---
license: openmdw-1.1
base_model: poolside/Laguna-XS-2.1
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - laguna
  - code
  - coder
---

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# Laguna-XS-2.1 — APEX GGUF

**APEX (Adaptive Precision for EXpert Models)** quantizations of [poolside/Laguna-XS-2.1](https://huggingface.co/poolside/Laguna-XS-2.1) — poolside's Laguna XS.2 Mixture-of-Experts model for coding and agentic software engineering.

**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/mudler/apex-quant) | [Technical Report](https://github.com/mudler/apex-quant/blob/main/paper/APEX_Technical_Report.pdf)

> **Requires a recent llama.cpp** with Laguna support ([PR #25165](https://github.com/ggml-org/llama.cpp/pull/25165)). Older builds cannot load `arch=laguna`.

## Available Files

| File | Profile | Best For |
|------|---------|----------|
| Laguna-XS-2.1-APEX-I-Balanced.gguf | I-Balanced | Best overall — imatrix-enhanced |
| Laguna-XS-2.1-APEX-I-Quality.gguf | I-Quality | Highest quality with imatrix |
| Laguna-XS-2.1-APEX-Quality.gguf | Quality | Highest quality (no imatrix) |
| Laguna-XS-2.1-APEX-Balanced.gguf | Balanced | General purpose |
| Laguna-XS-2.1-APEX-I-Compact.gguf | I-Compact | Consumer GPUs, imatrix-enhanced |
| Laguna-XS-2.1-APEX-Compact.gguf | Compact | Consumer GPUs |
| Laguna-XS-2.1-APEX-I-Mini.gguf | I-Mini | Smallest viable, fastest inference |

## What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention, dense FFN) and applies a layer-wise precision gradient — edge layers get higher precision, middle layers compress more aggressively. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

In MoE models the routed-expert FFN tensors dominate the weight budget but only ~8/256 experts fire per token, so APEX compresses middle-layer routed experts hardest while preserving edge layers, attention, and the always-active shared expert.

## APEX layout for Laguna

Laguna XS.2 has a structure APEX handles explicitly:

- **Layer 0 is a leading dense FFN** (no experts) — pinned to **Q8_0**, since every token traverses it.
- **Layers 1–39 are MoE** — 256 routed experts + a shared expert, 8 active per token, sigmoid gating.
- **Shared expert** (`ffn_*_shexp`) kept at **Q8_0** on every tier (always active).
- **Routed experts** follow the 5+5 symmetric edge gradient (higher precision at the first/last layers, most aggressive in the middle).
- **Router** (`ffn_gate_inp`), norms and the `exp_probs_b` gating bias stay at full precision.

## Architecture

- **Model**: Laguna-XS-2.1 (`LagunaForCausalLM`, arch `laguna`)
- **Layers**: 40 (1 dense + 39 MoE) · **Experts**: 256 routed + 1 shared (8 active)
- **Attention**: 48 heads / 8 KV, per-layer output gate, hybrid full + sliding-window, YaRN rope
- **Vocab**: 100352 · text-only
- **Calibration**: v1.3 diverse dataset

## Run with LocalAI

```bash
local-ai run mudler/Laguna-XS-2.1-APEX-GGUF@Laguna-XS-2.1-APEX-I-Balanced.gguf
```

## Credits

APEX is brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. Built on [llama.cpp](https://github.com/ggerganov/llama.cpp). Base model by [poolside](https://huggingface.co/poolside).