---
license: apache-2.0
base_model: FINAL-Bench/Darwin-36B-Opus
tags:
  - gguf
  - quantized
  - apex
  - moe
  - mixture-of-experts
  - qwen3
  - qwen3.5
  - reasoning
  - chain-of-thought
  - evolutionary-merge
  - darwin
---

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# Darwin-36B-Opus — APEX GGUF

**APEX (Adaptive Precision for EXpert Models)** quantizations of [FINAL-Bench/Darwin-36B-Opus](https://huggingface.co/FINAL-Bench/Darwin-36B-Opus).

**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)

## Available Files

| File | Profile | Size | Best For |
|------|---------|------|----------|
| Darwin-36B-Opus-APEX-I-Balanced.gguf | I-Balanced | 24 GB | Best overall quality/size ratio |
| Darwin-36B-Opus-APEX-Balanced.gguf | Balanced | 24 GB | General purpose |
| Darwin-36B-Opus-APEX-I-Quality.gguf | I-Quality | 22 GB | Highest quality with imatrix |
| Darwin-36B-Opus-APEX-Quality.gguf | Quality | 22 GB | Highest quality standard |
| Darwin-36B-Opus-APEX-I-Compact.gguf | I-Compact | 16 GB | Consumer GPUs, best quality/size |
| Darwin-36B-Opus-APEX-Compact.gguf | Compact | 16 GB | Consumer GPUs |
| Darwin-36B-Opus-APEX-I-Mini.gguf | I-Mini | 13 GB | Smallest "safe" tier |
| Darwin-36B-Opus-APEX-I-Nano.gguf | **I-Nano** | 11 GB | Experimental — IQ2_XXS mid-layer experts |
| Darwin-36B-Opus-F16.gguf | F16 reference | 65 GB | Full-precision reference |

## 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](https://github.com/mudler/apex-quant) 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 IQ2_S, edges to Q3_K, 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

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

## Credits

- **Base / evolutionary merge**: [FINAL-Bench](https://huggingface.co/FINAL-Bench)
- **APEX quantization**: [LocalAI](https://github.com/mudler/LocalAI) team
- Built on [llama.cpp](https://github.com/ggerganov/llama.cpp)