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Sponsored by
APIMaster
AtomicChat / Laguna-S-2.1-MLX-4bit
Model's Last Updated: July 23 2026
text-generation
Introduction of Laguna-S-2.1-MLX-4bit
Model Details of Laguna-S-2.1-MLX-4bit
Laguna S 2.1 , self-quantized to MLX 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.
These MLXs 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 | MLX 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-MLX-4bitand hit Use this model . -
mlx-lm:
mlx_lm.generate --model AtomicChat/Laguna-S-2.1-MLX-4bit --prompt "Hello" --max-tokens 512 -
Server:
mlx_lm.server --model AtomicChat/Laguna-S-2.1-MLX-4bit --port 8080
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). -
Convert and quantize with
mlx_lm.converton our pipeline.
License
Original model by Poolside, released under the OpenMDW-1.1 license. Full terms: OpenMDW-1.1 . Quantized by Atomic Chat.
Runs of AtomicChat Laguna-S-2.1-MLX-4bit on huggingface.co
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More Information About Laguna-S-2.1-MLX-4bit huggingface.co Model
Url of Laguna-S-2.1-MLX-4bit
Laguna-S-2.1-MLX-4bit huggingface.co Url
Provider of Laguna-S-2.1-MLX-4bit huggingface.co
Other API from AtomicChat
Updated:August 27 2026
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