Llama-3.2-3B-Instruct-Platinum-GGUF/README.md

base_model, library_name, pipeline_tag, license, tags

base_model	library_name	pipeline_tag	license	tags
meta-llama/Llama-3.2-3B-Instruct	gguf	text-generation	llama3.2	gguf llama-cpp llama-3.2 celeste-imperia

Llama-3.2-3B-Instruct-GGUF (Platinum Series)

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

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🐍 Python Inference (llama-cpp-python)

To run these engines using 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"])

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💻 For C# / .NET Users (LLamaSharp)

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

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.");

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🏗️ Technical Details

• Optimization Tool: llama.cpp (CUDA-accelerated)
• Architecture: Llama 3.2 (3B)
• Hardware Validation: Dual-GPU (RTX 3090 + RTX A4000)

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☕ 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

Scan to support via UPI (India Only):

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Connect with the architect: Abhishek Jaiswal on LinkedIn