Ultimate AI Engineering: Build a Local Small Language Model
Build, wrap, and deploy a custom Small Language Model (SLM) from scratch using PyTorch. Learn model streaming, local Chat UIs, production APIs, and custom web scraping data pipelines.
- Set up a clean Python machine learning environment with PyTorch and CUDA architectures.
- Tokenize raw text datasets using Byte-Pair Encoding (BPE) and manage disk memory footprints with NumPy memmap.
- Engineer a Decoder-Only Transformer architecture from scratch with Multi-Head Self-Attention layers.
- Train a neural network locally using AdamW, Cross-Entropy Loss, and Gradient Accumulation parameters.
- Wrap custom weights in production interfaces, creating responsive interactive CLIs and professional local browser Chat UIs.
- Securely serialize models into Safetensors formats and host scalable local APIs using FastAPI framework topologies.
- Design data extraction pipelines, program automated web scrapers, and clean text strings to generate tokenized binary datasets for future custom SLMs.
- Proficiency in Python programming fundamentals.
- A local machine with Python 3.10 or 3.11 installed.
- Basic familiarity with command line terminal operations.
- A dedicated CUDA-enabled GPU is recommended for acceleration, but standard modern CPUs are fully supported.
The Local SLM Masterclass
This course cuts through the hype and teaches you how to build, train, run, wrap, and deploy a Small Language Model (SLM) entirely on your local machine using Python and PyTorch. Moving away from massive API black-boxes, you will engineer a custom Decoder-Only Transformer from scratch, develop your own web-scraping pipelines, and serve models through interactive UI architectures.
The Project Goal: We will use the Hugging Face TinyStories dataset to train an SLM capable of generating coherent, grammatically correct short stories. This dataset uses a highly compact vocabulary, making it fun, fast, and easy to train on standard consumer GPUs or modern CPUs. Following that, you will design custom data scrapers to collect, clean, and tokenize targeted training data for your next model iteration.
© A proprietary course creation by Surya Partap Singh. All Rights Reserved.
Chapter 1: Groundwork & Environment 2 Lessons
Isolating your workflow, configuring Python, and installing the required ML stack dependencies.
Chapter 2: Data Engineering & Tokenization 3 Lessons
Acquiring the TinyStories dataset, understanding tokens, and writing large data to disk efficiently.
Chapter 3: Building the Data Pipeline 2 Lessons
Creating PyTorch data loaders, handling context windows, and mapping input-output training pairs.
Chapter 4: Architecting the Transformer 3 Lessons
Coding the neural network structure, including embeddings, attention mechanisms, and linear layers.
Chapter 5: Training & Generation Engines 4 Lessons
Optimizing the model weights, running the training loop, and writing the text generation script.
Chapter 6: Model Serving & User Interfaces 2 Lessons
Wrapping custom language weights in consumer interfaces, building interactive command-line loops, and deploying graphical local Chat UIs.
Chapter 7: Packaging & API Deployment 2 Lessons
Optimizing model serialization using modern secure storage formats and exporting the network as a high-performance web API.
Chapter 8: Custom Dataset Engineering & Web Scraping 3 Lessons
Building custom datasets from scratch, engineering automated web scrapers to target domain-specific text, and preprocessing raw strings into tokenized files for future SLM projects.