AI is happening. The future is updating.Laguna-XS-2.1-NVFP4
Laguna XS 2.1-NVFP4 is a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token designed for agentic coding and long-horizon work on a local machine. It uses Sliding Window Attention with per-head gating in 30 out of 40 layers for fast inference and low KV cache requirements. > [!NOTE] > This is the NVFP4 variant with an FP8-quantized KV cache. The BF16, FP8 and INT4 variants are also available on Hugging Face. *We used the highest publicly-referenced scores for all comparison models across each benchmark. In all cases these were official scores published in release blog posts or equivalent, with the exception of gpt-oss-120b and Claude Haiku 4.5 where the highest published (verified) scores for SWE-Bench Pro and Terminal-Bench 2.0 are from their respective official leaderboards.*
Laguna XS 2.1-NVFP4 is a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token designed for agentic coding and long-horizon work on a local machine. It uses Sliding Window Attention with per-head gating in 30 out of 40 layers for fast inference and low KV cache requirements. > [!NOTE] > This is the NVFP4 variant with an FP8-quantized KV cache. The BF16, FP8 and INT4 variants are also available on Hugging Face. *We used the highest publicly-referenced scores for all comparison models across each benchmark. In all cases these were official scores published in release blog posts or equivalent, with the exception of gpt-oss-120b and Claude Haiku 4.5 where the highest published (verified) scores for SWE-Bench Pro and Terminal-Bench 2.0 are from their respective official leaderboards.*