poolside/Laguna-XS.2
text-generation model by poolside
From the publisher
Model card & documentation
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Get an API key · Release blog post · Technical report Laguna XS.2 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. We used the highest publicly-referenced scores for all comparison models across each benchmark. In almost all cases these were official scores published in release blog posts or equivalent, with the exception of Gemma 4 31B IT where the highest published scores were reported by the Qwen team 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 was completed using the Laude Institute’s Harbor Framework with our agent harness, using…
Read the full model card ↗ · Preview checked 2026-10-29T07:04:45.287Z
Inside the original model card — Document outline
- Laguna XS.2
- Highlights
- Model overview
- Benchmark results
- Usage
- pool
- Feedback and issues
- Local deployment
- vLLM
- SGLang
- Speculative decoding (DFlash)
- Transformers
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Model overview
poolside/Laguna-XS.2 is a text-generation model repository published by poolside on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-generation
- Library
- transformers
- Recent downloads (30 days)
- 1,017,267
- Cumulative likes
- 320
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 33,442,617,088
- Architecture
- LagunaForCausalLM
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-04-23T20:50:45.000Z
- Last modified
- 2026-09-22T08:53:28.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-01T06:51:29.042Z. Daily imports are snapshots, not real-time monitoring.
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