Instructions to use vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-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 vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-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 vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

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 vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

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 vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

Use Docker

docker model run hf.co/vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

Ollama
How to use vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF with Ollama:
```
ollama run hf.co/vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M
```

Unsloth Studio

How to use vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-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 vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-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 vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF to start chatting

How to use vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

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": "vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF with Docker Model Runner:
```
docker model run hf.co/vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M
```

Lemonade

How to use vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-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 vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

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 vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M

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 "vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF:Q4_K_M" \
  --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"

Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF / README.md

vcruz305

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metadata

language:
  - en
  - zh
license: apache-2.0
library_name: gguf
pipeline_tag: text-generation
base_model: AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-BF16
base_model_relation: quantized
quantized_by: vcruz305
tags:
  - gguf
  - qwen
  - qwen3.8
  - llama.cpp
  - uncensored
  - abliterated
  - aeon
  - mtp

Qwen3.8-27B AEON Ultimate Uncensored GGUF

llama.cpp K-quants of AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-BF16, an abliterated BF16 of Qwen/Qwen3.8-27B.

This is not official Qwen. It is also not vcruz305/Qwen3.8-27B-GGUF (official trunk) or vcruz305/Qwen3.8-27B-Uncensored-GGUF (orcarouter FP8).

MTP is baked into every Q2–Q8 file (866 tensors, qwen35.nextn_predict_layers=1, +~0.24 GiB vs the old trunk-only files). You do not need a second GGUF for draft-mtp.

What is in these files

27B dense hybrid-attention (qwen35). 64 language-trunk blocks plus 1 nextn/MTP block. Converted from the AEON BF16 master (no --no-mtp). Pair a separate mmproj if you need vision.

The source removed the refusal direction. These GGUFs inherit that.

Chat template

Official 3.8 jinja wraps empty <think> blocks and breaks multi-turn agents. These GGUFs bake a fixed template. Use --jinja.

llama-server -m Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-Q4_K_M.gguf --jinja --reasoning-format deepseek

Files

File	Quant	Bytes	GiB	Notes
`Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-Q2_K.gguf`	Q2_K	10864592928	10.12	live; MTP baked in
`Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-Q3_K_M.gguf`	Q3_K_M	13500737568	12.57	live; MTP baked in
`Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-Q4_K_M.gguf`	Q4_K_M	16810715168	15.66	live; MTP baked in
`Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-Q5_K_M.gguf`	Q5_K_M	19535702048	18.20	live; MTP baked in
`Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-Q6_K.gguf`	Q6_K	22431000608	20.89	live; MTP baked in; largest full-GPU on 24GB Turing
`Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-Q8_0.gguf`	Q8_0	29047085088	27.05	live; MTP baked in; will not `-ngl 99` on 24GB

How to run MTP

Needs a llama.cpp build that understands Qwen3.5 nextn / draft-mtp. --parallel 1 is required.

llama-server \
  -m Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-Q4_K_M.gguf \
  --spec-type draft-mtp --spec-draft-n-max 2 --spec-draft-p-min 0.7 \
  --parallel 1 --jinja --reasoning-format deepseek \
  -a qwen38-27b-aeon --host 127.0.0.1 --port 8085 \
  -ngl 99 -fa on -b 512 -ub 512 -c 32768

Optional extra mtp-* sidecars are still in the repo if you want a separate -md draft. They are not required for the files above.

Download

Use hf_xet. Do not git clone. Grab only the file that exists.

export HF_XET_HIGH_PERFORMANCE=1
hf download vcruz305/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF \
  --local-dir Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-GGUF \
  --include "Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-Q4_K_M.gguf"

Q6_K is the largest file that still full-offloads a 24GB Turing card. Q8_0 does not.

Intended use

Local llama.cpp of the AEON uncensored 27B trunk + native MTP. Out of scope: treating this as official Qwen, or as a drop-in for the official-trunk GGUF pack.

Source

BF16 master: https://huggingface.co/AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-BF16
Official base: https://huggingface.co/Qwen/Qwen3.8-27B
Convert: convert_hf_to_gguf.py --outtype f16 (MTP mixin on, no --no-mtp) → llama-quantize
License: Apache-2.0

Credits

Abliteration / BF16: AEON-7. Base: Qwen / Alibaba. GGUF pack: Victor Cruz (vcruz305).