Nex-N2-mini-APEX-GGUF

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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

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/[email protected]

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.