Welcome to the wonderful world of AI optimization! In this guide, we will explore how to utilize the optimized version of the Mistral-7B model, which has been specifically designed for performance boosts through ONNX Runtime’s CUDA execution provider. Get ready to accelerate your inference processes!

What is Mistral-7B?

Mistral-7B is a pretrained generative text model developed by MistralAI, available under the Apache 2.0 License. This model serves as the powerhouse behind advanced natural language processing tasks, enabling you to generate coherent and contextually relevant text.

Performance Insights

Before we dive into the practicalities of using the Mistral-7B model, let’s take a moment to appreciate its performance metrics. When comparing latency for token generation across various batch sizes and prompt lengths on the NVIDIA A100-SXM4-80GB GPU, you’ll find significant differences in efficiency:

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           NA                  554.5ms

This table illustrates just how much faster ONNX Runtime can execute various token generations, making it an essential toolbox for enhancing machine learning workflows.

Step-by-Step Guide to Usage

To start using Mistral-7B for inference, follow these straightforward steps:

• Clone the ONNX Runtime repository:

  git clone https://github.com/microsoft/onnxruntime

• Navigate to the cloned directory:

  cd onnxruntime

• Install the required dependencies:

  python3 -m pip install -r onnxruntime/python/tools/transformers/models/llama/requirements-cuda.txt

Inference Example

Here’s a simplified code snippet to help you get started with inference:

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))

This example calls upon the model to generate answers based on a prompt, showcasing the elegance of AI in understanding and generating human-like responses.

Troubleshooting

If you encounter any issues, here are some troubleshooting tips:

• Ensure that you have the correct versions of Python and the necessary libraries installed.
• Verify that you are using a compatible GPU and that CUDA is properly configured.
• Check the model file path if an error indicates it cannot find `Mistral-7B-v0.1.onnx`.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.