Unsloth Quantized Versions of Laguna S 2.1 Released
Quantized versions of the Laguna S 2.1 language model have been released, optimized using the Unsloth framework. These new formats allow users to run the model with significantly lower hardware requirements. Laguna S 2.1 is a highly capable agentic coding model, and its quantization makes it much more accessible for developers to run locally. By reducing the computational footprint, it lowers the barrier to entry for advanced AI-assisted coding. The Laguna S 2.1 model, developed by Poolside, has a size of 118B-A8B and scores 70.2% on Terminal-Bench 2.1. The new quantized versions were made available thanks to optimization tools from the Unsloth team.
## BACKGROUND
Laguna S 2.1 is an AI model designed by Poolside specifically for agentic coding tasks. Quantization is a model compression technique that converts weights from high-precision formats to lower-precision formats to save memory and compute. Unsloth is an open-source framework designed to accelerate the training and running of LLMs locally.