Archive - Tuned, by Axolotl

Training Large Language Models with Interpreter Feedback using WebAssembly

A fast, local, and secure approach to training LLMs for code with WebAssembly and interpreter-based rewards

Apr 3 •

Salman Mohammadi

and

Enabling Long Context Training with Sequence Parallelism in Axolotl (v0.8.0)

Training large language models (LLMs) with long contexts has become an important capability as models continue to expand in both size and context…

Apr 2 •

and

February 2025

Accelerating LoRA Fine-tuning with Custom CUDA Kernels and Autograd Functions

We're excited to announce a performance improvement to Axolotl's LoRA and QLoRA fine-tuning capabilities.

Feb 27 •

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Fine-tuning LLMs as a Cost Strategy

Tips from experts since GPUs and inference can be pricey

Feb 24 •

Supported LLM Models with Axolotl

Quick breakdown of compatibility with Axolotl v0.8.0

Feb 18 •

Axolotl Update v0.7.0 - with GRPO

New fixes and updates to parameters since December 2024

Feb 18 •

Process Reward Models

Training Process Reward Models in axolotl

Feb 18 •

Salman Mohammadi

and

September 2024

Fine Tuning Llama 3.1 405B with Axolotl on a Lambda 1-Click Cluster

Personalizing SOTA Open Source AI

Sep 3, 2024 •

August 2024

Welcome to Tuned, by Axolotl

Bringing you the latest news and techniques in AI Infrastructure

Aug 15, 2024 •

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