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Chapter 1: The World of LLM Post-Training and Tunix
Learn how to use Tunix, a JAX-native library for LLM post-training and specialization.
ACCESS >>// A COMPREHENSIVE GUIDE TO CREATE A COMPREHENSIVE BEGINNER-TO-ADVANCED MASTERY GUIDE FOR TUNIX, A JAX-NATIVE LIBRARY FOR LLM POST-TRAINING. COVER ITS FUNDAMENTALS, SETUP, CORE CONCEPTS, ADVANCED FEATURES, REAL-WORLD APPLICATIONS, PERFORMANCE CONSIDERATIONS, DEBUGGING, DEPLOYMENT, AND BEST PRACTICES. CHAPTERS
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Chapter 2: Setting Up Your Tunix Environment
A step-by-step guide to setting up your Tunix environment for LLM post-training.
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Chapter 3: JAX Essentials for Tunix Users
Learn the essentials of JAX for optimizing and diagnosing LLM training workflows in Tunix.
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Chapter 4: Your First Tunix Fine-Tuning: Supervised Fine-Tuning (SFT)
Learn how to perform Supervised Fine-Tuning (SFT) with Tunix, a powerful tool for aligning LLMs.
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Chapter 5: Data Preparation and Loading for Tunix
Learn how to prepare and load data for Tunix, a Large Language Model using JAX.
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Chapter 6: Understanding Tunix Model Architectures and State Management
Learn how Tunix, built on JAX and Flax NNX, handles model architectures and state management for effective post-training.
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Chapter 7: Introduction to Reinforcement Learning from Human Feedback (RLHF) Concepts
Explains the principles and components of Reinforcement Learning from Human Feedback (RLHF) in LLM development.
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Chapter 8: Implementing Basic RLHF Workflows with Tunix
Learn how to implement basic RLHF workflows with Tunix for creating helpful and aligned Language Models.
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Chapter 9: Distributed Training and Scaling with Tunix
Learn how to scale large language models using Tunix and JAX for distributed training.
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Chapter 10: Performance Optimization and Profiling in Tunix
Learn how to optimize and profile your Tunix-powered LLM post-training for better performance.
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Chapter 11: Customizing Tunix: Loss Functions, Optimizers, and Callbacks
Learn how to customize Tunix with custom loss functions, optimizers, and callbacks for advanced LLM post-training.
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Chapter 12: Advanced RLHF Strategies and Proximal Policy Optimization (PPO)
Learn advanced RLHF strategies, focusing on Proximal Policy Optimization (PPO) with Tunix.
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Chapter 13: Project 1: Fine-Tuning a Conversational Agent
Learn how to fine-tune a conversational agent using Tunix, JAX, and Flax.
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Chapter 14: Project 2: Aligning an LLM for Factual Accuracy
Learn to align an LLM for factual accuracy using Tunix, a JAX-native framework.
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Chapter 15: Debugging and Troubleshooting Tunix Workflows
Learn how to effectively debug and troubleshoot Tunix workflows using JAX.
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Chapter 16: Deployment Strategies for Fine-Tuned LLMs
Learn how to deploy fine-tuned LLMs using FastAPI and Docker for efficient, scalable inference.
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Chapter 17: Ethical Considerations and Responsible AI in Post-Training
Explore ethical considerations and responsible AI practices in the post-training phase of Large Language Models.
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