Llm

Notes on training LLMs using sharding strategies

Notes on training LLMs using data parallelism strategy

Notes on Ultra-scale Playbook - training LLM on a single GPU

The post on post-training SFT covered four finetuning experiments on direct and reasoning tasks using LoRA and full-finetuning approaches. This post covers different reinforcement learning (RL) approaches used in post-training. SFT teaches a model by giving it examples to imitate. The training data contains target responses, and the model learns to generate tokens from the target distribution. RL takes a different approach. Instead of showing the model exactly how to solve a problem, we let it generate solutions, evaluate how good those solutions are, and update the model so that successful behaviour becomes more likely. ...

In previous posts, I explored how to pretrain a language model. The setup for pretraining used Llama 3.2 1B base model trained on tokyotech-llm/swallow-code-v2 dataset for Fill-in-Middle (FIM) task. The mistake here was using narrow code-only corpus as pretraining dataset. A more practical pipeline would be to begin with a broadly pretrained base model, optionally continue pretraining it on high-quality code, and then apply SFT using verified prompt–response demonstrations. Pretraining task are used to learn language representation across various tasks. ...