200 citations · 565 across the 21 of their papers we have counts for
6 papers · 1 filter
SelfCodeAlign: Self-Alignment for Code Generation
Yuxiang Wei, Federico Cassano, Jiawei Liu +7
Instruction tuning is a supervised fine-tuning approach that significantly improves the ability of large language models (LLMs) to follow human instructions. We propose SelfCodeAli…
The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal +5
The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset. However, the pretraining datasets for state-of-the-art open LLMs…
BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim +30
Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to…
Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
Alexander Hägele, Elie Bakouch, Atli Kosson +3
Scale has become a main ingredient in obtaining strong machine learning models. As a result, understanding a model's scaling properties is key to effectively designing both the rig…
StarCoder 2 and The Stack v2: The Next Generation
Anton Lozhkov, Raymond Li, Loubna Ben Allal +63
The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In partnership wi…
Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models
Terry Yue Zhuo, Armel Zebaze, Nitchakarn Suppattarachai +4
The high cost of full-parameter fine-tuning (FFT) of Large Language Models (LLMs) has led to a series of parameter-efficient fine-tuning (PEFT) methods. However, it remains unclear…