapting pre-trained models to specific tasks","teams building domain-specific assistants","researchers experimenting with alignment techniques"],"limitations":["Notebooks use Colab free tier; large-scale fine-tuning may require paid compute","LoRA fine-tuning trades off quality for efficiency; full fine-tuning may overfit on small datasets","Preference alignment (RLHF) requires human feedback data; DPO is newer and less battle-tested"],"requires":["Pre-trained model (e.g., Llama 2, Mistral)","Custom dataset (CSV, JSON, or HuggingFace format)","Google Colab account with GPU quota","HuggingFace API key for model access"],"input_types":["base model identifier","training dataset (instruction-response pairs)","hyperparameters (learning rate, epochs, LoRA rank)"],"output_types":["fine-tuned model weights","training logs and metrics","evaluation results","merged model checkpoint"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-mlabonne--llm-course__cap_9","uri":"capability://automation.workflow.quantization.techniques.and.optimization","name":"quantization-techniques-and-optimization","description":"Provides educational content and 4 executable notebooks on quantization techniques for reducing model size and inference latency, covering post-training quantization (PTQ), quantization-aware training (QAT), and specific formats (GGUF, GPTQ, AWQ). Content links to research papers on quantization methods and includes notebooks implementing quantization pipelines on Colab. 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