You can deploy this model and start earning money today!

CelesteImperia/
Llama-3.2-1B-Instruct-OpenVINO-INT8

Name: Llama-3.2-1B-Instruct-OpenVINO-INT8
Availability: InStock

meta-llama/Llama-3.2-1B-Instruct

finetune

llama3.2

Views

No views yet

Llama-3.2-1B-Instruct-OpenVINO-INT8 (Silver Series)

This repository contains the Silver Series optimized OpenVINO™ IR version of Llama-3.2-1B-Instruct, quantized to INT8 precision using NNCF. This high-fidelity model is designed for low-latency agentic workflows and edge reasoning on local Windows workstations.

🐍 Python Inference (Optimum-Intel)

To run this Silver Series engine locally using the optimum-intel library:

python

from optimum.intel import OVModelForCausalLM
from transformers import AutoTokenizer

model_id = "CelesteImperia/Llama-3.2-1B-Instruct-OpenVINO-INT8"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)

prompt = "Write a quick C# function to calculate Fibonacci numbers."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

💻 For C# / .NET Users (OpenVINO.GenAI)

The Silver Series is optimized for the native OpenVINO.GenAI NuGet package, providing a direct path for high-performance integration into Windows application stacks.

csharp

using OpenVino.GenAI;

// 1. Initialize the LLM Pipeline
var device = "CPU"; // Use "GPU" to target your RTX 3090/A4000
using var pipe = new LLMPipeline("path/to/llama-3.2-1b-int8-model", device);

// 2. Set Generation Config
var config = new GenerationConfig { MaxNewTokens = 256, Temperature = 0.6f };

// 3. Execute Inference
var prompt = "Explain the concept of Dependency Injection in C#.";
var result = pipe.Generate(prompt, config);

Console.WriteLine(result);

🏗️ Technical Details

Optimization Tool: NNCF (Neural Network Compression Framework)
Quantization: INT8 Symmetric (Per-channel)
Series: Silver (High Fidelity)
Workstation Validation: Dual-GPU (RTX 3090 + RTX A4000)

☕ Support the Forge

Maintaining a high-capacity local AI warehouse and producing high-fidelity weights requires significant hardware resources. If these tools power your development, please consider supporting the Forge:

Platform	Support Link
Global & India	Support via Razorpay

Scan to support via UPI (India Only):

📜 License

This model is released under the Llama 3.2 Community License.

Connect with the architect: Abhishek Jaiswal on LinkedIn