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metadata
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
language:
  - en
tags:
  - mistral
  - onnxruntime
  - onnx
  - llm

This is an optimized version of the Mistral 7B model, available on this repository: https://huggingface.co/mistralai/Mistral-7B-v0.1 and under the license on such repository. Microsoft permits you to use, modify, redistribute, and create derivatives of Microsoft's contributions to the optimized version subject to the restrictions and disclaimers of warranty and liability in license agreement.

Mistral-7b for ONNX Runtime

Introduction

This repository hosts the optimized versions of Mistral-7B-v0.1 to accelerate inference with ONNX Runtime CUDA execution provider.

See the usage instructions for how to inference this model with the ONNX files hosted in this repository.

Model Description

  • Developed by: MistralAI
  • Model type: Pretrained generative text model
  • License: Apache 2.0 License
  • Model Description: This is a conversion of the Mistral-7B-v0.1 for ONNX Runtime inference with CUDA execution provider.

Performance Comparison

Latency for token generation

Below is average latency of generating a token using a prompt of varying size using NVIDIA A100-SXM4-80GB GPU, taken from the ORT benchmarking script for Mistral

Prompt Length Batch Size PyTorch 2.1 torch.compile ONNX Runtime CUDA
32 1 32.58ms 12.08ms
256 1 54.54ms 23.20ms
1024 1 100.6ms 77.49ms
2048 1 236.8ms 144.99ms
32 4 63.71ms 15.32ms
256 4 86.74ms 75.94ms
1024 4 380.2ms 273.9ms
2048 4 N/A 554.5ms

Usage Example

Following the benchmarking instructions. Example steps:

  1. Clone onnxruntime repository.
git clone https://github.com/microsoft/onnxruntime
cd onnxruntime
  1. Install required dependencies
python3 -m pip install -r onnxruntime/python/tools/transformers/models/llama/requirements-cuda.txt
  1. Inference using manual model API, or use Hugging Face's ORTModelForCausalLM
from optimum.onnxruntime import ORTModelForCausalLM
from onnxruntime import InferenceSession
from transformers import AutoConfig, AutoTokenizer

sess = InferenceSession("Mistral-7B-v0.1.onnx", providers = ["CUDAExecutionProvider"])
config = AutoConfig.from_pretrained("mistralai/Mistral-7B-v0.1")

model = ORTModelForCausalLM(sess, config, use_cache = True, use_io_binding = True)

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")

inputs = tokenizer("Instruct: What is a fermi paradox?\nOutput:", return_tensors="pt")

outputs = model.generate(**inputs)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))