Setting Up Your Own Large Language Model

How to Choose Between Small and Frontier Models

The rise of small language models The post How to Choose Between Small and Frontier Models appeared first on Towards Data Science .

Very Large Language Models and How to Evaluate Them

Introducing The World's Largest Open Multilingual Language Model: BLOOM

Understanding the capabilities, limitations, and societal impact of large language models

Language Models for Portuguese: A Systematic Mapping Study

The paper surveys language models created for Portuguese, noting that while rapid progress has been made in NLP, development has been uneven across languages. It systematically maps 46 Portuguese models, detailing aspects such as base model, architecture, resources, datasets, licensing, code, data, and weights. The study also traces model evolution phylogenetically, highlights research gaps, and outlines future directions for Portuguese language modeling.

Cosmopedia: how to create large-scale synthetic data for pre-training Large Language Models

How to Make Your Own JEV Model from an Open LLM

The article explains how to transform a small open‑source Qwen LLM into a fast, single‑pass text classifier by replacing its language‑modeling head with a JEV model. It provides a step‑by‑step guide to swapping the head, enabling the LLM to perform classification tasks efficiently. The process leverages the flexibility of open‑source models to create a lightweight, high‑performance classifier.

Best practices for deploying language models

Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.

Evaluating large language models trained on code

Size Matters: Foundation Model for Czech HTML documents

The paper introduces HTML‑LM, a 154‑million‑parameter foundation model designed for Czech HTML documents. It leverages HTML‑aware training and a ModernBERT architecture, trained on 100 million web pages with objectives such as masked language modeling, bag‑of‑words prediction, and contrastive distillation from larger language models. HTML‑LM achieves state‑of‑the‑art performance on classification and regression tasks in the Czech Internet domain, outperforms larger encoders and small LLMs, and is deployed in production to process thousands of web documents per second.

Improving language model behavior by training on a curated dataset

Our latest research finds we can improve language model behavior with respect to specific behavioral values by fine-tuning on a small, curated dataset.

The Reformer - Pushing the limits of language modeling