2 citations · 6 across the 8 of their papers we have counts for
8 papers
GQA-μP: The maximal parameterization update for grouped query attention
Kyle R. Chickering, Huijuan Wang, Mengxi Wu +7
Hyperparameter transfer across model architectures dramatically reduces the amount of compute necessary for tuning large language models (LLMs). The maximal update parameterization…
K2-V2: A 360-Open, Reasoning-Enhanced LLM
K2 Team, Zhengzhong Liu, Liping Tang +36
We introduce K2-V2, a 360-open LLM built from scratch as a superior base for reasoning adaptation, in addition to functions such as conversation and knowledge retrieval from genera…
MoE-Inference-Bench: Performance Evaluation of Mixture of Expert Large Language and Vision Models
Krishna Teja Chitty-Venkata, Sylvia Howland, Golara Azar +5
Mixture of Experts (MoE) models have enabled the scaling of Large Language Models (LLMs) and Vision Language Models (VLMs) by achieving massive parameter counts while maintaining c…
Power Lines: Scaling Laws for Weight Decay and Batch Size in LLM Pre-training
Shane Bergsma, Nolan Dey, Gurpreet Gosal +3
Efficient LLM pre-training requires well-tuned hyperparameters (HPs), including learning rate and weight decay . We study scaling laws for HPs: formulas for how to scale HPs…
Straight to Zero: Why Linearly Decaying the Learning Rate to Zero Works Best for LLMs
Shane Bergsma, Nolan Dey, Gurpreet Gosal +3
LLMs are commonly trained with a learning rate (LR) warmup, followed by cosine decay to 10% of the maximum (10x decay). In a large-scale empirical study, we show that under an opti…
Position Interpolation Improves ALiBi Extrapolation
Faisal Al-Khateeb, Nolan Dey, Daria Soboleva +1
Linear position interpolation helps pre-trained models using rotary position embeddings (RoPE) to extrapolate to longer sequence lengths. We propose using linear position interpola…
BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model
Nolan Dey, Daria Soboleva, Faisal Al-Khateeb +11
We introduce the Bittensor Language Model, called "BTLM-3B-8K", a new state-of-the-art 3 billion parameter open-source language model. BTLM-3B-8K was trained on 627B tokens from th…
SlimPajama-DC: Understanding Data Combinations for LLM Training
Zhiqiang Shen, Tianhua Tao, Liqun Ma +8
This paper aims to understand the impacts of various data combinations (e.g., web text, Wikipedia, GitHub, books) on the pretraining of large language models using SlimPajama. Slim…