Instructions to use rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit")
config = load_config("rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit"

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 rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit"

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 "rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit" \
  --custom-provider-id mlx-lm \
  --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-Uncensored-Cyber-MLX-8bit

Unofficial 8-bit MLX conversion of philbert440/Qwen3.8-27B-Uncensored-Cyber for Apple Silicon.

The source is a cyber-specialized, substantially de-refused derivative of Qwen/Qwen3.8-27B. This repository is only a format conversion and quantization; it does not claim additional training or safety evaluation.

Conversion

Property	Value
Source revision	`c3e40d890c50b5ad5e7cb035701316605a7f6d16`
Converter	`mlx-lm` 0.31.3
Quantization	MLX affine, 8 bits, group size 64
Reported average	8.501 bits per weight
Unquantized dtype	BF16
Architecture	`Qwen3_5ForConditionalGeneration`

Conversion command:

mlx_lm.convert \
  --hf-path /path/to/pinned-source-snapshot \
  --mlx-path ./Qwen3.8-27B-Uncensored-Cyber-MLX-8bit \
  --quantize \
  --q-bits 8 \
  --q-group-size 64

Usage

pip install "mlx-lm==0.31.3"

mlx_lm.generate \
  --model rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit \
  --prompt "Explain the purpose of network segmentation." \
  --max-tokens 256

OpenAI-compatible local server:

mlx_lm.server \
  --model rwcii/Qwen3.8-27B-Uncensored-Cyber-MLX-8bit \
  --host 127.0.0.1 \
  --port 8080

Local validation

Validated on Apple Silicon with MLX-LM 0.31.3:

Model load and text generation succeeded.
OpenAI-compatible chat completion succeeded.
Structured function/tool calling produced valid OpenAI-format JSON.
Observed generation throughput was approximately 16.7 tokens/second for one short test, with approximately 28.9 GB peak memory. These figures are machine- and workload-specific and are not a general benchmark.

See CHECKSUMS.sha256 for artifact hashes.

Safety and limitations

The source model has had safety alignment deliberately reduced and is tuned to answer cyber and offensive-security questions that other models may refuse. It can generate harmful, illegal, incorrect, or dangerous material. It has no meaningful built-in safety boundary.

Use only in environments where authorization, access control, monitoring, and applicable-law compliance are independently enforced. Do not treat the model as an authority, execute generated commands without review, or expose it directly to untrusted users. The uploader provides no warranty and has not independently reproduced the source author's behavioral evaluations.

Quantization can reduce accuracy, reasoning quality, and tool-call reliability. Validation above was limited and is not a comprehensive safety, quality, multimodal, or cybersecurity benchmark.

Attribution and license

Base model: Qwen/Qwen3.8-27B
Cyber derivative: philbert440/Qwen3.8-27B-Uncensored-Cyber
MLX conversion: rwcii

Distributed under the Apache License 2.0 inherited from the source. This is an unofficial community conversion and is not affiliated with or endorsed by Qwen, Alibaba Cloud, Philbert440, Hugging Face, Apple, or the MLX team.