---

license: apache-2.0

base_model: nex-agi/Nex-N2-mini

tags:

- gguf

- quantized

- apex

- moe

- mixture-of-experts

- qwen3

- vlm

- vision

- agentic

---

<!-- apex-banner-v2 -->

<div style="background-color: #f59e0b; color: white; padding: 20px; border-radius: 10px; text-align: center; margin: 20px 0;">

<h2 style="color: white; margin: 0 0 10px 0;">⚡ Each donation = another big MoE quantized</h2>

<p style="font-size: 18px; margin: 0 0 15px 0;">I host <b>30+ free APEX MoE quantizations</b> as independent research. My only local hardware is an <b>NVIDIA DGX Spark</b> (122 GB unified memory) — enough for ~30-50B-class MoEs, but <b>bigger ones (200B+) require rented compute</b> on H100/H200/Blackwell, typically $20-100 per quant.<br>If APEX quants are useful to you, your support directly funds those bigger runs.</p>

<p style="font-size: 20px; margin: 0;">

<a href="https://www.patreon.com/cw/mudler" style="color: white; text-decoration: underline;">🎉 Patreon (Monthly)</a> &nbsp;|&nbsp;

<a href="https://www.buymeacoffee.com/mudler" style="color: white; text-decoration: underline;">☕ Buy Me a Coffee</a> &nbsp;|&nbsp;

<a href="https://github.com/sponsors/mudler" style="color: white; text-decoration: underline;">⭐ GitHub Sponsors</a>

</p>

</div>

Nex-N2-mini — APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of nex-agi/Nex-N2-mini — an agentic model with Agentic Thinking, post-trained on Qwen3.5-35B-A3B-Base for first-tier coding and long-horizon agentic tasks.

Brought to you by the LocalAI team | APEX Project | Technical Report

Available Files

| File | Profile | Best For |

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

| Nex-N2-mini-APEX-I-Balanced.gguf | I-Balanced | Best overall — imatrix-enhanced, lowest worst-case divergence |

| Nex-N2-mini-APEX-I-Quality.gguf | I-Quality | Highest quality with imatrix |

| Nex-N2-mini-APEX-Quality.gguf | Quality | Highest quality (no imatrix) |

| Nex-N2-mini-APEX-Balanced.gguf | Balanced | General purpose |

| Nex-N2-mini-APEX-I-Compact.gguf | I-Compact | Consumer GPUs, imatrix-enhanced |

| Nex-N2-mini-APEX-Compact.gguf | Compact | Consumer GPUs |

| Nex-N2-mini-APEX-I-Mini.gguf | I-Mini | Smallest viable, fastest inference |

| mmproj.gguf | Vision projector | Required for image understanding |

What is APEX?

APEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient — edge layers (first/last 5) get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).

The key insight: in MoE models, expert FFN tensors make up the bulk of model weight but only ~8/256 experts activate per token. APEX compresses middle-layer experts more aggressively while preserving edge layers and keeping attention, SSM/Mamba, and shared-expert tensors at higher precision.

See the APEX project for full details, technical report, and scripts.

Architecture

• Model: Nex-N2-mini (Qwen3_5MoeForConditionalGeneration, post-trained on Qwen3.5-35B-A3B-Base)
• Layers: 40
• Experts: 256 routed + 1 shared (8 active per token)
• Total Parameters: ~35B
• Active Parameters: ~3B per token
• Attention: Hybrid (full attention every 4th layer, linear otherwise)
• Vision: Built-in vision encoder (mmproj included)
• APEX Config: 5+5 symmetric edge gradient across 40 layers
• Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, agentic traces, Wikipedia)

Run with LocalAI

local-ai run mudler/Nex-N2-mini-APEX-GGUF@Nex-N2-mini-APEX-I-Balanced.gguf

Credits

APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp. Base model by Nex-AGI.