#llm

Build enterprise-grade LLM fine-tuning system. Pipeline: 1. Implement data preprocessing and quality validation. 2. Set up LoRA (Low-Rank Adaptation) for efficient training. 3. Configure distributed training across multiple GPUs. 4. Implement gradient checkpointing for memory optimization. 5. Add automated evaluation with ROUGE, BLEU, and custom metrics. 6. Create A/B testing framework for model comparison. 7. Set up MLflow for experiment tracking. 8. Implement model versioning and deployment pipeline. Include cost monitoring and training time optimization.

Professional diagram following Retrieval Augmented Generation architecture. Components: 1. Document Loader -> Splitting -> Embeddings. 2. Vector DB Storage. 3. Query Rewrite -> Retrieval -> Re-ranking. 4. Contextual Prompt -> LLM Generation. Use blue/violet gradients and high-quality technical icons.

Master generative AI and large language model development, fine-tuning, and deployment for various applications. LLM architecture fundamentals: 1. Transformer architecture: self-attention mechanism, multi-head attention, positional encoding. 2. Model scaling: parameter count (GPT-3: 175B), training data (tokens), computational requirements. 3. Architecture variants: encoder-only (BERT), decoder-only (GPT), encoder-decoder (T5). Pre-training strategies: 1. Data preparation: web crawling, deduplication, quality filtering, tokenization (BPE, SentencePiece). 2. Training objectives: next token prediction, masked language modeling, contrastive learning. 3. Infrastructure: distributed training, gradient accumulation, mixed precision (FP16/BF16). Fine-tuning approaches: 1. Supervised fine-tuning: task-specific datasets, learning rate 5e-5 to 1e-4, batch size 8-32. 2. Parameter-efficient fine-tuning: LoRA (Low-Rank Adaptation), adapters, prompt tuning. 3. Reinforcement Learning from Human Feedback (RLHF): reward modeling, PPO training. Prompt engineering: 1. Zero-shot prompting: task description without examples, clear instruction formatting. 2. Few-shot learning: 1-5 examples, in-context learning, demonstration selection strategies. 3. Chain-of-thought: step-by-step reasoning, intermediate steps, complex problem solving. Evaluation methods: 1. Perplexity: language modeling capability, lower is better, domain-specific evaluation. 2. BLEU score: text generation quality, n-gram overlap, reference comparison. 3. Human evaluation: quality, relevance, safety assessment, inter-rater reliability. Deployment considerations: inference optimization, model quantization, caching strategies, latency <1000ms target, cost optimization through batching.

A tool to auto-generate Hugging Face model cards. Sections to include: 1. Model Description (Architecture, Parameters). 2. Training Data (Datasets used). 3. Evaluation Results (MMLU, HumanEval scores). 4. Intended Use and Biases. 5. Citation info. Minimalist layout with badges for 'Transformers', 'PyTorch', 'Safetensors'.

A leaderboard-style comparison of different fine-tuned models. Compare: 1. Llama 3 (LoRA) vs GPT-4v (RLHF) vs Mistral (Base). 2. Benchmarks: MMLU, GSM8k, HumanEval. 3. Column to show 'Inference Cost' vs 'Accuracy'. 4. Radar chart for multi-dimensional performance analysis.

Optimize prompts for Claude. Techniques: 1. Use XML tags for structure (<document>, <instructions>). 2. Human/Assistant message format. 3. Chain-of-thought prompting. 4. Few-shot examples for context. 5. System prompts for behavior. 6. explicit instructions format. 7. Handle 100k+ token context. 8. Streaming for long outputs. Claude excels at following instructions precisely. Implement constitutional AI principles.

Build RAG systems with LlamaIndex. Workflow: 1. Load documents (PDF, DOCX, web). 2. Node parser for chunking. 3. Create embeddings with LLM. 4. Build index (Vector, Tree, Keyword). 5. Query engine for retrieval. 6. Response synthesizer. 7. Sub-question query engine. 8. Chat engine for conversations. Use ServiceContext for configuration and implement hybrid retrieval.

Integrate GPT-4 API effectively. Patterns: 1. Chat completions with system/user messages. 2. Function calling for structured outputs. 3. Streaming responses for better UX. 4. Token counting to manage costs. 5. Temperature and top_p tuning. 6. Max tokens control. 7. Error handling and retries. 8. Rate limiting awareness. Use tiktoken for accurate token counts and implement caching for repeated queries.

Visualize a complex LangChain agent flow. Flow components: 1. User Input -> Embedding Model. 2. Vector DB (Pinecone) retrieval. 3. LLM (GPT-4) reasoning step. 4. Tool execution (Google Search, Python Repl). 5. Final Output. Use a node-based diagram style with directed arrows and color-coded component boxes.

Get structured data from LLMs with Instructor. Pattern: 1. Define Pydantic models for output. 2. Use instructor.patch() on OpenAI client. 3. LLM returns validated objects. 4. Automatic retry on validation errors. 5. Partial streaming for progressive updates. 6. Union types for multiple formats. 7. Nested models for complex data. 8. Field descriptions guide LLM. Type-safe LLM outputs. Use for data extraction and classification.