Apodex 1.

Apodex released version 1.1 of its open-source autonomous agent model lineup, led by the Apodex-1.1-mini base model and including NVFP4, GPTQ-Int4, and FP8 quantized variants, alongside the 1.0 series with 4B, 2B, and 0.8B SFT models. The release includes the FrontierAgent open-source agent harness on GitHub and two technical papers covering the model architecture and the FrontierChallenge benchmark for agentic performance, aiming to support local, verifiable agent deployments.

Apodex 1. If you are looking into autonomous agents, the focus here is on "sustained, verifiable progress." This means the models are optimized for reasoning, searching, file manipulation, code execution, and most importantly, the ability to recover from errors during an agentic loop. The Model Lineup and Quantization Options The 1.1 release focuses on the "mini" architecture, but they have provided several ways to deploy it depending on your hardware constraints. This is a great practical tutorial for anyone wanting to test different quantization levels on local hardware. Apodex-1.1-mini : The base model for those who want the full precision. Apodex-1.1-mini-NVFP4 : Optimized for NVIDIA hardware using FP4. Apodex-1.1-mini-GPTQ-Int4 : A standard choice for high-speed inference on consumer GPUs. Apodex-1.1-mini-FP8 : A middle ground for modern hardware supporting 8-bit floating point. For those who need even smaller footprints or want to benchmark against previous iterations, the 1.0 series is also available: Apodex-1.0-mini Apodex-1.0-4B-SFT Apodex-1.0-2B-SFT Apodex-1.0-0.8B-SFT Beyond the Weights: Agent Harness and Research What makes this more than just a standard model dump is the accompanying ecosystem. The team has released an open-source agent harness called FrontierAgent on GitHub. If you are building an AI workflow, having a standardized harness to test how your models handle tool-calling and error recovery is critical. They have also published two technical papers that provide a deep dive into the methodology: - The Apodex 1.1 model paper covers the architectural nuances. - The FrontierChallenge benchmark paper outlines how they evaluate agentic performance. This is a solid step for the open-source community, especially for developers trying to move away from closed-source APIs toward local, verifiable agent deployments. If you're working on prompt engineering for agents or trying to figure out how to make an LLM actually "use" a computer, checking out their FrontierAgent repository is a high-priority move. Next My local LLM memory architecture finally stopped hallucinating → /en/threads/7916/