arcee-ai/mergekit

Arcee AI | Arcee AI and mergekit unite

Several months ago, I stumbled upon an innovative technique in the world of language model training known as Model Merging. This SOTA approach involves the fusion of two or more LLMs into a singular, cohesive model, presenting a novel and experimental method for creating sophisticated models at a fraction of

Arcee AI | March is Merge Madness

To celebrate Arcee’s recent merger with mergekit, we’re bringing you a month of resources and knowledge on model merging.

Extracting Training Data from Large Language Models

Nicholas Carlini, Google; Florian Tramèr, Stanford University; Eric Wallace, UC Berkeley; Matthew Jagielski, Northeastern University; Ariel Herbert-Voss, OpenAI and Harvard University; Katherine Lee and Adam Roberts, Google; Tom Brown, OpenAI; Dawn Song, UC Berkeley; Úlfar Erlingsson, Apple; Alina Oprea, Northeastern University; Colin Raffel, Google

Introducing FlexOlmo: a new paradigm for language model training and data collaboration | Ai2

Explore how FlexOlmo enables collaborative language model training without sacrificing data privacy or control, introducing a new, flexible approach to building shared AI models.

Tiiny-AI/PowerInfer

High-speed Large Language Model Serving for Local Deployment

Arcee AI | Announcing the Arcee Model Engine Public Beta

Get direct access to the small language models (SLMs) that power Arcee Orchestra, our new end-to-end, SLM-powered agentic AI platform. Sign up for the public beta of the Arcee Model Engine today.

Scaling Laws Across Model Architectures: A Comparative Analysis of...

The scaling of large language models (LLMs) is a critical research area for the efficiency and effectiveness of model training and deployment. Our work investigates the transferability and...

Stanford CS336 | Language Modeling from Scratch (Spring 2025 Archive)

Archived course website for Stanford CS336: Language Modeling from Scratch (Spring 2025), including schedule, assignments, logistics, and materials.

The Kaitchup – AI on a Budget | Benjamin Marie | Substack

Weekly tutorials and news on adapting large language models (LLMs) to your tasks and hardware using the most recent techniques and models. The Kaitchup proposes a collection of 180+ AI notebooks regularly updated. Click to read The Kaitchup – AI on a Budget, by Benjamin Marie, a Substack publication with tens of thousands of subscribers.

Wiki-40B: Multilingual Language Model Dataset

We propose a new multilingual language model benchmark that is composed of 40+ languages spanning several scripts and linguistic families. With around 40 billion characters, we hope this new resource will accelerate the research of multilingual modeling. We train monolingual causal language models using a state-of-the-art model (Transformer-XL) establishing baselines for many languages. We also introduce the task of multilingual causal language modeling where we train our model on the combined text of 40+ languages from Wikipedia with different vocabulary sizes and evaluate on the languages individually. We released the cleaned-up text of 40+ Wikipedia language editions, the corresponding trained monolingual language models, and several multilingual language models with different fixed vocabulary sizes.

LLM in a Flash: Efficient Large Language Model Inference with Limited Memory

Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks…

Targeted Multilingual Adaptation for Low-resource Language Families

The "massively-multilingual" training of multilingual models is known to limit their utility in any one language, and they perform particularly poorly on low-resource languages. However, there is evid

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.

Olmo from Ai2

Our fully open language model and complete model flow.

How memory augmentation can improve large language models

Generative AI models struggle to handle long context, but memory augmentation can reduce their memory footprint, making them more accurate and lightweight