| --- |
| library_name: transformers |
| inference: false |
| extra_gated_description: >- |
| To learn more about how we process your personal data, please read our <a |
| href="https://poolside.ai/legal/privacy">Privacy Policy</a>. |
| tags: |
| - laguna-s-2.1 |
| license: openmdw-1.1 |
| pipeline_tag: text-generation |
| --- |
| |
| <p align="center"> |
| <img alt="poolside-banner" src="https://poolside.ai/assets/laguna/laguna-s-2-1-banner.svg" width="800px"> |
| </p> |
|
|
| <p align="center"> |
| <a href="https://openrouter.ai/poolside/laguna-s-2.1"><strong>Use on OpenRouter</strong></a> 路 |
| <a href="https://vercel.com/ai-gateway/models/laguna-s-2.1"><strong>Use on Vercel AI Gateway</strong></a> 路 |
| <a href="https://poolside.ai/blog/introducing-laguna-s-2-1"><strong>Release blog post</strong></a> |
| </p> |
|
|
| <br> |
|
|
| # Laguna S 2.1-base |
|
|
| Laguna S 2.1-base is the base (pre-trained, not post-trained) model behind |
| [Laguna S 2.1](https://huggingface.co/poolside/Laguna-S-2.1). It is a 118B total |
| parameter Mixture-of-Experts model with roughly 8B activated parameters per token, |
| using interleaved full and sliding-window attention over 48 layers, with 256 routed |
| experts and 1 shared expert. |
|
|
| Please note that we are not making the base model available publicly. Researchers who |
| wish to use the model can reach out to us at models@poolside.ai. |
|
|
| This is a base model. It has not been instruction tuned and has no chat, reasoning |
| or tool-calling behaviour. For those, use [Laguna S 2.1](https://huggingface.co/poolside/Laguna-S-2.1). |
|
|
| ## Benchmark results |
|
|
| Short context: |
|
|
| | Benchmark | Score | |
| |---|---| |
| | PIQA | 84.8% | |
| | WinoGrande | 84.5% | |
| | ARC-e | 98.2% | |
| | ARC-c | 93.8% | |
| | HellaSwag | 85.8% | |
| | MMLU | 82.9% | |
| | GPQA-D | 43.6% | |
| | MultiPL-E | 60.4% | |
| | EvalPlus | 71.6% | |
| | BigCodeBench | 57.0% | |
| | MATH | 64.7% | |
| | GSM8K | 88.7% | |
| | APT-Bench 4k | 39.9% | |
|
|
| Long context: |
|
|
| | Benchmark | Score | |
| |---|---| |
| | APT-Bench 32k | 58.8% | |
| | RULER 32k | 81.6% | |
| | GSM-Infinite 16k | 33.8% | |
| | LongBench 32k | 41.6% | |
| | APT-Bench 128k | 47.5% | |
| | RULER 128k | 72.8% | |
| | GSM-Infinite 64k | 24.3% | |
| | LongBench 128k | 37.3% | |
| | HELMET 128k | 51.2% | |
|
|
| ## License |
|
|
| This model is licensed under the [OpenMDW-1.1 License](https://huggingface.co/poolside/Laguna-S-2.1-base/blob/main/LICENSE.md). |
|
|
| ## Intended and Responsible Use |
|
|
| Laguna S 2.1-base 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-base is subject to the [OpenMDW-1.1 License](https://huggingface.co/poolside/Laguna-S-2.1-base/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-base 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). |
|
|
