Instructions to use mudler/Carnice-MoE-35B-A3B-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mudler/Carnice-MoE-35B-A3B-APEX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mudler/Carnice-MoE-35B-A3B-APEX-GGUF",
	filename="Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-APEX-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf mudler/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-APEX-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf mudler/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-APEX-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf mudler/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-APEX-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mudler/Carnice-MoE-35B-A3B-APEX-GGUF:F16

Use Docker

docker model run hf.co/mudler/Carnice-MoE-35B-A3B-APEX-GGUF:F16

LM Studio
Jan
Ollama
How to use mudler/Carnice-MoE-35B-A3B-APEX-GGUF with Ollama:
```
ollama run hf.co/mudler/Carnice-MoE-35B-A3B-APEX-GGUF:F16
```

Unsloth Studio

How to use mudler/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-APEX-GGUF to start chatting

How to use mudler/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-APEX-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mudler/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-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/Carnice-MoE-35B-A3B-APEX-GGUF:F16

Run Hermes

hermes

Docker Model Runner
How to use mudler/Carnice-MoE-35B-A3B-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/mudler/Carnice-MoE-35B-A3B-APEX-GGUF:F16
```

Lemonade

How to use mudler/Carnice-MoE-35B-A3B-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mudler/Carnice-MoE-35B-A3B-APEX-GGUF:F16

Run and chat with the model

lemonade run user.Carnice-MoE-35B-A3B-APEX-GGUF-F16

List all available models

lemonade list

⚡ Each donation = another big MoE quantized

I host 25+ 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.

Carnice MoE 35B-A3B APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of samuelcardillo/Carnice-MoE-35B-A3B.

Brought to you by the LocalAI team | APEX Project

Available Files

File	Profile	Size	Best For
Carnice-MoE-35B-A3B-APEX-I-Quality.gguf	I-Quality	21 GB	Highest quality with imatrix
Carnice-MoE-35B-A3B-APEX-Quality.gguf	Quality	21 GB	Highest quality standard
Carnice-MoE-35B-A3B-APEX-I-Balanced.gguf	I-Balanced	24 GB	Best overall quality/size ratio
Carnice-MoE-35B-A3B-APEX-Balanced.gguf	Balanced	24 GB	General purpose
Carnice-MoE-35B-A3B-APEX-I-Compact.gguf	I-Compact	16 GB	Consumer GPUs, best quality/size
Carnice-MoE-35B-A3B-APEX-Compact.gguf	Compact	16 GB	Consumer GPUs
Carnice-MoE-35B-A3B-APEX-I-Mini.gguf	I-Mini	13 GB	Smallest viable, fastest inference
Carnice-MoE-35B-A3B-F16.gguf	F16	65 GB	Full precision reference

Benchmark Results (Native Evals)

Model	Size	PPL ↓	KL ↓	HellaSwag	WinoGrande	MMLU	ARC-C	TruthfulQA	pp512 t/s	tg128 t/s
F16 (ref)	65G	6.16	-	-	-	-	-	-	2315	109.1
APEX-Quality	21G	6.2	0.010	83.5	74.0	40.9	56.9	34.0	4717	134.2
APEX-I-Quality	21G	6.2	0.009	83.0	75.0	40.3	55.5	34.3	4734	132.6
APEX-Balanced	24G	6.2	0.007	83.0	73.8	41.1	54.5	33.8	4572	130.3
APEX-I-Balanced	24G	6.2	0.006	83.5	74.8	40.6	54.2	34.0	4539	128.7
APEX-Compact	16G	6.4	0.045	82.8	75.5	40.8	55.9	34.0	4516	132.1
APEX-I-Compact	16G	6.3	0.032	83.0	73.8	41.2	56.2	34.9	4352	130.6
APEX-I-Mini	13G	6.6	0.071	82.0	72.2	40.6	53.8	33.7	4293	133.1

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).

See the APEX project for full details.

Architecture

Base Model: samuelcardillo/Carnice-MoE-35B-A3B
Architecture: Qwen3.5-MoE 35B-A3B
Layers: 40
Experts: 256 routed (8 active per token)
Total Parameters: 35B
Active Parameters: ~3B per token
APEX Config: 6+6 symmetric edge gradient across 40 layers
Calibration: v1.2 diverse dataset

Run with LocalAI

local-ai run mudler/Carnice-MoE-35B-A3B-APEX-GGUF@Carnice-MoE-35B-A3B-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.

Downloads last month: 428

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

16-bit

View +7 variants

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Model tree for mudler/Carnice-MoE-35B-A3B-APEX-GGUF

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Finetuned

samuelcardillo/Carnice-MoE-35B-A3B

Quantized

(4)

this model

Collection including mudler/Carnice-MoE-35B-A3B-APEX-GGUF

APEX Quants (GGUF)

Collection

MoE models quantized with the APEX Quantization technique ( https://github.com/mudler/apex-quant ) • 36 items • Updated 3 days ago • 106