Instructions to use blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF",
	filename="Gemma-4-14B-A4B-Heretic-v2-F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf blascotobasco/Gemma-4-14B-A4B-Heretic-v2-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 blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf blascotobasco/Gemma-4-14B-A4B-Heretic-v2-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 blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF with Ollama:
```
ollama run hf.co/blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use blascotobasco/Gemma-4-14B-A4B-Heretic-v2-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 blascotobasco/Gemma-4-14B-A4B-Heretic-v2-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 blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF to start chatting

How to use blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf blascotobasco/Gemma-4-14B-A4B-Heretic-v2-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": "blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use blascotobasco/Gemma-4-14B-A4B-Heretic-v2-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 blascotobasco/Gemma-4-14B-A4B-Heretic-v2-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 blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M
```

Lemonade

How to use blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull blascotobasco/Gemma-4-14B-A4B-Heretic-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-14B-A4B-Heretic-v2-GGUF-Q4_K_M

List all available models

lemonade list

Following up on the previous trained version of my Gemma 4 prune, this version has been trained on MetaMathQA to restore arithmetic capabilities. I have confirmed that the model's roleplaying and instruction following capabilities are fully intact, as is vision.

While the model can do arithmetic properly now, it may still struggle with complex proofs or problems. However for most use cases it should be robust enough.

Since this is a pruned model, I do not recommend relying on it for important, complex work, especially mathematics. Even though it has been trained, a prune is still weaker than the original model, especially a fifty percent prune like this one.

This is based on a Heretic Abliterated version of Gemma 4 26B A4B IT.

After further testing, it seems math was not restored. It's possible the model just got lucky during training or the unquantized weights were more capable. However I noticed that the model still has improved logic following even if degraded.