#jupyter_notebook #ai #artificial_intelligence #chatgpt #deep_learning #from_scratch #gpt #language_model #large_language_models #llm #machine_learning #python #pytorch #transformer

You can learn how to build your own large language model (LLM) like GPT from scratch with clear, step-by-step guidance, including coding, training, and fine-tuning, all explained with examples and diagrams. This approach mirrors how big models like ChatGPT are made but is designed to run on a regular laptop without special hardware. You also get access to code for loading pretrained models and fine-tuning them for tasks like text classification or instruction following. This helps you deeply understand how LLMs work inside and lets you create your own functional AI assistant, gaining practical skills in AI development[1][2][3][4].

https://github.com/rasbt/LLMs-from-scratch

#python #agents #ai #ai_agents #ai_engineering #computer_vision #course #deep_learning #from_scratch #generative_ai #llm #machine_learning #mcp #nlp #python #reinforcement_learning #rust #swarm_intelligence #transformers #tutorial #typescript

This is a free MIT learning guide for AI engineering with 428 lessons in 20 phases. It teaches you AI from the math up, then moves into machine learning, deep learning, LLMs, agents, tools, safety, and production. Each lesson helps you build useful code or AI tools, not just read theory. You can start at the right level, follow a clear path, and keep reusable artifacts for real work. The benefit is simple: you learn how AI actually works and gain practical skills you can use to build and ship better AI systems.

https://github.com/rohitg00/ai-engineering-from-scratch

#c_lang #avx2 #c99 #cpu_inference #deep_learning #from_scratch #inference_engine #kimi_k3 #linear_attention #llm #llm_inference #machine_learning #memory_efficient #mixture_of_experts #moe #mxfp4 #quantization #simd #systems_programming #transformer #zero_dependencies

kimi-k3-in-c runs the Kimi K3 language model on a CPU without a GPU or machine learning framework. It supports text generation and multi-turn text chat on Linux, macOS, and Windows. By loading model data from disk as needed, it can run the full model with limited memory, making it useful on machines that cannot hold the model in RAM. It requires roughly 1.7 TB of free storage, and generation can be slow, especially with less memory or slower disks.

https://github.com/FareedKhan-dev/kimi-k3-in-c