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Model: CelesteImperia/Llama-3.2-3B-Instruct-Platinum-GGUF
Source: Original Platform

.gitattributes

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LICENSE

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+Llama 3.2 Community License Agreement
+
+This model is subject to the Llama 3.2 Community License. 
+The full license agreement, acceptable use policy, and 
+redistribution terms are available at:
+https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE

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README.md

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+---
+base_model: meta-llama/Llama-3.2-3B-Instruct
+library_name: gguf
+pipeline_tag: text-generation
+license: llama3.2
+tags:
+- gguf
+- llama-cpp
+- llama-3.2
+- celeste-imperia
+---
+
+# Llama-3.2-3B-Instruct-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 **Platinum Series** universal GGUF release of **Llama-3.2-3B-Instruct**. This collection provides multiple quantization levels optimized for cross-platform performance, offering a significant reasoning upgrade over the 1B variant while maintaining exceptional speed on consumer hardware.
+
+## 📦 Available Files & Quantization Details
+
+| File Name | Quantization | Size | Accuracy | Recommended For |
+| :--- | :--- | :--- | :--- | :--- |
+| **Llama-3.2-3B-Instruct-Platinum-F16.gguf** | FP16 | ~6.5 GB | 100% | Master Reference / Benchmarking |
+| **Llama-3.2-3B-Instruct-Platinum-Q8_0.gguf** | Q8_0 | ~3.4 GB | 99.9% | Platinum Reference / High-Fidelity |
+| **Llama-3.2-3B-Instruct-Platinum-Q6_K.gguf** | Q6_K | ~2.7 GB | 99.7% | High-Quality Reasoning |
+| **Llama-3.2-3B-Instruct-Platinum-Q5_K_M.gguf** | Q5_K_M | ~2.4 GB | 99.3% | Balanced Desktop Performance |
+| **Llama-3.2-3B-Instruct-Platinum-Q4_K_M.gguf** | Q4_K_M | ~2.0 GB | 98.6% | Edge Devices / Efficiency |
+
+---
+
+## 🐍 Python Inference (llama-cpp-python)
+
+To run these engines using Python:
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(
+    model_path="Llama-3.2-3B-Instruct-Platinum-Q8_0.gguf",
+    n_gpu_layers=-1, # Target all layers to NVIDIA/Apple GPU
+    n_ctx=4096
+)
+
+output = llm("Discuss the architectural benefits of Llama 3.2 3B.", 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("Llama-3.2-3B-Instruct-Platinum-Q8_0.gguf") {
+    ContextSize = 4096,
+    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:** Llama 3.2 (3B)
+- **Hardware Validation:** Dual-GPU (RTX 3090 + RTX A4000)
+
+---
+
+### ☕ Support the Forge
+
+Maintaining the production line for high-fidelity models requires significant hardware resources. If these tools power your research or industrial projects, 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/)

requirements.txt

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+optimum-intel[openvino,nncf]>=1.20.0
+transformers>=4.45.0
+accelerate
+sentencepiece