Mistral-Nemo-12B-Instruct-OpenVINO-INT4

This repository contains a high-performance OpenVINO™ IR version of the Mistral-Nemo-12B-Instruct-v1, quantized to INT4 precision using NNCF. This 12B "Heavy Hitter" is specifically optimized for advanced reasoning and large-context window tasks on local Windows AI workstations.

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🐍 Python Inference (Optimum-Intel)

To run this 12B engine locally using the optimum-intel library:

from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer

model_id = "CelesteImperia/Mistral-Nemo-12B-Instruct-OpenVINO-INT4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)

prompt = "Explain the architectural advantages of the Mistral-Nemo 12B model."
messages = [
    {"role": "user", "content": prompt},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(input_ids, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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💻 For C# / .NET Users (LLamaSharp Implementation)

This model supports native integration for Windows applications via the LLamaSharp OpenVINO backend.

using LLama.Common;
using LLama;

// 1. Initialize the OpenVINO Model for Mistral-Nemo 12B
var parameters = new ModelParams("path/to/openvino_model.xml")
{
    ContextSize = 8192, // Optimized for high-reasoning depth
    GpuLayerCount = 0 
};

// 2. Load Weights and Create Context
using var weights = LLamaWeights.LoadFromFile(parameters);
using var context = weights.CreateContext(parameters);
var executor = new StatelessExecutor(weights, parameters);

// 3. Native Windows AI Inference
var chatHistory = new ChatHistory();
chatHistory.AddMessage(AuthorRole.User, "Analyze the benefits of local 12B models in .NET apps.");

foreach (var text in executor.InferAsync(chatHistory, new InferenceParams { MaxTokens = 512 }))
{
    Console.Write(text);
}

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🏗️ Technical Details

• Optimization Tool: NNCF (Neural Network Compression Framework)
• Quantization: INT4 Asymmetric (Group Size: 128)
• Workstation Validation: Dual-GPU (RTX 3090 + RTX A4000)
• Infrastructure: S: NVMe Scratch / K: 12TB Warehouse

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☕ Support the Forge

Maintaining a dual-GPU AI workstation and hosting high-bandwidth models requires significant resources. If our open-source tools power your projects, consider supporting our development:

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📜 License

This model is released under the Apache 2.0 License.

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Connect with the architect: Abhishek Jaiswal on LinkedIn