mudler/Darwin-36B-Opus-APEX-GGUF

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Darwin-36B-Opus — APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of FINAL-Bench/Darwin-36B-Opus.

Brought to you by the [LocalAI](https://github.com/mudler/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 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 (first/last 5) and keeping attention, SSM/Mamba, and shared expert tensors at higher precision.

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

Nano (experimental tier)

The APEX Nano tier pushes mid-layer routed experts to IQ2_XXS (2.06 bpw), near-edge to IQ2S, edges to Q3K, with shared experts kept at Q5_K. About 20% smaller than Mini with modest quality cost — viable only on MoE thanks to sparse per-token expert activation. Requires imatrix.

Benchmarks pending. Feedback welcome.

Architecture

• —Base: Qwen 3.5 MoE (Qwen3_5MoeForCausalLM) — evolutionary-merge reasoning fine-tune
• —Layers: 40
• —Experts: 256 routed (8 active per token)
• —Total Parameters: ~36B
• —Active Parameters: ~3B per token
• —Hidden size: 2048
• —Attention: Hybrid (full attention every 4th layer, linear/Mamba otherwise)
• —APEX Config: 5+5 symmetric edge gradient across 40 layers
• —Calibration: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)

Run with LocalAI

local-ai run mudler/Darwin-36B-Opus-APEX-GGUF@Darwin-36B-Opus-APEX-I-Balanced.gguf

Credits

• —Base / evolutionary merge: FINAL-Bench
• —APEX quantization: LocalAI team
• —Built on llama.cpp