AtomicChat/Laguna-XS-2.1-GGUF overview

---

license: openmdw-1.1

license_link: https://huggingface.co/poolside/Laguna-XS-2.1/blob/main/LICENSE.md

thumbnail: https://huggingface.co/AtomicChat/Laguna-XS-2.1-GGUF/resolve/main/hero.png

base_model:

• poolside/Laguna-XS-2.1

base_model_relation: quantized

quantized_by: AtomicChat

pipeline_tag: text-generation

library_name: gguf

tags:

• atomic-chat
• laguna
• poolside
• gguf
• llama.cpp
• quantized

---

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<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Laguna-XS-2.1-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>

<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Laguna-XS-2.1-GGUF/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>

<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Laguna-XS-2.1-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>

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<img src="https://huggingface.co/AtomicChat/Laguna-XS-2.1-GGUF/resolve/main/hero.png" alt="Laguna XS 2.1" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

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<a href="https://huggingface.co/poolside/Laguna-XS-2.1"><strong>Base model: poolside/Laguna-XS-2.1</strong></a>

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Laguna XS 2.1, self-quantized to GGUF 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

• 33.4B parameters: the weights this repo quantizes.
• Context length: 262,144 tokens (256K), as published by Poolside.
• 40 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: 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.

> [!NOTE]

> These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

> [!IMPORTANT]

> Always pass --jinja so the Laguna XS 2.1 chat template is applied. Without it the model can emit malformed turns.

Model Overview

| Property | Value |

|---|---|

| Base model | poolside/Laguna-XS-2.1 |

| Parameters | 33.4B |

| Layers | 40 |

| Experts | 256 routed (top-8) |

| Sliding window | 512 tokens |

| Context length | 262,144 tokens (256K) |

| Vocabulary | 100,352 |

| Modalities | Text |

| Architecture | Mixture-of-Experts, 256 experts (top-8), hybrid sliding-window (512) and global attention, 48 attention heads over 8 KV heads, LagunaForCausalLM |

| This repo | GGUF quants (imatrix). Quants: Q3_K_M, Q4_K_M, Q5_K_M, Q6_K, Q8_0 |

<img src="https://huggingface.co/AtomicChat/Laguna-XS-2.1-GGUF/resolve/main/benchmark.png" alt="Laguna XS 2.1 benchmark scores" style="width:100%; max-width:900px;"/>

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

Choosing a quant

| Quant | Size | Notes |

|---|---|---|

| Q3_K_M | 16.1 GB | Low quality but usable. |

| Q4_K_M | 20.3 GB | Recommended default. Best balance of size, speed and quality. |

| Q5_K_M | 23.8 GB | Higher quality, low loss. |

| Q6_K | 27.5 GB | Near lossless, noticeably lighter than Q8_0. |

| Q8_0 | 35.6 GB | Effectively lossless, reference quality. |

> [!TIP]

> Pick the largest file that fits your (V)RAM with room for context. Q4_K_M is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Laguna XS 2.1 locally with:

• Atomic Chat: the easiest path. Open the app, search AtomicChat/Laguna-XS-2.1-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AtomicChat/Laguna-XS-2.1-GGUF:Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AtomicChat/Laguna-XS-2.1-GGUF:Q4_K_M
• LM Studio / Jan: search the repo id, download any quant.

Best practices

| Parameter | Value |

|---|---|

| temperature | 1.0 |

| top_p | 1 |

| top_k | 20 |

| min_p | 0.0 |

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

Run in llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/Laguna-XS-2.1-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download poolside/Laguna-XS-2.1 (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over our calibration corpus.
4. Quantize the ladder with --imatrix.

License

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