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AtomicChat/
Laguna-S-2.1-NVFP4

Name: Laguna-S-2.1-NVFP4
Availability: InStock

poolside/Laguna-S-2.1

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Base model: poolside/Laguna-S-2.1

Laguna S 2.1, self-quantized to NVFP4 by Atomic Chat. Built straight from Poolside's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

117.6B parameters: the weights this repo quantizes.
Context length: 1,048,576 tokens (1M), as published by Poolside.
48 layers: Mixture-of-Experts, hybrid sliding-window (512) and global attention.
Full imatrix ladder: every quant is calibrated with an importance matrix.
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.
Native reasoning support: interleaved thinking between tool calls, with per-request control via enable_thinking.
Speculative decoding: a trained DFlash draft model is available for lower-latency serving.

[!NOTE] These NVFP4s are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

Model Overview

Property	Value
Base model	`poolside/Laguna-S-2.1`
Parameters	117.6B
Layers	48
Experts	256 routed (top-10)
Sliding window	512 tokens
Context length	1,048,576 tokens (1M)
Vocabulary	100,352
Modalities	Text
Architecture	Mixture-of-Experts, 256 experts (top-10), hybrid sliding-window (512) and global attention, 48 attention heads over 8 KV heads, `LagunaForCausalLM`
This repo	NVFP4 weights

Benchmarks

Benchmark	Score
Laguna S 2.1	70.2%
Tencent Hy3	71.7%
Inkling	63.8%
Nemotron 3 Ultra	56.4%
DeepSeek-V4-Pro Max	64.0%
Kimi K3	88.3%
Qwen 3.7 Max	74.5%
Muse Spark 1.1	80%
Claude Fable 5	88%

Scores are Poolside's published results for the base poolside/Laguna-S-2.1, not our own measurements. Quantization preserves the large majority of this; Q4_K_M and up stay close to full precision.

Get started

Atomic Chat: search AtomicChat/Laguna-S-2.1-NVFP4 and hit Use this model.
vLLM: vllm serve AtomicChat/Laguna-S-2.1-NVFP4 --max-model-len 8192

Best practices

Parameter	Value
temperature	1.0
top_p	1.0
top_k	20
min_p	0.0

Poolside's recommended sampling configuration for poolside/Laguna-S-2.1.

How these were made

Download poolside/Laguna-S-2.1 (original weights).
Quantize to NVFP4 with llm-compressor over a calibration corpus.

License

Original model by Poolside, released under the OpenMDW-1.1 license. Full terms: OpenMDW-1.1. Quantized by Atomic Chat.

AtomicChat/Laguna-S-2.1-NVFP4