Mistral-Nemo-Instruct-Platinum-GGUF

Mistral-Nemo-12B-Instruct-v1-GGUF (Platinum Series)

This repository contains the Platinum Series universal GGUF release of Mistral-Nemo-12B-Instruct-v1. This collection provides multiple quantization levels optimized for high-performance reasoning and creative applications, bridging the gap between small and large-scale language models.

📦 Available Files & Quantization Details

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🐍 Python Inference (llama-cpp-python)

To run these engines using Python:

from llama_cpp import Llama

llm = Llama(
    model_path="Mistral-Nemo-12B-Instruct-Platinum-Q8_0.gguf",
    n_gpu_layers=-1, # Target all layers to NVIDIA/Apple GPU
    n_ctx=8192
)

output = llm("Discuss the impact of the Mistral-Nemo architecture.", 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("Mistral-Nemo-12B-Instruct-Platinum-Q8_0.gguf") {
    ContextSize = 8192,
    GpuLayerCount = 40 
};

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

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

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🏗️ Technical Details

• Optimization Tool: llama.cpp (CUDA-accelerated)
• Architecture: Mistral Nemo (12B)
• Hardware Validation: Dual-GPU (RTX 3090 + RTX A4000)

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☕ Support the Forge

Maintaining high-capacity workstations for model conversion requires hardware investment. If these tools power your industrial workflows or local research, please consider supporting the development:

Scan to support via UPI (India Only):

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

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Connect with the architect: Abhishek Jaiswal on LinkedIn