most citedDevstral: Fine-tuning Language Models for Coding Agent Applications

2 citations · 5 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL20261 cited

Ministral 3

Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian +116

We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes:…

cs.SE20252 cited

Devstral: Fine-tuning Language Models for Coding Agent Applications

Abhinav Rastogi, Adam Yang, Albert Q. Jiang +100

We introduce Devstral-Small, a lightweight open source model for code agents with the best performance among models below 100B size. In this technical report, we give an overview o…

cs.SD2025

Voxtral

Alexander H. Liu, Andy Ehrenberg, Andy Lo +103

We present Voxtral Mini and Voxtral Small, two multimodal audio chat models. Voxtral is trained to comprehend both spoken audio and text documents, achieving state-of-the-art perfo…

cs.CL20252 cited

Magistral

Mistral-AI, :, Abhinav Rastogi +98

We introduce Magistral, Mistral's first reasoning model and our own scalable reinforcement learning (RL) pipeline. Instead of relying on existing implementations and RL traces dist…

cs.CV2025

Unified Multimodal Discrete Diffusion

Alexander Swerdlow, Mihir Prabhudesai, Siddharth Gandhi +2

Multimodal generative models that can understand and generate across multiple modalities are dominated by autoregressive (AR) approaches, which process tokens sequentially from lef…

cs.IR2025

Repository-level Code Search with Neural Retrieval Methods

Siddharth Gandhi, Luyu Gao, Jamie Callan

This paper presents a multi-stage reranking system for repository-level code search, which leverages the vastly available commit histories of large open-source repositories to aid…