Instructions to use MassivDash/Gemma-4-Rust-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MassivDash/Gemma-4-Rust-Coder with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MassivDash/Gemma-4-Rust-Coder",
	filename="gemma-4-e2b-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 MassivDash/Gemma-4-Rust-Coder with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MassivDash/Gemma-4-Rust-Coder:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MassivDash/Gemma-4-Rust-Coder:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MassivDash/Gemma-4-Rust-Coder:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MassivDash/Gemma-4-Rust-Coder: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 MassivDash/Gemma-4-Rust-Coder:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MassivDash/Gemma-4-Rust-Coder: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 MassivDash/Gemma-4-Rust-Coder:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MassivDash/Gemma-4-Rust-Coder:Q4_K_M

Use Docker

docker model run hf.co/MassivDash/Gemma-4-Rust-Coder:Q4_K_M

LM Studio
Jan
Ollama
How to use MassivDash/Gemma-4-Rust-Coder with Ollama:
```
ollama run hf.co/MassivDash/Gemma-4-Rust-Coder:Q4_K_M
```

Unsloth Studio new

How to use MassivDash/Gemma-4-Rust-Coder 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 MassivDash/Gemma-4-Rust-Coder 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 MassivDash/Gemma-4-Rust-Coder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MassivDash/Gemma-4-Rust-Coder to start chatting

Pi new

How to use MassivDash/Gemma-4-Rust-Coder with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MassivDash/Gemma-4-Rust-Coder: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": "MassivDash/Gemma-4-Rust-Coder:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MassivDash/Gemma-4-Rust-Coder with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MassivDash/Gemma-4-Rust-Coder: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 MassivDash/Gemma-4-Rust-Coder:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use MassivDash/Gemma-4-Rust-Coder with Docker Model Runner:
```
docker model run hf.co/MassivDash/Gemma-4-Rust-Coder:Q4_K_M
```

Lemonade

How to use MassivDash/Gemma-4-Rust-Coder with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MassivDash/Gemma-4-Rust-Coder:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-Rust-Coder-Q4_K_M

List all available models

lemonade list

Gemma-4-Rust-Coder

File size: 2,284 Bytes

---
tags:
- gguf
- llama.cpp
- unsloth
- vision-language-model
- rust
- coding
license: mit
datasets:
- Fortytwo-Network/Strandset-Rust-v1
base_model:
- google/gemma-4-E4B-it
---

# Gemma-4-Rust-Coder : GGUF

This model is a specialized fine-tune of **Gemma 4**, specifically optimized for **Rust systems programming**, memory safety patterns, and high-performance development. It was trained using **Unsloth Studio** to ensure maximum efficiency and performance.

## 🦀 Fine-Tuning Focus
The model has been adjusted to excel in:
* **Idiomatic Rust:** Writing clean, "Rusty" code using modern patterns.
* **Concurrency:** Deep understanding of `Send`, `Sync`, and async runtimes like `Tokio`.
* **Vision-to-Code:** Using its multimodal capabilities to translate architecture diagrams or UI mockups into functional Rust code.

## 🤝 Credits & Acknowledgments
Special thanks to **Fortytwo-Network** for providing the **[Strandset-Rust-v1](https://huggingface.co/datasets/Fortytwo-Network/Strandset-Rust-v1)** dataset. This model's specialized knowledge of the Rust ecosystem is a direct result of this high-quality data.

## 🚀 Usage
This model is converted to GGUF format for seamless use with `llama.cpp` and other compatible executors.

**Example usage**:
* **Text-only LLM:** `llama-cli -hf MassivDash/Gemma-4-Rust-Coder --jinja`
* **Multimodal / Vision:** `llama-mtmd-cli -hf MassivDash/Gemma-4-Rust-Coder --jinja`

## 📂 Available Model files:
* `gemma-4-e2b-it.Q3_K_M.gguf`
* `gemma-4-e2b-it.BF16-mmproj.gguf`

## ⚠️ Ollama Note for Vision Models
**Important:** Ollama currently does not support separate `mmproj` files for vision models.

To create an Ollama model from this vision model:
1. Place the `Modelfile` in the same directory as the finetuned bf16 merged model.
2. Run: `ollama create model_name -f ./Modelfile`
   *(Replace `model_name` with your desired name)*

## 🔗 Stay Connected
For more insights on AI development and fine-tuning, visit my blog: 
👉 **[spaceout.pl](https://spaceout.pl)**

---

*This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)*

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)