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Sponsored by
PoYo.AI
AtomicChat / Laguna-XS-2.1-MLX-5bit
Model's Last Updated: July 23 2026
text-generation
Introduction of Laguna-XS-2.1-MLX-5bit
Model Details of Laguna-XS-2.1-MLX-5bit
Laguna XS 2.1 , quantized to MLX (5-bit) by Atomic Chat for Apple Silicon. Built straight from poolside's original weights. Runs fully offline on your Mac.
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.
These are MLX builds for Apple Silicon (M-series), quantized from the original weights, not a repack. Laguna's architecture runs on mlx-vlm (0.6.3+) as a text model; stock
mlx-lmdoes not yet include it.
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 | MLX quants (3-8 bit) for Apple Silicon, built from the original weights with mlx-vlm. |
Scores are poolside's published results for the full-precision base
poolside/Laguna-XS-2.1
. The MLX quants run the same model locally; lower bit-widths trade a little accuracy for size and speed.
This quant
This repo is the 5-bit MLX build (~21 GB). The full ladder (5/6/8-bit) lives in the Laguna XS 2.1 collection .
Get started
-
Atomic Chat
:
open the app, search
AtomicChat/Laguna-XS-2.1-MLX-5bit, pick a quant, hit Use this model . -
mlx-vlm (generate):
pip install -U mlx-vlm python -m mlx_vlm generate --model AtomicChat/Laguna-XS-2.1-MLX-5bit-5bit \ --prompt "Write a Python retry wrapper with exponential backoff." \ --max-tokens 512 --temperature 1.0 -
mlx-vlm (OpenAI-compatible server):
python -m mlx_vlm server --model AtomicChat/Laguna-XS-2.1-MLX-5bit-5bit --host 0.0.0.0 --port 8080 # POST http://localhost:8080/v1/chat/completions with "model": "6bit"
Reasoning is native and on by default. Start the server with
--enable-thinking
(optionally
--thinking-budget N
) to keep it; omit the flag for direct, non-reasoning replies.
Best practices
| Parameter | Value |
|---|---|
| temperature | 1.0 |
| top_k | 20 |
| top_p | 1.0 |
poolside's benchmark settings. For agentic coding, keep reasoning enabled and preserve prior thinking blocks across turns.
How these were made
-
Download
poolside/Laguna-XS-2.1(original BF16 weights). -
Quantize each rung with
python -m mlx_vlm convert --hf-path poolside/Laguna-XS-2.1 -q --q-bits <N> --q-group-size 64.
License
Released by poolside under the OpenMDW-1.1 license, which permits free use, modification and redistribution with attribution. MLX 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 each quant folder.
Runs of AtomicChat Laguna-XS-2.1-MLX-5bit on huggingface.co
381
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30-day runs
More Information About Laguna-XS-2.1-MLX-5bit huggingface.co Model
Url of Laguna-XS-2.1-MLX-5bit
Laguna-XS-2.1-MLX-5bit huggingface.co Url
Provider of Laguna-XS-2.1-MLX-5bit huggingface.co
Other API from AtomicChat
Updated:August 09 2026
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