Concept

How frontier AI labs use multi-teacher on-policy distillation to merge capabilities, recover from RL forgetting, and scale post-training across MiMo-V2-Flash, GLM-5, Nemotron-Cascade 2, and DeepSeek-V4.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Learn all the basics and best practices of fine-tuning. Beginner-friendly.

Discover the key differences between RAG and fine-tuning, what each approach can bring, and how to choose the right AI approach for your business goals.

A deep dive into the methods, fine-tuning pipeline, and operational risks of building specialised models.

Best practices for LoRA hyperparameters and how they affect the fine-tuning process.

AI Engineering bootcamp. Contribute to apatti/AIEBootcamp development by creating an account on GitHub.

Open Source Application for Advanced LLM Engineering: interact, train, fine-tune, and evaluate large language models on your own computer. - transformerlab/transformerlab-app

I summarise the kinds of evaluations that are needed for a structured data generation task.