// TAG: JAX

Learn how to use Tunix, a JAX-native library for LLM post-training and specialization.

A step-by-step guide to setting up your Tunix environment for LLM post-training.

Learn the essentials of JAX for optimizing and diagnosing LLM training workflows in Tunix.

Learn how to perform Supervised Fine-Tuning (SFT) with Tunix, a powerful tool for aligning LLMs.

Learn how to prepare and load data for Tunix, a Large Language Model using JAX.

Learn how Tunix, built on JAX and Flax NNX, handles model architectures and state management for effective post-training.

Explains the principles and components of Reinforcement Learning from Human Feedback (RLHF) in LLM development.

Learn how to implement basic RLHF workflows with Tunix for creating helpful and aligned Language Models.

Learn how to scale large language models using Tunix and JAX for distributed training.

Learn how to optimize and profile your Tunix-powered LLM post-training for better performance.

Learn how to customize Tunix with custom loss functions, optimizers, and callbacks for advanced LLM post-training.

Learn advanced RLHF strategies, focusing on Proximal Policy Optimization (PPO) with Tunix.

Learn how to fine-tune a conversational agent using Tunix, JAX, and Flax.

Learn to align an LLM for factual accuracy using Tunix, a JAX-native framework.

Learn how to effectively debug and troubleshoot Tunix workflows using JAX.

Learn how to deploy fine-tuned LLMs using FastAPI and Docker for efficient, scalable inference.

A comprehensive guide to mastering Tunix, a JAX-native library for LLM post-training.