---
license: mit
language:
- en
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
---
This is plain vanilla exported as onnx. I am researching the offline use of various models, and thought might come in handy for the commmunity
Example use
#0. download and unzip the package into a subfolder in you project folder

#1. Create a new python environment
python -m venv llama_env

#2. activate the environment
llama_env\Scripts\activate

#3. Install onnx runtime
pip install onnx onnxruntime-gpu

#4. Install transformers and py/torch
pip install transformers
pip install torch
pip install pytorch

#I had to run this when I had a conflic
python -m pip install --upgrade pip
python -m pip install "numpy<2"

#I use VSCode, so if you'd like:
#Install Jupyter and create notebook
pip install jupyter
code #run vscode



import onnxruntime as ort
import torch
import numpy as np

# Load the ONNX model
onnx_model_path = "payhTo/llama3.1.onnx"
session = ort.InferenceSession(onnx_model_path)

# Check the model's input and output names and shapes
for input_meta in session.get_inputs():
    print(f"Input Name: {input_meta.name}, Shape: {input_meta.shape}, Type: {input_meta.type}")

for output_meta in session.get_outputs():
    print(f"Output Name: {output_meta.name}, Shape: {output_meta.shape}, Type: {output_meta.type}")