Laguna-S-2.1-NVFP4

모델 소개

Laguna S 2.1-NVFP4 is a 117.6B total parameter Mixture-of-Experts model with 8.5B 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 36 out of 48 layers for fast inference and low KV cache requirements. > **Updated release (August 2026).** This is a new checkpoint that supersedes the earlier version of this repository. The weights have changed, not only the config, so if you downloaded a previous copy please re-download to pick up the current checkpoint. - **Mixed SWA and global attention layout**: Laguna S 2.1 uses softplus gating with per-layer rotary scales, enabling mixed SWA (Sliding Window Attention) and global attention layers in a 3:1 ratio (across 48 total layers) - **KV cache in FP8**: KV cache quantized to FP8, reducing memory per token - **Native reasoning support**: Interleaved thinking between tool calls with support for enabling and disabling thinking per-request - **Local-ready**: At 117.6B total parameters and 8.5B activated, the NVFP4 weights are roughly 71 GB. Available on Ollama and llama.cpp (BF16 and Q4\_K\_M only) - **OpenMDW-1.1 license**: Use and modify the model and associated materials freely for commercial and non-commercial purposes (learn more about OpenMDW)

주요 특징

特点摘要待依据来源补齐，不凭模型名称推断能力。

한계 및 유의 사항

구체적 한계를 판단할 출처가 부족합니다. 실제 성능, 지연, 데이터 정책, 라이선스와 버전 수명을 확인하세요.

출처별 요금 및 비용 분석

요금은 채널, 지역, 설정에 따라 다릅니다. 각 채널 요금은 개별 표시되며 제공사의 통일 공식 요금이 아닙니다.

공개 평가 및 실행 설정

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

자주 묻는 질문

출처 및 추적 기록

许可证：openmdw-1.1。模型介绍与第三方评测均以原来源的具体版本和方法为准。