Last Call: Build your own Language Model from scratch

2026-04-25 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Advanced, quick

Summary

A 2-day hands-on workshop, scheduled for April 25-26 from 8-10 AM PST, offers participants the opportunity to build a language model from scratch for $29. The workshop focuses on implementing core components like tokenization, positional encoding, the attention mechanism, and KV Cache optimization. It aims to move participants beyond API usage to a fundamental understanding of model construction, including building a Small Language Model (SLM) end-to-end. The instructor has developed several SLMs, including Gemma 3 & 4, DeepSeek, Qwen, DevOps SLM, and LLaMA 4, which are available on Hugging Face, and authored a book on the subject. Lifetime access to workshop recordings is included.

Key takeaway

For AI Engineers or Machine Learning practitioners looking to deepen their understanding beyond API calls, enrolling in this workshop provides a practical, step-by-step guide to building language models. You will gain hands-on experience with foundational components like attention mechanisms and KV caching, which is crucial for customizing and optimizing models. Consider this an opportunity to transition from an AI user to a builder, enhancing your technical capabilities.

Key insights

The workshop teaches end-to-end language model construction, emphasizing foundational components over API usage.

Principles

• Understand core LLM mechanisms
• Build models from first principles

Method

The workshop follows a step-by-step implementation approach, covering tokenization, positional encoding, attention mechanisms, and KV Cache optimization to build an SLM.

In practice

• Implement tokenization
• Build an SLM end-to-end

Topics

• Language Model Development
• Tokenization
• Positional Encoding
• Attention Mechanism
• KV Cache

Best for: Machine Learning Engineer, NLP Engineer, AI Engineer

Related on AIssential

• Building LLM From Scratch: Understanding How Large Language Models Work — Large Language Models fundamentally predict the next token by processing numerical representations of text through attention mechanisms.
• AI 101: Your Ultimate Guide to Attention: Mechanism, QKV, and KV Cache — Attention enables Transformer models to dynamically weigh token relevance for contextual understanding using queries, keys, and values.
• LaCy: What Small Language Models Can and Should Learn is Not Just a Question of Loss — SLMs can improve factual accuracy by learning to delegate specific tokens rather than predicting all tokens.
• Build and Train an LLM from Scratch — Training an LLM from scratch involves data preparation, custom Transformer architecture, and tokenizer implementation.
• Building LMs that can model the world and model themselves | ARC Prize @ MIT — LLMs require explicit training for internal coherence, encompassing both factual consistency and accurate self-assessment of knowledge.
• Construction of Knowledge Graph based on Language Model — PLMs significantly enhance Knowledge Graph construction by automating entity and relation extraction from text.

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.