Category
llm
29 articles across 3 sub-topics
Types of LLM Quantization: By Timing, Scope, and Mapping
TLDR: There is no single "best" LLM quantization. You classify and choose quantization along three axes: when you quantize (timing), what you quantize (scope), and how values are encoded (mapping). In practice, most teams start with weight quantizati...
Practical LLM Quantization in Colab: A Hugging Face Walkthrough
TLDR: This is a practical, notebook-style quantization guide for Google Colab and Hugging Face. You will quantize real models, run inference, compare memory/latency, and learn when to use 4-bit NF4 vs safer INT8 paths. 📖 What You Will Build in Thi...
GPTQ vs AWQ vs NF4: Choosing the Right LLM Quantization Pipeline
TLDR: GPTQ, AWQ, and NF4 all shrink LLMs, but they optimize different constraints. GPTQ focuses on post-training reconstruction error, AWQ protects salient weights for better quality at low bits, and NF4 offers practical 4-bit compression through bit...
SFT for LLMs: A Practical Guide to Supervised Fine-Tuning
TLDR: Supervised fine-tuning (SFT) is the stage where a pretrained model learns task-specific response behavior from curated input-output examples. It is usually the first alignment step after pretraining and often the foundation for later RLHF. Good...
RLHF in Practice: From Human Preferences to Better LLM Policies
TLDR: Reinforcement Learning from Human Feedback (RLHF) helps align language models with human preferences after pretraining and SFT. The typical pipeline is: collect preference comparisons, train a reward model, then optimize a policy (often with KL...
PEFT, LoRA, and QLoRA: A Practical Guide to Efficient LLM Fine-Tuning
TLDR: Full fine-tuning updates every model weight, which is expensive in memory, compute, and storage. PEFT methods update only a small trainable slice. LoRA learns low-rank adapters on top of frozen base weights. QLoRA pushes efficiency further by q...
LLM Model Naming Conventions: How to Read Names and Why They Matter
TLDR: LLM names encode practical decisions: model family, size, training stage, context window, format, and quantization level. If you can decode naming conventions, you can avoid costly deployment mistakes and choose the right checkpoint faster. �...
Unlocking the Power of ML, DL, and LLM Through Real-World Use Cases
TLDR: ML, Deep Learning, and LLMs are not competing technologies — they are a nested hierarchy. LLMs are a type of Deep Learning. Deep Learning is a subset of ML. Choosing the right layer depends on your data type, problem complexity, and available t...
Text Decoding Strategies: Greedy, Beam Search, and Sampling
TLDR: An LLM doesn't "write" text — it generates a probability distribution over all possible next tokens and then uses a decoding strategy to pick one. Greedy, Beam Search, and Sampling are different rules for that choice. Temperature controls the c...
RLHF Explained: How We Teach AI to Be Nice
TLDR: A raw LLM is a super-smart parrot that read the entire internet — including its worst parts. RLHF (Reinforcement Learning from Human Feedback) is the training pipeline that transforms it from a pattern-matching engine into an assistant that is ...
Mastering Prompt Templates: System, User, and Assistant Roles with LangChain
TLDR: A production prompt is not a string — it is a structured message list with system, user, and optional assistant roles. LangChain's ChatPromptTemplate turns this structure into a reusable, testable, injection-safe blueprint. TLDR: LangChain p...
Prompt Engineering Guide: From Zero-Shot to Chain-of-Thought
TLDR: Prompt Engineering is the art of writing instructions that guide an LLM toward the answer you want. Zero-Shot, Few-Shot, and Chain-of-Thought are systematic techniques — not guesswork — that can dramatically improve accuracy without changing a ...
Multistep AI Agents: The Power of Planning
TLDR: A simple ReAct agent reacts one tool call at a time. A multistep agent plans a complete task decomposition upfront, then executes each step sequentially — handling complex goals that require 5-10 interdependent actions without re-prompting the ...
LoRA Explained: How to Fine-Tune LLMs on a Budget
TLDR: Fine-tuning a 7B-parameter LLM updates billions of weights and requires expensive GPUs. LoRA (Low-Rank Adaptation) freezes the original weights and trains only tiny adapter matrices that are added on top. 90%+ memory reduction; zero inference l...
How to Develop Apps Using LangChain and LLMs
TLDR: LangChain is a framework that simplifies building LLM applications. It provides abstractions for Chains (linking steps), Memory (remembering chat history), and Agents (using tools). It turns raw API calls into composable building blocks. TLD...
Guide to Using RAG with LangChain and ChromaDB/FAISS
TLDR: RAG (Retrieval-Augmented Generation) gives an LLM access to your private documents at query time. You chunk and embed documents into a vector store (ChromaDB or FAISS), retrieve the relevant chunks at query time, and inject them into the LLM's ...
'The Developer''s Guide: When to Use Code, ML, LLMs, or Agents'
TLDR: AI is a tool, not a religion. Use Code for deterministic logic (banking, math). Use Traditional ML for structured predictions (fraud, recommendations). Use LLMs for unstructured text (summarization, chat). Use Agents only when a task genuinely ...
AI Agents Explained: When LLMs Start Using Tools
TLDR: A standard LLM is a brain in a jar — it can reason but cannot act. An AI Agent connects that brain to tools (web search, code execution, APIs). Instead of just answering a question, an agent executes a loop of Thought → Action → Observation unt...
A Guide to Pre-training Large Language Models
TLDR: Pre-training is the phase where an LLM learns "Language" and "World Knowledge" by reading petabytes of text. It uses Self-Supervised Learning to predict the next word in a sentence. This creates the "Base Model" which is later fine-tuned. 📖 ...
LLM Model Quantization: Why, When, and How to Deploy Smaller, Faster Models
TLDR: Quantization converts high-precision model weights and activations (FP16/FP32) into lower-precision formats (INT8 or INT4) so LLMs run with less memory, lower latency, and lower cost. The key is choosing the right quantization method for your a...
LLM Hyperparameters Guide: Temperature, Top-P, and Top-K Explained
TLDR: Temperature, Top-p, and Top-k are three sampling controls that determine how "creative" or "deterministic" an LLM's output is. Temperature rescales the probability distribution; Top-k limits the candidate pool by count; Top-p limits it by cumul...
Mastering Prompt Templates: System, User, and Assistant Roles with LangChain
TLDR: Prompt templates are the contract between your application and the LLM. Role-based messages (System / User / Assistant) provide structure. LangChain's ChatPromptTemplate and MessagesPlaceholder turn ad-hoc strings into versioned, testable pipel...
Tokenization Explained: How LLMs Understand Text
TLDR: LLMs don't read words — they read tokens. A token is roughly 4 characters. Byte Pair Encoding (BPE) builds an efficient subword vocabulary by iteratively merging frequent character pairs. Tokenization choices directly affect cost, context limit...
RAG Explained: How to Give Your LLM a Brain Upgrade
TLDR: LLMs have a training cut-off and no access to private data. RAG (Retrieval-Augmented Generation) solves both problems by retrieving relevant documents from an external store and injecting them into the prompt before generation. No retraining re...
LLM Terms You Should Know: A Helpful Glossary
TLDR: The world of LLMs has its own dense vocabulary. This post is your decoder ring — covering foundation terms (tokens, context window), generation settings (temperature, top-p), safety concepts (hallucination, grounding), and architecture terms (a...
Large Language Models (LLMs): The Generative AI Revolution
TLDR: Large Language Models predict the next token, one at a time, using a Transformer architecture trained on billions of words. At scale, this simple objective produces emergent reasoning, coding, and world-model capabilities. Understanding the tra...
LLM Skills vs Tools: The Missing Layer in Agent Design
TLDR: A tool is a single callable capability (search, SQL, calculator). A skill is a reusable mini-workflow that coordinates multiple tool calls with policy, guardrails, retries, and output structure. If you model everything as "just tools," your age...
LLM Skill Registries, Routing Policies, and Evaluation for Production Agents
TLDR: If tools are primitives and skills are reusable routines, then the skill registry + router + evaluator is your production control plane. This layer decides which skill runs, under what constraints, and how you detect regressions before users do...