#fine-tuning (8)
- Stabilize Multi-Agent LLM Teams with Trust-Region Fine-Tuning
Learn how trust-region fine-tuning prevents shared-context drift in multi-agent LLM systems, enhancing their coordination and stability.
- Customizing Mistral AI's Vox-Trainer for Audio-Text Applications
The reader will learn how to leverage Mistral AI's Vox-Trainer and its accessible fine-tuning APIs to build custom multimodal audio-text applications.
- Efficient LLM Post-Training with Tunix and JAX
Learn to efficiently align and refine Large Language Models using Tunix, Google's JAX-native library for post-training techniques such as SFT and RLHF.
- The World of LLM Post-Training and Tunix
Learn how to use Tunix, a JAX-native library for LLM post-training and specialization.
- Your First Supervised Fine-Tuning (SFT) Project with Tunix
Learn to perform Supervised Fine-Tuning with Tunix, preparing data, configuring the Trainer, and observing your model learn new skills.
- Build a Specialized Conversational AI with Tunix Fine-Tuning
Discover how to fine-tune a pre-trained Large Language Model with Tunix, JAX, and Flax to develop a specialized conversational partner.
- Fine-Tuning Large Language Models (LLMs)
Learn how to fine-tune Large Language Models for specific tasks using efficient techniques like PEFT and the Hugging Face library.
- Project: Fine-Tuning an LLM for a Specific Task
Learn how to fine-tune a Large Language Model for a specific task using Parameter-Efficient Fine-Tuning techniques like LoRA.