Instructions to use mudler/Darwin-36B-Opus-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mudler/Darwin-36B-Opus-APEX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mudler/Darwin-36B-Opus-APEX-GGUF",
	filename="Darwin-36B-Opus-APEX-Balanced.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mudler/Darwin-36B-Opus-APEX-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mudler/Darwin-36B-Opus-APEX-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf mudler/Darwin-36B-Opus-APEX-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mudler/Darwin-36B-Opus-APEX-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf mudler/Darwin-36B-Opus-APEX-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mudler/Darwin-36B-Opus-APEX-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf mudler/Darwin-36B-Opus-APEX-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mudler/Darwin-36B-Opus-APEX-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mudler/Darwin-36B-Opus-APEX-GGUF:F16

Use Docker

docker model run hf.co/mudler/Darwin-36B-Opus-APEX-GGUF:F16

LM Studio
Jan
Ollama
How to use mudler/Darwin-36B-Opus-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Darwin-36B-Opus-APEX-GGUF:F16
```

Unsloth Studio new

How to use mudler/Darwin-36B-Opus-APEX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mudler/Darwin-36B-Opus-APEX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mudler/Darwin-36B-Opus-APEX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mudler/Darwin-36B-Opus-APEX-GGUF to start chatting

Pi new

How to use mudler/Darwin-36B-Opus-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mudler/Darwin-36B-Opus-APEX-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mudler/Darwin-36B-Opus-APEX-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mudler/Darwin-36B-Opus-APEX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf mudler/Darwin-36B-Opus-APEX-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mudler/Darwin-36B-Opus-APEX-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use mudler/Darwin-36B-Opus-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Darwin-36B-Opus-APEX-GGUF:F16
```

Lemonade

How to use mudler/Darwin-36B-Opus-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Darwin-36B-Opus-APEX-GGUF:F16

Run and chat with the model

lemonade run user.Darwin-36B-Opus-APEX-GGUF-F16

List all available models

lemonade list

⚡ Each donation = another big MoE quantized

I host 30+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory) — enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant.
If APEX quants are useful to you, your support directly funds those bigger runs.

🎉 Patreon (Monthly) | ☕ Buy Me a Coffee | ⭐ GitHub Sponsors

💚 Big thanks to Hugging Face for generously donating additional storage — much appreciated.

Darwin-36B-Opus — APEX GGUF

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

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

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 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

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

Downloads last month: -

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

16-bit

View +8 variants

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Model tree for mudler/Darwin-36B-Opus-APEX-GGUF

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

FINAL-Bench/Darwin-36B-Opus

Quantized

(5)

this model