Instructions to use jgebbeken/gemma-4-coder-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jgebbeken/gemma-4-coder-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jgebbeken/gemma-4-coder-gguf",
	filename="gemma-4-E4b-it.BF16-mmproj.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use jgebbeken/gemma-4-coder-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jgebbeken/gemma-4-coder-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf jgebbeken/gemma-4-coder-gguf:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jgebbeken/gemma-4-coder-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf jgebbeken/gemma-4-coder-gguf:BF16

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 jgebbeken/gemma-4-coder-gguf:BF16
# Run inference directly in the terminal:
./llama-cli -hf jgebbeken/gemma-4-coder-gguf:BF16

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 jgebbeken/gemma-4-coder-gguf:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jgebbeken/gemma-4-coder-gguf:BF16

Use Docker

docker model run hf.co/jgebbeken/gemma-4-coder-gguf:BF16

LM Studio
Jan
Ollama
How to use jgebbeken/gemma-4-coder-gguf with Ollama:
```
ollama run hf.co/jgebbeken/gemma-4-coder-gguf:BF16
```

Unsloth Studio new

How to use jgebbeken/gemma-4-coder-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 jgebbeken/gemma-4-coder-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 jgebbeken/gemma-4-coder-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jgebbeken/gemma-4-coder-gguf to start chatting

Pi new

How to use jgebbeken/gemma-4-coder-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jgebbeken/gemma-4-coder-gguf:BF16

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": "jgebbeken/gemma-4-coder-gguf:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jgebbeken/gemma-4-coder-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 jgebbeken/gemma-4-coder-gguf:BF16

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 jgebbeken/gemma-4-coder-gguf:BF16

Run Hermes

hermes

Docker Model Runner
How to use jgebbeken/gemma-4-coder-gguf with Docker Model Runner:
```
docker model run hf.co/jgebbeken/gemma-4-coder-gguf:BF16
```

Lemonade

How to use jgebbeken/gemma-4-coder-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jgebbeken/gemma-4-coder-gguf:BF16

Run and chat with the model

lemonade run user.gemma-4-coder-gguf-BF16

List all available models

lemonade list

gemma-4-coder-gguf : GGUF

Support

If this model helped you, consider buying me a coffee — it funds the next training run! ☕

🧠 Gemma-4 E4B Code Model

Model ID: jgebbeken/gemma-4-coder-e4b Base Model: Gemma 4 Type: Instruction-tuned code model Quantization: E4B (4-bit efficient format)

📌 Overview

Gemma-4 E4B is an instruction-tuned coding model optimized for:

💻 Code generation 🛠️ Debugging assistance 🧩 Code completion 📚 Developer Q&A

This is my very first released AI model! 🎉 It's an adapter that built on top of Google's Gemma 4, with my own custom modifications. I'm excited to share it with the community and welcome any feedback or contributions in making making this small model better.

The model follows a MagicCoder-style instruction tuning approach, enabling strong performance on structured programming tasks and natural language → code generation.

⚙️ Training Details Base Model: Gemma 4 Fine-tuning: Instruction tuning Quantization: E4B (efficient 4-bit) Primary Dataset: Magicoder-Evol-Instruct-110K

✨ Capabilities Instruction-following for programming tasks Multi-language support (Python, Swift, JavaScript, etc.) Efficient inference due to quantization Suitable for local inference and agent workflows ⚠️ Limitations May generate incorrect or non-compilable code Limited reasoning compared to larger models May reflect biases or artifacts from synthetic data Should not be used in production without validation 📜 License Model License: Apache License 2.0

This model is released under the Apache License 2.0.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this work except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

For text only LLMs: llama-cli -hf jgebbeken/gemma-4-coder-gguf --jinja
For multimodal models: llama-mtmd-cli -hf jgebbeken/gemma-4-coder-gguf --jinja

Available Model files:

gemma-4-E4b-it.Q4_K_M.gguf
gemma-4-E4b-it.BF16-mmproj.gguf This was trained 2x faster with Unsloth

Downloads last month: 14,725

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for jgebbeken/gemma-4-coder-gguf

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Quantized

(194)

this model