Instructions to use alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-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 alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-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 alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF
# Run inference directly in the terminal:
llama cli -hf alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-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 alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF
# Run inference directly in the terminal:
./llama-cli -hf alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-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 alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF

Use Docker

docker model run hf.co/alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF

LM Studio
Jan
Ollama
How to use alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF with Ollama:
```
ollama run hf.co/alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF
```
Unsloth Desktop

How to use alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-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": "alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF
```

Lemonade

How to use alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF

Run and chat with the model

lemonade run user.Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-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 alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-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 alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-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 "alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-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"

Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF

APEX GGUF quantizations of OS-Software/Hy-MT2-30B-A3B-uncensored-heretic, quantized by alphaZimuth.

This release is based directly on the existing uncensored / decensored model from OS-Software, rather than performing an additional uncensoring or weight-ablation process on the original Tencent model.

The source model was created from Tencent Hy-MT2-30B-A3B using Heretic v1.4.0+custom, with Arbitrary-Rank Ablation (ARA), a LoRA adapter, and row-norm preservation.

No additional uncensoring, abliteration, or behavioral weight editing is performed prior to GGUF conversion and APEX quantization.

Source Model

Source:

OS-Software/Hy-MT2-30B-A3B-uncensored-heretic

Original base model:

tencent/Hy-MT2-30B-A3B

According to the source model authors, the decensored model was produced using:

Heretic v1.4.0+custom
Arbitrary-Rank Ablation (ARA)
LoRA adapter
Row-norm preservation
Modified layers 18–28

The source repository should be considered the authoritative reference for the uncensoring methodology and evaluation.

Upstream Evaluation

The following results were reported by OS-Software for the source Hy-MT2-30B-A3B-uncensored-heretic model:

Metric	Uncensored Heretic	Original Hy-MT2
Keywords / Refusal Test	0 / 100	100 / 100
KL Divergence	0.0276	0

The upstream evaluation used a custom mixed-language dataset.

Note

These results belong to the original full-precision source model from OS-Software.

They are included here for reference and should not be interpreted as an independent benchmark of the APEX GGUF quantizations.

Quantization can introduce small behavioral or quality differences compared with the source model.

Available Quantizations

Four APEX-I quantization tiers are currently available:

File	Size	Description
`Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-I-Nano.gguf`	8.99 GiB	Near-limit quantization; not generally recommended
`Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-I-Mini.gguf`	10.4 GiB	A slightly higher-quality lightweight option
`Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-I-Compact.gguf`	13.0 GiB	Recommended balance between model size and quality
`Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-I-Quality.gguf`	17.7 GiB	Higher-precision variant prioritizing model quality

Quantization Details

This repository uses APEX quantization, based on llama.cpp.

The APEX-I variants use importance-matrix-assisted quantization.

Conversion pipeline:

OS-Software/Hy-MT2-30B-A3B-uncensored-heretic
                         ↓
              convert_hf_to_gguf.py
                         ↓
     Hy-MT2-30B-A3B-uncensored-heretic-BF16.gguf
                         ↓
                Imatrix Calibration
                         ↓
                 APEX Quantization
                         ↓
       APEX-I-Nano / Compact / Quality

Imatrix Calibration

The APEX quantizations in this repository were generated using an importance matrix (imatrix) calibration pass.

The calibration run reported:

Final estimate: PPL = 5.2905 +/- 0.02242

This value is the final perplexity estimate reported during the imatrix calibration process and is provided for reproducibility and reference.

Important distinction:

Uncensoring / Heretic processing → OS-Software
GGUF conversion / Imatrix calibration / APEX quantization → alphaZimuth

No additional Heretic processing or abliteration is performed in this release.

llama.cpp Compatibility

Important

Hy-MT2-30B-A3B uses the hy_v3 architecture.

Support for hy_v3 has been merged into upstream llama.cpp.

Please use a recent version of llama.cpp for inference.

Older builds may fail to load the model or may not correctly support the architecture.

Recommended Inference Parameters

The upstream Hy-MT2 documentation recommends the following parameters for the 30B-A3B model:

{
  "temperature": 0.7,
  "top_p": 1.0,
  "top_k": -1,
  "repetition_penalty": 1.0,
  "max_tokens": 4096
}

About the Uncensored Version

This model has substantially reduced safety alignment compared with the original Hy-MT2 model.

As a result, it may be less likely to refuse certain requests, but it may also produce:

inaccurate information
harmful content
biased content
offensive content
unexpected outputs

The uncensored behavior originates from the OS-Software source model and is not introduced by APEX quantization.

Users should treat model outputs as untrusted and independently verify important information.

This model is primarily intended for research, experimentation, translation research, alignment research, and local model experimentation.

Users are responsible for complying with applicable laws, regulations, licenses, and platform policies.

Previous Experimental Version

The previous release:

Hy-MT2-30B-A3B-Uncensored-v1-APEX-GGUF

used a custom directional / orthogonal ablation pipeline created specifically for that release.

Because issues were reported with the experimental version, it is now considered a legacy / experimental release.

For new deployments, use:

alphaZimuth/Hy-MT2-30B-A3B-Uncensored-Heretic-APEX-GGUF

instead.

Credits

Special thanks to:

Tencent Hy-MT2 team for the original Hy-MT2-30B-A3B model.
OS-Software for creating and evaluating the Hy-MT2-30B-A3B-uncensored-heretic model.
p-e-w for the Heretic project.
LocalAI for the APEX quantization framework.
llama.cpp contributors for GGUF and hy_v3 architecture support.
The wider open-source LLM community.