14 авг. 2024 г. · Pruning and classical knowledge distillation is a highly cost-effective method to progressively obtain LLMs of smaller size, achieving superior ...

3 авг. 2023 г. · We trained an ensemble consisting of a GPT-2 and small LLaMA models on the developmentally-plausible, 10M-word BabyLM dataset, then distilled it into a small, ...

This guide will teach you about knowledge distillation (KD) and show you how you can use torchtune to distill a Llama3.1 8B model into Llama3.2 1B.

18 нояб. 2024 г. · In this blog, we present a case study on distilling a Llama 3.1 8B model into Llama 3.2 1B using torchtune's knowledge distillation recipe.

27 авг. 2024 г. · Our top-performing model, distilled from Llama3-8B-Instruct, achieves a 29.61 length-controlled win rate on AlpacaEval 2 against GPT-4 and 7.35 ...

This notebook has to do with fine-tuning an LLM Judge that evaluates the responses of another LLM to a user query.

Продолжительность: 29:19
Опубликовано: 18 окт. 2024 г.

14 авг. 2024 г. · In a new research paper, our partners at NVIDIA explore how various large models can be made smaller using structured weight pruning and knowledge distillation.

A local database to store the result of all queries. Schema: results(key text, turns text, results text) - key: hash of the conversation - turns: all human ...

9 сент. 2024 г. · Our goal is to distill transformer models into hybrid Mamba and Mamba2 models with varying attention layers (50%, 25%, 12.5%, and 0%). Mamba2 is ...