Instructions to use pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF
# Run inference directly in the terminal:
llama cli -hf pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF
# Run inference directly in the terminal:
llama cli -hf pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

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 pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF
# Run inference directly in the terminal:
./llama-cli -hf pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

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 pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

Use Docker

docker model run hf.co/pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

LM Studio
Jan
Ollama
How to use pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF with Ollama:
```
ollama run hf.co/pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF
```
Unsloth Desktop

How to use pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF with Docker Model Runner:
```
docker model run hf.co/pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF
```

Lemonade

How to use pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

Run and chat with the model

lemonade run user.Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

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 pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.6-27B-AEON-Ultimate-Uncensored — Q6_K GGUF + MTP

GGUF Q6_K quantization of AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16 with MTP (Multi-Token Prediction) weights included.

Key Features

Q6_K quantization — near-lossless quality, practical for consumer hardware
MTP weights included (866 tensors) — enables speculative decoding via --spec-type draft-mtp in llama.cpp for significantly faster inference
Vision supported — mmproj file (931MB) available in separate repo for multimodal image input

Model Details

Property	Value
Base Model	AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16
Architecture	Qwen 3.6 (Hybrid Linear Attention + Full Attention, 64 layers)
Parameters	27B
Quantization	Q6_K
Main Model Size	21.4 GB
mmproj Size	931 MB
Main Model Tensors	866 (including MTP extension)
mmproj Tensors	334 (27 ViT blocks + merger + patch embed + position embed)
Context Length	262,144 tokens (training)
License	Apache 2.0

Quantization Process

BF16 original → FP16 GGUF conversion (V100 does not support BF16)
FP16 GGUF → Q6_K quantization with MTP weights preserved

How to Use

llama.cpp (recommended)

# Text-only
llama-server \
  -m qwen3.6-27b-Q6K-mtp.gguf \
  -ngl 99 \
  -c 80000 \
  --host 0.0.0.0 \
  --port 8080 \
  --spec-type draft-mtp

# With vision (download mmproj from https://huggingface.co/pyys/Qwen3.6-27B-mmproj-GGUF)
llama-server \
  -m qwen3.6-27b-Q6K-mtp.gguf \
  --mmproj qwen3.6-27b-mmproj.gguf \
  -ngl 99 \
  -c 80000 \
  --host 0.0.0.0 \
  --port 8080 \
  --spec-type draft-mtp

MTP (Multi-Token Prediction)

MTP enables speculative decoding without a separate draft model. The MTP weights are baked into this GGUF file. Use --spec-type draft-mtp to activate.

Performance

Tested on V100 SXM2 32GB:

Configuration	TPS	Notes
Q6_K + MTP	~31.5 t/s	MTP acceptance rate ~41%
Q6_K + MTP (via OpenAI-compatible API)	~39.5 t/s	Acceptance rate ~61%

Files

File	Size	Description
`qwen3.6-27b-Q6K-mtp.gguf`	21.4 GB	Main model (Q6_K + MTP weights)
`qwen3.6-27b-mmproj.gguf`	931 MB	Vision projector (separate repo)

Notes

V100 GPUs do not support BF16 — this model was converted via FP16 intermediate
For single-GPU deployment, use --split-mode none -mg 0 to keep all weights on one GPU
KV cache of 80K tokens fits within a single V100 32GB alongside the model

Credits

Original model: AEON-7 — Qwen3.6-27B-AEON-Ultimate-Uncensored
Base architecture: Qwen — Qwen3.6-27B
Quantization tooling: llama.cpp

Downloads last month: 448

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for pyys/Qwen3.6-27B-AEON-Ultimate-Uncensored-Q6K-MTP-GGUF

Base model

Qwen/Qwen3.6-27B

Finetuned

AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored-BF16

Quantized

(41)

this model