Instructions to use MassivDash/Gemma-4-RUST-CODER-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MassivDash/Gemma-4-RUST-CODER-12B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MassivDash/Gemma-4-RUST-CODER-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MassivDash/Gemma-4-RUST-CODER-12B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MassivDash/Gemma-4-RUST-CODER-12B 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 MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MassivDash/Gemma-4-RUST-CODER-12B: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-12B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MassivDash/Gemma-4-RUST-CODER-12B: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-12B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M

Use Docker

docker model run hf.co/MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M

LM Studio
Jan

vLLM

How to use MassivDash/Gemma-4-RUST-CODER-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MassivDash/Gemma-4-RUST-CODER-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MassivDash/Gemma-4-RUST-CODER-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M

SGLang

How to use MassivDash/Gemma-4-RUST-CODER-12B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MassivDash/Gemma-4-RUST-CODER-12B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MassivDash/Gemma-4-RUST-CODER-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MassivDash/Gemma-4-RUST-CODER-12B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MassivDash/Gemma-4-RUST-CODER-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use MassivDash/Gemma-4-RUST-CODER-12B with Ollama:
```
ollama run hf.co/MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M
```

Unsloth Studio

How to use MassivDash/Gemma-4-RUST-CODER-12B 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-12B 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-12B 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-12B to start chatting

How to use MassivDash/Gemma-4-RUST-CODER-12B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MassivDash/Gemma-4-RUST-CODER-12B: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-12B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use MassivDash/Gemma-4-RUST-CODER-12B with Docker Model Runner:
```
docker model run hf.co/MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M
```

Lemonade

How to use MassivDash/Gemma-4-RUST-CODER-12B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-RUST-CODER-12B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use MassivDash/Gemma-4-RUST-CODER-12B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MassivDash/Gemma-4-RUST-CODER-12B: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-12B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MassivDash/Gemma-4-RUST-CODER-12B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M

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 "MassivDash/Gemma-4-RUST-CODER-12B:Q4_K_M" \
  --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"

Gemma-4-RUST-CODER-12B / README.md

MassivDash

Create README.md

b0ea4f1 verified about 1 month ago

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metadata

base_model:
  - google/gemma-4-12B-it
pipeline_tag: text-generation
library_name: transformers
datasets:
  - Fortytwo-Network/Strandset-Rust-v1
license: mit
tags:
  - fine-tuned
  - rust
  - coding
  - unsloth
  - spaceout.pl

Gemma-4-12B-Rust-Coder

This model is a specialized fine-tune of Google's Gemma-4-12B-it, rigorously optimized for Rust systems programming, memory safety patterns, and high-performance application development.

While the base model provides excellent general reasoning, this fine-tune specifically enhances idiomatic Rust code generation, handling of advanced concurrency constraints, and standard library familiarity.

🦀 Fine-Tuning Focus & Model Details

Base Model: google/gemma-4-12B-it
Target Domain: Rust software development and debugging.
Key Improvements:
- Idiomatic Rust: Generates clean, "Rusty" code utilizing modern patterns (e.g., proper Result and Option handling, idiomatic error propagation).
- Concurrency & Safety: Enhanced understanding of strict borrow checker rules, lifetimes, Send/Sync traits, and async runtimes like Tokio.
- Instruction Following: Tuned to deliver concise, code-first responses with minimal conversational overhead compared to the base model.

🤝 Training Data & Acknowledgments

Special thanks to Fortytwo-Network for providing the Strandset-Rust-v1 dataset. This model's specialized knowledge of the Rust ecosystem is a direct result of fine-tuning on this high-quality, domain-specific instruction set.

⚙️ Training Procedure

This model was trained using Unsloth Studio for optimal memory efficiency and throughput.

Method: QLoRA
Steps: 30
Tokens Processed: 148,306
Learning Rate: 8.00e-6
Final Loss: 1.1045
Training Time: 3 minutes 57 seconds
Hardware: [Enter your GPU here, e.g., 1x RTX 4090 / A100]

🚀 Usage

You can easily load and run this model using the Hugging Face transformers library.

Installation:

pip install transformers accelerate torch

Inference Snippet:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "MassivDash/Gemma-4-12B-Rust-Coder"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

messages = [
    {"role": "user", "content": "Write an asynchronous Rust function using Tokio to fetch a URL and return its body as a String. Handle errors idiomatically."}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

⚠️ Limitations & Out-of-Scope Use

Language Degradation: Because this model was heavily fine-tuned on Rust, its performance in other languages (like Python or JavaScript) may have degraded relative to the base model (catastrophic forgetting).
Non-Coding Tasks: It is designed specifically for technical and programming queries. It is not recommended for creative writing, general knowledge trivia, or non-technical instruction following.
Compilation Guarantees: While fine-tuned for syntax and borrow-checker compliance, the model may still occasionally generate code that fails to compile or contains logical bugs. Always review and test generated code.

🔗 Stay Connected

For more insights on AI development, custom integrations, and fine-tuning, visit my blog: 👉 spaceout.pl

This model was trained 2x faster with Unsloth