poolside / Laguna-XS-2.1-INT4

Use on OpenRouter · Release blog post

Laguna XS 2.1-INT4

Laguna XS 2.1-INT4 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.

This is the INT4 variant with an FP8-quantized KV cache. The BF16 , FP8 and NVFP4 variants are also available on Hugging Face.

Highlights

• Mixed SWA and global attention layout : Laguna XS 2.1 uses sigmoid gating with per-layer rotary scales, enabling mixed SWA (Sliding Window Attention) and global attention layers in a 3:1 ratio (across 40 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 33B total parameters and 3B activated, Laguna XS 2.1 is compact enough to run on a Mac with 36 GB of RAM. 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 )

---

Model overview

• Training: pre-training, post-training and reinforcement learning stages
• Number of parameters: 33B total with 3B activated per token
• Optimizer: Muon
• Layers: 40 layers (10 layers with global attention, 30 layers with sliding window attention)
• Experts: 256 experts with 1 shared expert
• Sliding Window: 512 tokens
• Modality: text-to-text
• Context window: 262,144 tokens
• Reasoning support: interleaved thinking with preserved thinking

Benchmark results

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.

Expand for benchmarking methodology

All benchmarking for Laguna XS 2.1 was completed using Laude Institute’s Harbor Framework with our agent harness , with a maximum of 500 steps and sandboxed execution. The same sampling parameters were used for all Laguna XS 2.1 benchmarking: temperature=1.0, top_k=20 and top_p=1, with thinking mode enabled and a context length of 256K tokens. All tasks were run in their own sandbox using 8 GB RAM/2 CPUs, with the exception of Terminal-Bench 2.0, which used 48 GB RAM/32 CPUs.

Some base task images and verifiers were patched to fix infrastructure reliability issues inherent in task setup, such as rate limits on third-party dependencies in external registries used by the verifier. All four agentic benchmarks were run with patched images. We also ran a reward-hack judge post-hoc on Laguna XS 2.1 evaluation runs and did not find significant reward hacking after joint judge review and manual review.

• SWE-bench Verified: mean pass@1 averaged over 4 attempts per task
• SWE-bench Multilingual: mean pass@1 averaged over 4 attempts per task
• SWE-Bench Pro: mean pass@1 averaged over 2 attempts per task
• Terminal-Bench 2.0: mean pass@1 averaged over 5 attempts per task; 48 GB RAM/32 CPUs

Quantization quality

Laguna XS 2.1-INT4 was evaluated against the unquantized BF16 checkpoint on the same four agentic benchmarks. Scores are mean pass@1; the ± figure is the run-to-run variation and RH is the share of runs flagged by our post-hoc reward-hack judge (see the benchmarking methodology above).

Usage

Laguna XS 2.1-INT4 has launch-day support in vLLM and Transformers.

For complete usage instructions, see the main Laguna XS 2.1 model card .

Local deployment

Laguna XS 2.1-INT4 is supported in vLLM and Transformers. Use Laguna XS 2.1 with Ollama (with MLX support) or Llama.cpp (BF16 and Q4_K_M only) for the best results on your local machine.

vLLM

The full vLLM recipe is on the main Laguna XS 2.1 model card and on the vLLM recipes page . Quantization is detected automatically from quantization_config in this checkpoint, so the same command works with poolside/Laguna-XS-2.1-INT4 substituted for the model ID. No extra flags required.

The FP8-quantized KV cache requires vLLM >= 0.22.0. Earlier versions produce scrambled output on non-Hopper GPUs because of a per-layer attention-head count bug, fixed in vllm#42650 . On older vLLM, disable the FP8 KV cache by adding --kv-cache-dtype-skip-layers $(seq 0 39) .

SGLang

The full SGLang recipe is on the main Laguna XS 2.1 model card and on the SGLang cookbook . SGLang reads the checkpoint quantization_config, so you can use the same launch command after replacing the model ID

Transformers

The full Transformers recipe is on the main Laguna XS 2.1 model card . Substitute poolside/Laguna-XS-2.1-INT4 for the model ID; quantization is detected automatically from quantization_config .

Ollama

Available on the Ollama library .

macOS (Metal) users: Chat ( ollama run / /api/chat ) works as expected on Linux/CUDA. On macOS/Metal it may currently return empty output; the root cause is not yet fully understood and we're investigating it with the Ollama team. On a Mac, use a Linux/CUDA host, or the /api/generate endpoint with "raw": true .

Controlling reasoning

Laguna XS 2.1-INT4 uses the same reasoning controls (interleaved thinking, preserved reasoning, and the enable_thinking flag) as the base model. See the Controlling reasoning section of the main Laguna XS 2.1 model card.

License

This model is licensed under the OpenMDW-1.1 License .

Intended and Responsible Use

Laguna XS 2.1-INT4 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 XS 2.1-INT4 is subject to the OpenMDW-1.1 License , and should be used consistently with Poolside's Acceptable Use Policy . We advise against circumventing Laguna XS 2.1-INT4 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case.

Please report security vulnerabilities or safety concerns to [email protected] .