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Reza Shabani: How to train your own LLM

Lecture by Reza Shabani. Published May 25, 2023. Download slides.

Chapter Summaries

Why train your own LLMs?

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  • Topic of the lecture: how to train large language models
  • Reasons for training your own models are customization, reduce dependency, cost efficiency, data privacy and control over updates
  • Lecture covers the process of training Ghostwriter code completion model
  • Ghostwriter is a competitor to Co-pilot, used for code generation

The Modern LLM Stack

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  • Replit uses Databricks for all of their data pipelines, including pre-processing, summary statistics, analytics transformations, and more.
  • Replit also makes use of Hugging Face for data sets, pre-trained models, tokenizers, and inference tools.
  • Mosaic ML is used for GPU nodes and model training, with pre-configured LLM configurations available.
  • The process is divided into three stages: data processing, model training, and deployment/production.

Data Pipelines: Databricks & Hugging Face

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  • The data pipeline starts with a large corpus of permissively licensed code data from The Stack.
  • The data set comes from the GitHub archive and undergoes license filtering and near-deduplication.
  • The data set contains programming languages in the hundreds.
  • Databricks is used for processing and transformations, rather than Hugging Face tooling.
  • Databricks allows for more control over the data and enables processing at scale.
  • Proprietary data sources and data sets not on Hugging Face can be included in the training set.
  • The process is tractable and extensible.
  • Pre-processing steps are important in understanding the data set.


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  • Anonymizing the data is an important pre-processing step, which involves removing emails, IP addresses, and secret keys.
  • Auto-generated code and minified code are also removed using regexes and other heuristics.
  • Code that doesn't compile or is not parsable is removed to remove bugs and improve model training.
  • The team uses filters based on average line length, maximum line length, and percentage of alphanumeric characters.
  • Metrics such as the number of GitHub stars or issues do not necessarily improve model quality.
  • The team also trains its own tokenizer.

Tokenizer Training

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  • Tokenizers are made up of a tokenization algorithm and a vocabulary.
  • Standard tokenizers are available on Hugging Face, but custom tokenizers can be trained on domain-specific data.
  • A custom tokenizer can result in a smaller vocabulary, which speeds up model training and inference while capturing more relevant information.
  • The tokenizer feeds back into the data pipeline and the training process, making it an integral part of the model.

Running Training: MosaicML, Weights & Biases

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  • Mosaic ML provides GPUs from multiple Cloud providers at reduced prices
  • They have well-tuned LLM training configurations for specific models
  • The manager infrastructure is fault-tolerant and has an easy-to-use CLI for training runs
  • The speaker found using Mosaic ML worth it due to these benefits
  • They use Weights & Biases for logging during training runs

Testing & Evaluation: HumanEval, Hugging Face

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  • Testing language models is difficult and time-consuming
  • HumanEval is a common dataset for testing code generation models
  • Hugging Face's code inference tool is useful for running tests quickly
  • Running tests for multiple languages and certain tasks, like web completion, is more difficult
  • Models need to be tested on unseen data to prevent bias
  • Models can score well on tests but still not be practical or effective

Deployment: FasterTransformer, Triton Server, k8s

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  • Deployment into production is a complex topic with many factors to consider
  • Replit uses FasterTransformer and NVIDIA's Triton server for optimized performance
  • Trton server allows for multiple model instances per GPU or multiple GPUs per model, with useful features like batching and request cancellation for reducing latency
  • Auto-scaling infrastructure is used for running the models, but there are unique challenges for deployed models such as larger model sizes and specific GPU requirements
  • Dealing with GPU shortages in individual zones is necessary

Lessons learned: data-centrism, eval, and collaboration

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  • Data is the most difficult part of the process
  • Good pipelines are important for scalability and quick iteration
  • Data is a critical factor in model quality and output
  • Human evaluation and user testing are important for model vibes and usefulness
  • Collaboration across the team is key to ensure all moving parts are working together

What makes a good LLM engineer?

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  • A good engineer in this field requires a mix of research and engineering mindset
  • Working with data at scale is crucial, including the ability to move data into distributed pipelines
  • A strong technical background in stats, computer science, algorithms, and data structures is important
  • Skilled software development, including familiarity with libraries and frameworks like PyTorch is essential
  • Engineers who appreciate and build in CI/CD help with the fast iteration loop for training models
  • The replit team is hiring for these types of problems and welcomes interested applicants to speak with them about opportunities

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