---
base_model: google/gemma-2-2b-it
library_name: gguf
pipeline_tag: text-generation
license: gemma
tags:
- gguf
- llama-cpp
- gemma-2
- celeste-imperia
---

# Gemma-2-2B-IT-GGUF (Platinum Series)

![Status](https://img.shields.io/badge/Status-Active-success)
![Format](https://img.shields.io/badge/Format-GGUF-green)
![Series](https://img.shields.io/badge/Series-Platinum-silver)
[![Support](https://img.shields.io/badge/Support-Razorpay-orange)](https://razorpay.me/@huggingface)

This repository contains the **Universal GGUF** release of **Gemma-2-2B-Instruct**. This collection provides multiple quantization levels to support everything from high-VRAM workstations to mobile and edge devices.

## 📦 Available Files & Quantization Details

| File Name | Quantization | Size | Accuracy | Recommended For |
| :--- | :--- | :--- | :--- | :--- |
| **Gemma-2-2B-IT-Platinum-F16.gguf** | FP16 | ~5.2 GB | 100% | Master Reference / Benchmarking |
| **Gemma-2-2B-IT-Platinum-Q8_0.gguf** | Q8_0 | ~2.7 GB | 99.9% | Platinum Reference / High-Fidelity |
| **Gemma-2-2B-IT-Platinum-Q6_K.gguf** | Q6_K | ~2.1 GB | 99.5% | High-end GPU / Complex Reasoning |
| **Gemma-2-2B-IT-Platinum-Q5_K_M.gguf** | Q5_K_M | ~1.8 GB | 99.0% | Balanced Desktop Performance |
| **Gemma-2-2B-IT-Platinum-Q4_K_M.gguf** | Q4_K_M | ~1.5 GB | 98.2% | Mobile / Low-VRAM / Efficiency |

---

## 🐍 Python Inference (llama-cpp-python)

To run these engines using Python:

```python
from llama_cpp import Llama

llm = Llama(
    model_path="Gemma-2-2B-IT-Platinum-Q8_0.gguf",
    n_gpu_layers=-1, # Target all layers to NVIDIA/Apple GPU
    n_ctx=2048
)

output = llm("Explain the architecture of Gemma 2.", max_tokens=200)
print(output["choices"][0]["text"])
```

---

## 💻 For C# / .NET Users (LLamaSharp)

This collection is fully compatible with .NET applications via the ``LLamaSharp`` library.

```csharp
using LLama.Common;
using LLama;

var parameters = new ModelParams("Gemma-2-2B-IT-Platinum-Q8_0.gguf") {
    ContextSize = 2048,
    GpuLayerCount = 35 
};

using var model = LLamaWeights.LoadFromFile(parameters);
using var context = model.CreateContext(parameters);
var executor = new InteractiveExecutor(context);

Console.WriteLine("Universal Engine Active.");
```

---

## 🏗️ Technical Details
- **Optimization Tool:** llama.cpp (CUDA-accelerated)
- **Architecture:** Gemma-2
- **Hardware Validation:** RTX 3090 + RTX A4000

---

### ☕ Support the Forge

Maintaining a high-capacity local AI warehouse with full-fidelity weights requires significant hardware resources. If these models power your industrial projects or research, please consider supporting the development:

| Platform | Support Link |
| :--- | :--- |
| **Global & India** | [Support via Razorpay](https://razorpay.me/@huggingface) |

**Scan to support via UPI (India Only):**

<img src="https://huggingface.co/datasets/CelesteImperia/Assets/resolve/main/QrCode.jpeg" width="200">

---

**Connect with the architect:** [Abhishek Jaiswal on LinkedIn](https://www.linkedin.com/in/abhishek-jaiswal-524056a/)