ONNX Model Zoo

alpindale/Llama-2-7b-ONNX

By alpindale · ↓ 0 · ♥ 8 · Updated 2023-09-21

What's in this repo

This page lists the files in alpindale/Llama-2-7b-ONNX, including LlamaV2_7B_FT_float16.onnx, LlamaV2_7B_float16.onnx, LlamaV2_7B_FT_float32.onnx and more.

  • 4 ONNX models (.onnx files)
  • 1203 files, including 260 .weight, 8 .py, 6 .pyc, 4 .onnx, 4 .data, 2 .txt
  • Last updated 2023-09-21

ONNX files

File Size Download
FP16-Chat/LlamaV2_7B_FT_float16.onnx — Download
FP16/LlamaV2_7B_float16.onnx — Download
FP32-Chat/LlamaV2_7B_FT_float32.onnx — Download
FP32/LlamaV2_7B_float32.onnx — Download

How to use

These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.

README

Llama-2 ONNX

This repository contains optimized version of Llama-2 7B.

Downloading the model

You can use huggingface_hub to download this repository. This can be done through both python scripting and the commandline. Refer to the HuggingFace Hub Documentation for the Python examples.

With CLI:

  1. Make sure you have an updated huggingface_hub installed.
pip install -U huggingface_hub
  1. Download the repository.
huggingface-cli download alpindale/Llama-2-7b-ONNX --repo-type model --cache-dir /path/to/custom/cache/directory --local-dir /path/to/download/dir --local-dir-use-symlinks False

The --cache-dir kwarg is only necessary if your default cache directory (~/.cache) does not have enough disk space to accomodate the entire repository.

Chat Interface

You can use the Gradio chat interface to run the models.

First, install the required packages:

pip install -r ChatApp/requirements.txt

Set the Python path to the root directory of the repository (necessary for importing the required modules):

export PYTHONPATH=$PYTHONPATH:$(pwd)

Then you can simply run:

python ChatApp/app.py

You can then navigate to http://localhost:7860 on your browser to access the interface.

CLI Interface

The repository also provides example code for running the models.

python llama2_onnx_inference.py --onnx_file FP16/LlamaV2_7B_float16.onnx --embedding_file embeddings.pth --tokenizer_path tokenizer.model --prompt "What is the lightest element?"

Output:

The lightest element is hydrogen. Hydrogen is the lightest element on the periodic table, with an atomic mass of 1.00794 u (unified atomic mass units).

FAQ

Why is the first inference session slow?

ONNX runtime execution provider might need to generate JIT binaries for the underlying hardware, typically the binary is cache and will be loaded directly in the subsequent runs to reduce the overhead.

Why is FP16 slower than FP32 on my device?

Your device may not support native FP16 math, therefore weights will be cast to FP32 at runtime. Using the FP32 version of the model will avoid the cast overhead.

How do I optimize inference?

It's recommended that inputs/outputs are put on target device to avoid expensive data copies, please refer to the following documentations for details:

I/O Binding | onnxruntime

What generation parameters should I use the model with?

You can perform temperature and top-p sampling with the provided example code. Please refer to Meta's example here.

Model info

text-generation
onnx text generation text-generation en license:llama2 region:us
↓ 0 ♥ 8

Created: 2023-09-21

Updated: 2023-09-21

View on Hugging Face

Files (1203)

.gitattributes
ChatApp/app.py
ChatApp/app_modules/__pycache__/overwrites.cpython-39.pyc
ChatApp/app_modules/__pycache__/presets.cpython-39.pyc
ChatApp/app_modules/__pycache__/utils.cpython-39.pyc
ChatApp/app_modules/overwrites.py
ChatApp/app_modules/presets.py
ChatApp/app_modules/utils.py
ChatApp/assets/custom.css
ChatApp/assets/custom.js
ChatApp/interface/__pycache__/base_interface.cpython-39.pyc
ChatApp/interface/__pycache__/empty_stub_interface.cpython-39.pyc
ChatApp/interface/__pycache__/hddr_llama_onnx_interface.cpython-39.pyc
ChatApp/interface/base_interface.py
ChatApp/interface/empty_stub_interface.py
ChatApp/interface/hddr_llama_onnx_interface.py
ChatApp/requirements.txt
FP16-Chat/.gitattributes
FP16-Chat/.gitignore
FP16-Chat/Constant_69_attr__value
FP16-Chat/Constant_77_attr__value
FP16-Chat/LlamaV2_7B_FT_float16.onnx ONNX
FP16-Chat/embeddings.data
FP16-Chat/onnx__MatMul_20670
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README.md
embeddings-chat.pth
embeddings.pth
llama2_onnx_inference.py
requirements.txt
tokenizer.model

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