Cisco Foundation AI Releases Antares: 350M and 1B Open-Weight Models That Localize Known Vulnerabilities — 2026-07-22

## Short Segments In the world of AI fine-tuning, four frameworks are vying for dominance. Unsloth, Axolotl, TRL, and LLaMA-Factory each offer unique approaches to optimizing large language models. Unsloth focuses on custom Triton kernels, Axolotl on parallelism strategies, TRL on trainer APIs, and LLaMA-Factory on model coverage and zero-code operation. This comparison highlights their performance on training throughput, VRAM usage, and multi-GPU scaling. Engineers now have a clearer picture of which framework best suits their needs, whether it's maximizing speed, minimizing resource use, or simplifying deployment. Poolside's Laguna S 2.1 is making waves as an open-weight agentic coding model that competes with much larger counterparts. With 118 billion parameters, this Mixture-of-Experts model activates only 8 billion parameters per token, allowing it to perform efficiently on a single NVIDIA DGX Spark. It excels on long-horizon coding benchmarks, rivaling models like DeepSeek-V4-Pro-Max and NVIDIA's Nemotron 3 Ultra. By leveraging sparsity, Laguna S 2.1 offers a cost-effective solution for complex coding tasks, proving that size isn't everything in AI performance. ## Feature Story Cisco Foundation AI has unveiled Antares, a new family of security small language models designed to localize vulnerabilities within codebases. Available now on Hugging Face, the Antares-350M and Antares-1B models are open-weight and licensed under Apache 2.0. These models aim to streamline the initial triage process in software security by identifying files containing known vulnerabilities, a task traditionally requiring significant time and expertise. Antares models are not intended to replace existing security toolchains but to enhance them by reducing the time spent on the first step of vulnerability identification. The models achieve a File F1 score of 0.209, which, while not state-of-the-art, is competitive with larger models like GPT-5.5. This efficiency could make vulnerability localization more accessible and cost-effective for development teams. The release of Antares also includes the Vulnerability Localization Benchmark (VLoc Bench), a 500-task evaluation framework that allows developers to assess the models' performance in real-world scenarios. By focusing on this specific aspect of security, Cisco aims to address one of the most challenging and resource-intensive problems in the field. As software systems grow increasingly complex, the ability to quickly and accurately pinpoint vulnerabilities becomes crucial. Antares offers a promising solution by leveraging AI to automate and expedite this process, potentially saving companies both time and money. With the models now available for use, developers can begin integrating them into their workflows, marking a significant step forward in the intersection of AI and cybersecurity.