/Hand-on-LLM

Only by writing code will you know how LLM works.

Primary LanguageJupyter Notebook

Hand on LLM

Roadmap

1. The LLM architecture

  • High-level view: Revisit the encoder-decoder Transformer architecture, and more specifically the decoder-only GPT architecture, which is used in every modern LLM.

  • Tokenization: Understand how to convert raw text data into a format that the model can understand, which involves splitting the text into tokens (usually words or subwords).

  • Attention mechanisms: Grasp the theory behind attention mechanisms, including self-attention and scaled dot-product attention, which allows the model to focus on different parts of the input when producing an output.

  • Text generation: Learn about the different ways the model can generate output sequences. Common strategies include greedy decoding, beam search, top-k sampling, and nucleus sampling.

2. Building an instruction dataset

  • Alpaca-like dataset: Generate synthetic data from scratch with the OpenAI API (GPT). You can specify seeds and system prompts to create a diverse dataset.
  • Advanced techniques: Learn how to improve existing datasets with Evol-Instruct, how to generate high-quality synthetic data like in the Orca and phi-1 papers.
  • Filtering data: Traditional techniques involving regex, removing near-duplicates, focusing on answers with a high number of tokens, etc.
  • Prompt templates: There's no true standard way of formatting instructions and answers, which is why it's important to know about the different chat templates, such as ChatML, Alpaca, etc.

3. Pre-traing models

4. Supervised Fine-Tuning

5. RLHF

6. Evaluation

7. Quantization

8. New Trends

Reference