Instructions to use Shrijanagain/H-GEMMA4-SFT-INSTRUCT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Shrijanagain/H-GEMMA4-SFT-INSTRUCT 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 Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M

Use Docker

docker model run hf.co/Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use Shrijanagain/H-GEMMA4-SFT-INSTRUCT with Ollama:
```
ollama run hf.co/Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M
```
Unsloth Desktop

How to use Shrijanagain/H-GEMMA4-SFT-INSTRUCT with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M

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": "Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Shrijanagain/H-GEMMA4-SFT-INSTRUCT with Docker Model Runner:
```
docker model run hf.co/Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M
```

Lemonade

How to use Shrijanagain/H-GEMMA4-SFT-INSTRUCT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M

Run and chat with the model

lemonade run user.H-GEMMA4-SFT-INSTRUCT-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Shrijanagain/H-GEMMA4-SFT-INSTRUCT with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shrijanagain/H-GEMMA4-SFT-INSTRUCT: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 Shrijanagain/H-GEMMA4-SFT-INSTRUCT:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Shrijanagain/H-GEMMA4-SFT-INSTRUCT with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Shrijanagain/H-GEMMA4-SFT-INSTRUCT: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 "Shrijanagain/H-GEMMA4-SFT-INSTRUCT: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"

----> We don't Own this model this Is prepared For Hackthaon

📂 Model Organization (Curated by SKT AI Labs)

To make it easier for Indian developers to use Google's power, we have categorized the models as follows:

1. 💻 SFT-CODER-GGUF

Assembled for software engineering tasks.

Base Models: Google Gemma-4 Coding Variants.
Format: GGUF (Optimized for local/edge inference).

2. 🧠 SFT-REASONING

Assembled for logic, math, and deep reasoning.

Base Models: Google Gemma-4 Instruct/Reasoning Variants.
Focus: High-parameter logic processing.

3. 🏗️ SFT-MODEL

Full weights/Safetensors for research and fine-tuning.

🚀 Purpose for the Hackathon

The goal of this repository is to showcase how Google's Gemma-4 can be utilized to build "Sovereign Intelligence" solutions. By organizing these models into specialized tiers, we demonstrate a scalable architecture for using Google's technology in diverse real-world scenarios.

⚖️ Licensing

These models are released under the Google Gemma Terms of Use. Please ensure you comply with Google's acceptable use policy when utilizing these weights.

Downloads last month: 538

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

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google/gemma-4-31B

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