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Sponsored by
APIMart
AtomicChat / Laguna-XS-2.1-GGUF
Model's Last Updated: July 23 2026
text-generation
Introduction of Laguna-XS-2.1-GGUF
Model Details of Laguna-XS-2.1-GGUF
Laguna XS 2.1 , quantized to GGUF by Atomic Chat with an importance matrix. Built straight from poolside's original weights. Runs fully offline on your machine.
Highlights
- 33B total / 3B active Mixture-of-Experts for agentic coding and long-horizon work on a local machine.
- Mixed attention layout: 40 layers, 10 global + 30 sliding-window (3:1 ratio), sigmoid gating with per-layer rotary scales.
- 256 experts + 1 shared expert , sliding window of 512 tokens.
- 262,144-token context.
- Native interleaved reasoning , enable or disable per request.
- Upgraded from Laguna XS.2 : +5.4% on SWE-bench Multilingual and stronger terminal-style performance.
Laguna is a new architecture. It runs in Atomic Chat 1.1.135+ out of the box, or in a build of llama.cpp with Laguna support ( PR #25165 ). Stock
llama.cppreleases do not load it yet. Always pass--jinjaso the chat template is applied.
Model Overview
| Property | Value |
|---|---|
| Base model |
poolside/Laguna-XS-2.1
|
| Total parameters | 33B (3B active per token) |
| Architecture | Laguna MoE, mixed sliding-window/global attention |
| Experts | 256 + 1 shared |
| Layers | 40 (10 global, 30 sliding-window) |
| Sliding window | 512 tokens |
| Context length | 262,144 |
| Optimizer | Muon |
| This repo | imatrix GGUF quants for llama.cpp, built from the original weights. |
Scores are poolside's published results for the full-precision base
poolside/Laguna-XS-2.1
. The GGUF quants run the same model locally; lower bit-widths trade a little accuracy for size and speed.
Choosing a quant
All rungs are quantized with an importance matrix (imatrix) calibrated on a general-purpose dataset.
| Quant | Size | Notes |
|---|---|---|
Q3_K_M
|
15 GB | smallest, usable |
Q4_K_M
|
19 GB | fast, low memory |
Q5_K_M
|
23 GB | balanced |
Q6_K
|
26 GB | recommended sweet spot |
Q8_0
|
34 GB | closest to the original |
Q6_Kis the best quality/size balance for most setups. UseQ3_K_M/Q4_K_Mon tighter memory;Q8_0when you want maximum fidelity.
Get started
-
Atomic Chat
:
open the app (1.1.135+), search
AtomicChat/Laguna-XS-2.1-GGUF, pick a quant, hit Use this model . -
llama.cpp (build with Laguna support):
llama-cli -m Laguna-XS-2.1-Q6_K.gguf --jinja \ -p "Write a Python retry wrapper with exponential backoff." -n 512 -
llama.cpp server:
llama-server -m Laguna-XS-2.1-Q6_K.gguf --jinja -c 8192 # OpenAI-compatible endpoint at http://localhost:8080/v1/chat/completions
Reasoning is native and on by default. For agentic coding, keep reasoning enabled and preserve prior thinking blocks across turns.
Best practices
| Parameter | Value |
|---|---|
| temperature | 1.0 |
| top_k | 20 |
| top_p | 1.0 |
poolside's benchmark settings.
How these were made
-
Download poolside's official
Laguna-XS-2.1-BF16.gguf. -
Build an importance matrix with
llama-imatrixon a general calibration set. -
Quantize each rung with
llama-quantize --imatrixfrom the BF16 GGUF.
License
Released by poolside under the OpenMDW-1.1 license, which permits free use, modification and redistribution with attribution. GGUF conversion by Atomic Chat. This is an unofficial community quantization and is not endorsed by poolside; the original
LICENSE.md
and notices of origin are retained in this repo.
Runs of AtomicChat Laguna-XS-2.1-GGUF on huggingface.co
620
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More Information About Laguna-XS-2.1-GGUF huggingface.co Model
Url of Laguna-XS-2.1-GGUF
Laguna-XS-2.1-GGUF huggingface.co Url
Provider of Laguna-XS-2.1-GGUF huggingface.co
Other API from AtomicChat
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