In Pretraining LLMs you’ll explore the first step of training large language models using a technique called pretraining. You’ll learn the essential steps to pretrain an LLM, understand the associated costs, and discover how starting with smaller, existing open source models can be more cost-effective.
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In Pretraining LLMs you’ll explore the first step of training large language models using a technique called pretraining. You’ll learn the essential steps to pretrain an LLM, understand the associated costs, and discover how starting with smaller, existing open source models can be more cost-effective.
In Pretraining LLMs you’ll explore the first step of training large language models using a technique called pretraining. You’ll learn the essential steps to pretrain an LLM, understand the associated costs, and discover how starting with smaller, existing open source models can be more cost-effective.
Pretraining involves teaching an LLM to predict the next token using vast text datasets, resulting in a base model, and this base model requires further fine-tuning for optimal performance and safety. In this course, you’ll learn to pretrain a model from scratch and also to take a model that’s already been pretrained and continue the pretraining process on your own data.
In detail:
1. Explore scenarios where pretraining is the optimal choice for model performance. Compare text generation across different versions of the same model to understand the performance differences between base, fine-tuned, and specialized pre-trained models.
2. Learn how to create a high-quality training dataset using web text and existing datasets, which is crucial for effective model pretraining.
3. Prepare your cleaned dataset for training. Learn how to package your training data for use with the Hugging Face library.
4. Explore ways to configure and initialize a model for training and see how these choices impact the speed of pretraining.
5. Learn how to configure and execute a training run, enabling you to train your own model.
6. Learn how to assess your trained model’s performance and explore common evaluation strategies for LLMs, including important benchmark tasks used to compare different models’ performance.
After taking this course, you’ll be equipped with the skills to pretrain a model—from data preparation and model configuration to performance evaluation.
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Reviews summary
Practical pretraining for llm developers
Explanations are clear, topics are current and essential for LLMs.
Reinforces concepts with real-world LLM pretraining tasks.
Activities
Python Refresher
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Brush up on your Python skills before starting the course to ensure a strong foundation and minimize any potential obstacles in your learning journey.
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Python
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Review basic syntax and data structures
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Practice coding exercises
-
Complete online tutorials or refresher courses
-
Build a small project to apply your skills
Subject Matter Experts
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Seek out subject matter experts beyond your instructor to gain additional insights and perspectives on the course material, broadening your understanding and expanding your professional network.
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Mentorship
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Identify potential mentors in the field
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Reach out and introduce yourself
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Schedule meetings or discussions
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Seek guidance and advice
Peer-to-Peer Training
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Engage in peer-to-peer training by mentoring other students in the class, reinforcing your understanding of the course material and developing your leadership and communication skills.
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Communication
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Identify a student who could benefit from your support
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Schedule regular sessions to review course material
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Provide constructive feedback and guidance
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Create practice exercises and assignments
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GPT-3 Response Generator
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Build a GPT-3 based response generator using Hugging Face to practice your text generation skills and improve the quality of your responses in the course.
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Hugging Face
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Install Hugging Face and transformers library
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Load a pre-trained text generator model from Hugging Face
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Write a function to generate text using the model
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Create a simple web app that uses your function to generate responses
Fine-tuning for Specific Tasks
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Follow guided tutorials to fine-tune pre-trained models for specific tasks, allowing you to apply the concepts learned in the course and enhance your problem-solving skills.
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Fine-tuning
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-
Identify a specific task and dataset
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Select an appropriate pre-trained model
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Fine-tune the model on your task
-
Evaluate the performance of the fine-tuned model
Pretrained Model Performance Evaluation
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Engage in practice drills to evaluate the performance of different pre-trained models on various benchmark tasks to reinforce your understanding of model evaluation techniques and gain practical experience.
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Model Evaluation
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Create a dataset for model evaluation
-
Select relevant evaluation metrics
-
Implement model evaluation code
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Evaluate and compare the performance of multiple models
-
Analyze the results and identify areas for improvement
State-of-the-Art Pretraining Techniques
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Compile resources and articles on state-of-the-art pre-training techniques to expand your knowledge and stay updated with the latest advancements in the field of Large Language Models.
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Conduct a literature search for relevant papers and articles
-
Summarize the key findings and insights
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Organize and categorize the resources
-
Create a presentation or document to share your findings
Contribute to Hugging Face
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Engage with the open-source community by contributing to Hugging Face, the leading platform for Large Language Models, to gain practical experience and deepen your understanding of the ecosystem.
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Open Source
Show steps
-
Find an issue or feature to work on
-
Fork the repository
-
Implement your changes
-
Submit a pull request
Python Refresher
Show steps
Brush up on your Python skills before starting the course to ensure a strong foundation and minimize any potential obstacles in your learning journey.
Browse courses on
Python
Show steps
- Review basic syntax and data structures
- Practice coding exercises
- Complete online tutorials or refresher courses
- Build a small project to apply your skills
Subject Matter Experts
Show steps
Seek out subject matter experts beyond your instructor to gain additional insights and perspectives on the course material, broadening your understanding and expanding your professional network.
Browse courses on
Mentorship
Show steps
- Identify potential mentors in the field
- Reach out and introduce yourself
- Schedule meetings or discussions
- Seek guidance and advice
Peer-to-Peer Training
Show steps
Engage in peer-to-peer training by mentoring other students in the class, reinforcing your understanding of the course material and developing your leadership and communication skills.
Browse courses on
Communication
Show steps
- Identify a student who could benefit from your support
- Schedule regular sessions to review course material
- Provide constructive feedback and guidance
- Create practice exercises and assignments
GPT-3 Response Generator
Show steps
Build a GPT-3 based response generator using Hugging Face to practice your text generation skills and improve the quality of your responses in the course.
Browse courses on
Hugging Face
Show steps
- Install Hugging Face and transformers library
- Load a pre-trained text generator model from Hugging Face
- Write a function to generate text using the model
- Create a simple web app that uses your function to generate responses
Fine-tuning for Specific Tasks
Show steps
Follow guided tutorials to fine-tune pre-trained models for specific tasks, allowing you to apply the concepts learned in the course and enhance your problem-solving skills.
Browse courses on
Fine-tuning
Show steps
- Identify a specific task and dataset
- Select an appropriate pre-trained model
- Fine-tune the model on your task
- Evaluate the performance of the fine-tuned model
Pretrained Model Performance Evaluation
Show steps
Engage in practice drills to evaluate the performance of different pre-trained models on various benchmark tasks to reinforce your understanding of model evaluation techniques and gain practical experience.
Browse courses on
Model Evaluation
Show steps
- Create a dataset for model evaluation
- Select relevant evaluation metrics
- Implement model evaluation code
- Evaluate and compare the performance of multiple models
- Analyze the results and identify areas for improvement
State-of-the-Art Pretraining Techniques
Show steps
Compile resources and articles on state-of-the-art pre-training techniques to expand your knowledge and stay updated with the latest advancements in the field of Large Language Models.
Show steps
- Conduct a literature search for relevant papers and articles
- Summarize the key findings and insights
- Organize and categorize the resources
- Create a presentation or document to share your findings
Contribute to Hugging Face
Show steps
Engage with the open-source community by contributing to Hugging Face, the leading platform for Large Language Models, to gain practical experience and deepen your understanding of the ecosystem.
Browse courses on
Open Source
Show steps
- Find an issue or feature to work on
- Fork the repository
- Implement your changes
- Submit a pull request
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