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Model: CelesteImperia/Gemma-2-2B-IT-GGUF
Source: Original Platform

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README.md

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+---
+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/)

gemma2_2b_q4_k_m.gguf

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requirements.txt

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+# GGUF Conversion & Inference
+llama-cpp-python
+sentencepiece
+gguf
+transformers>=4.45.0
+protobuf
+# Optional: For faster conversion on some architectures
+numpy<2.0.0