--- title: Laguna-S-2.1 canonical_url: "https://www.modelscope.cn/models/poolside/Laguna-S-2.1" md_url: "https://www.modelscope.cn/models/poolside/Laguna-S-2.1.md" repository: poolside/Laguna-S-2.1 last_updated: 2026-08-20 license: openmdw-1.1 pipeline_tag: text-generation tasks: - text-generation model_type: - laguna architectures: - LagunaForCausalLM parameters: 117.6B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - pytorch downloads: 35257 stars: 34 tags: - laguna-s-2.1 - vllm --- # Laguna-S-2.1 > Laguna-S-2.1 - poolside 在 ModelScope 开源的模型。Use on OpenRouter · Use on Vercel AI Gateway · Release blog post poolside/Laguna-S-2.1 是 ModelScope 魔搭社区上的 117.6B 参数text-generation模型，采用 openmdw-1.1 许可。 - **Repository**: poolside/Laguna-S-2.1 - **License**: openmdw-1.1 - **Tasks**: text-generation - **Parameters**: 117.6B - **Tags**: laguna-s-2.1, vllm - **Downloads**: 35257 - **Stars**: 34 - **Last updated**: 2026-08-20 Source: https://www.modelscope.cn/models/poolside/Laguna-S-2.1 ---

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Use on OpenRouter · Use on Vercel AI Gateway · Release blog post


# Laguna S 2.1 Laguna S 2.1 is a 118B total parameter Mixture-of-Experts model with 8B activated parameters per token, designed for agentic coding and long-horizon work. It sits between [Laguna XS 2.1](https://huggingface.co/poolside/Laguna-XS-2.1) (33B-A3B) and Laguna M.1 (225B-A23B) in the Laguna series and shares the family recipe: a token-choice router with softplus gating over 256 routed experts plus one shared expert, grouped-query attention, and interleaved full/sliding-window attention. >[!NOTE] >Laguna S 2.1 is released under OpenMDW-1.1, a fully permissive license. Use it, modify it, and build commercial products on it. No permission required. If you want more than the weights: production support, latency and cost optimization, or output indemnification, [talk to us](https://poolside.ai/contact?utm_source=hf&utm_medium=modelcard_cta). ## Highlights - **Mixed SWA and global attention layout**: 48 layers in a 1:3 global-to-SWA ratio (12 global attention layers, 36 sliding-window layers, window 512), with softplus attention gating and per-layer-type rotary scales - **1M context**: 1,048,576-token context window - **Native reasoning support**: interleaved thinking between tool calls, with per-request control via `enable_thinking` - **Speculative decoding**: a trained [DFlash draft model](https://huggingface.co/poolside/Laguna-S-2.1-DFlash) is available for lower-latency serving - **Quantized variants**: [FP8](https://huggingface.co/poolside/Laguna-S-2.1-FP8), [NVFP4](https://huggingface.co/poolside/Laguna-S-2.1-NVFP4), [INT4](https://huggingface.co/poolside/Laguna-S-2.1-INT4) and [GGUF](https://huggingface.co/poolside/Laguna-S-2.1-GGUF) - **OpenMDW-1.1 license**: Use and modify the model and associated materials freely for commercial and non-commercial purposes ([learn more about OpenMDW](https://openmdw.ai/)) ## Model overview - Number of parameters: 118B total, ~8B activated per token - Layers: 48 (12 global attention, 36 sliding-window attention) - Experts: 256 routed (top-10) plus 1 shared expert - Attention: grouped-query, 8 KV heads, head dim 128; per-head softplus output gating - Sliding window: 512 tokens - Context window: 1,048,576 tokens - Vocabulary: 100,352 tokens (Laguna family tokenizer) - Modality: text-to-text - Reasoning: interleaved thinking with preserved thinking ## Benchmark results

benchmarks

| Model | Size | Terminal-Bench 2.1 | SWE-bench Multilingual | SWE-Bench Pro (Public Dataset) | DeepSWE | SWE Atlas (Codebase QnA) | Toolathlon Verified | |---|---|---|---|---|---|---|---| | **Laguna S 2.1** | 118B-A8B | **70.2%** | **78.5%** | **59.4%** | **40.4%** | **46.2%** | **49.7%** | | Tencent Hy3 | 295B-A21B | 71.7% | 75.8% | 57.9% | - | - | - | | Inkling | 975B-A41B | 63.8% | - | 54.3% | - | - | 45.5%* | | Nemotron 3 Ultra | 550B-A55B | 56.4% | 67.7% | - | - | - | 34.3%* | | DeepSeek-V4-Pro Max | 1.6T-A49B | 64.0%* | 76.2% | 55.4% | 9.0%* | 27.2%* | 55.9%* | | Kimi K3 | 2800B-A50B | 88.3% | - | - | 69% | - | - | | Qwen 3.7 Max | - | 74.5%* | 78.3% | 60.6% | - | - | - | | Muse Spark 1.1 | - | 80% | - | 61.5% | 53.3% | 42.2%* | 75.6% | | Claude Fable 5 | - | 88% | - | 80.3% | 70% | - | - | Benchmarks as of 21 July 2026. Laguna S 2.1 in **bold**; a dash (-) marks a benchmark a model was not evaluated on. Scores marked * are as reported by third parties: Terminal-Bench 2.1 and DeepSWE via Artificial Analysis, SWE Atlas via Scale AI's official leaderboard, and Toolathlon Verified via its official leaderboard. Full evaluation trajectories: [trajectories.poolside.ai](https://trajectories.poolside.ai). ## Usage Laguna S 2.1 uses the same `laguna` architecture as Laguna XS 2.1, so the same engine integrations apply (vLLM, SGLang, Transformers, TRT-LLM, llama.cpp). At 118B parameters the BF16 checkpoint needs multiple GPUs (roughly 236GB of weights); quantized variants reduce this substantially. ### vLLM ```shell vllm serve \ --model poolside/Laguna-S-2.1 \ --tensor-parallel-size 4 \ --tool-call-parser poolside_v1 \ --reasoning-parser poolside_v1 \ --enable-auto-tool-choice \ --served-model-name laguna \ --default-chat-template-kwargs '{"enable_thinking": true}' ``` > [!NOTE] > **Optional: speculative decoding with DFlash.** Pair with the > [Laguna S 2.1 DFlash draft model](https://huggingface.co/poolside/Laguna-S-2.1-DFlash) > by adding > `--speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash","num_speculative_tokens":7,"method":"dflash"}'`. ### SGLang ```shell python -m sglang.launch_server \ --model-path poolside/Laguna-S-2.1 \ --tp-size 4 \ --reasoning-parser poolside_v1 \ --tool-call-parser poolside_v1 \ --trust-remote-code ``` ### TRT-LLM ```shell trtllm-serve poolside/Laguna-S-2.1 --trust-remote-code \ --tool_parser poolside_v1 --reasoning_parser laguna ``` Note the flag names differ from vLLM's (`--tool_parser`, and the reasoning parser is `laguna`, not `poolside_v1`). ### llama.cpp GGUF conversions are available at [poolside/Laguna-S-2.1-GGUF](https://huggingface.co/poolside/Laguna-S-2.1-GGUF). Serve with poolside's llama.cpp fork, branch [`laguna`](https://github.com/poolsideai/llama.cpp/tree/laguna), which carries full Laguna support including DFlash speculative decoding. (Base Laguna support is also in upstream review: [ggml-org/llama.cpp#25165](https://github.com/ggml-org/llama.cpp/pull/25165).) ```shell git clone --branch laguna https://github.com/poolsideai/llama.cpp cd llama.cpp && cmake -B build && cmake --build build -j ./build/bin/llama-server -m laguna-s-2.1-Q4_K_M.gguf --jinja --port 8000 # with DFlash speculative decoding: ./build/bin/llama-server -m laguna-s-2.1-Q4_K_M.gguf \ -md laguna-s-2.1-DFlash-BF16.gguf \ --spec-type draft-dflash --spec-draft-n-max 7 -fa on --jinja --port 8000 ``` ### Ollama Run directly from the [Ollama library](https://ollama.com/library/laguna-s-2.1): ```shell ollama run laguna-s-2.1 ``` Quantization variants are available as tags (`q4_K_M`, `q8_0`, `f16`, `mxfp8`, `nvfp4`, `mlx-bf16`), for example `ollama run laguna-s-2.1:q8_0`. The Laguna chat template is baked into the model, so tool-calling and interleaved reasoning work automatically. ## Controlling reasoning Laguna S 2.1 has native reasoning support and works best with *preserved thinking*: keep `reasoning_content` from prior assistant messages in the message history. The model will generally reason before calling tools and between tool calls, and may stop reasoning in follow-up steps if prior thinking blocks are dropped. Thinking is controlled per request via the chat template: ```python extra_body={"chat_template_kwargs": {"enable_thinking": False}} ``` or at the server level with `--default-chat-template-kwargs '{"enable_thinking": true}'`. For agentic coding use cases we recommend enabling thinking and preserving reasoning in the message history. ## License This model is licensed under the [OpenMDW-1.1 License](https://huggingface.co/poolside/Laguna-S-2.1/blob/main/LICENSE.md). ## Intended and Responsible Use Laguna S 2.1 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. Laguna S 2.1 is subject to the [OpenMDW-1.1 License](https://huggingface.co/poolside/Laguna-S-2.1/blob/main/LICENSE.md), and should be used consistently with Poolside's [Acceptable Use Policy](https://poolside.ai/legal/acceptable-use-policy). We advise against circumventing Laguna S 2.1 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case. Please report security vulnerabilities or safety concerns to [security@poolside.ai](mailto:security@poolside.ai).