Assembling GPT-like LLMs from scratch on PyTorch π₯
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https://github.com/analyticalrohit/llms-from-scratch
π 10 notebooks. Step-by-step explanation.
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AI-Study-Group is a public GitHub learning journal for builders trying to navigate AI resources across books, courses, videos, tools, models, datasets, papers, and notes. ππ€
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I came across a great find from Vizuara β a series of 43 lectures that truly delivers on its promise: showing how to build a large language model from scratch. π§ β¨
Most people use ChatGPT.
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π What you will learn:
β The architecture of Transformer ποΈ
β The internal structure of GPT
β Tokenization and BPE π§©
β Attention mechanisms π
β The process of training an LLM π
β Full implementations in Python π
β Suitable for:
β’ ML engineers
β’ AI enthusiasts
β’ Developers entering the GenAI field
β’ Anyone who is tired of explaining AI as a "black box" π΅οΈ
If you really want to understand what lies at the heart of models like ChatGPT, Claude, and Gemini β this material is worth watching. π
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Transformers & LLMs Cheatsheet.pdf
1.4 MB
The only LLM cheat sheet you'll ever need π
Covers the main concepts, architectures, and practical applications.
### Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)
### Transformer architecture and its variants
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- GPT (models with only a decoder)
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### Large language models (LLMs)
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- Optimizations (Mixture of Experts, Distillation, Quantization)
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- Reasoning models (Scaling)
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Covers the main concepts, architectures, and practical applications.
### Basics
- Tokens (tokenization, BPE)
- Embeddings (cosine similarity)
- Attention mechanism (Attention formula, Multi-Head Attention)
### Transformer architecture and its variants
- BERT (models with only an encoder)
- GPT (models with only a decoder)
- T5 (models with an encoder and a decoder)
### Large language models (LLMs)
- Prompting (context length, Chain-of-Thought)
- Pre-training (SFT, PEFT/LoRA)
- Preference tuning (Reward Model, Reinforcement Learning)
- Optimizations (Mixture of Experts, Distillation, Quantization)
### Applications
- LLM-as-a-Judge (LaaJ)
- RAG (Retrieval-Augmented Generation)
- Agents (ReAct)
- Reasoning models (Scaling)
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The ultimate guide to fine tuning.pdf
15.2 MB
π The Big Book on Fine-Tuning LLMs
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The tool allows you to convert any web pages into structured data using LLM.
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This isn't a drill. This isn't a rerun.
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β Every future course included automatically
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