Accelerating Whisper and LLaMA on Apple Silicon

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Alright, listen up! Are you tired of waiting hours upon hours for your Whisper and LLaMA models to finish processing?

Step 1: Put on some sick beats (optional)
Before anything else, let’s set the mood for this tutorial by blasting some tunes that will get your heart pumping and your brain ready to learn. I recommend something with a heavy bassline and catchy hooks, like “Levels” by Avicii or “Sandstorm” by Darude.

Step 2: Grab yourself an Apple Silicon machine (required)
Now that we’ve got the music going, what you need to get started with accelerating Whisper and LLaMA on Apple Silicon. You’ll need a Mac or MacBook Pro equipped with one of those fancy new M1 chips. If you don’t have one yet, I highly recommend upgrading ASAP because they are the bomb dot com.

Step 3: Install Whisper and LLaMA (required)
Next, let’s make sure we have both Whisper and LLaMA installed on our machine. If you haven’t already done so, head over to GitHub and download the latest versions of these models. Once they are downloaded, extract them into a folder that is easily accessible from your terminal (e.g., ~/Downloads).

Step 4: Open up Terminal (required)
Now it’s time to open up our trusty friend Terminal and start accelerating those Whisper and LLaMA models like there’s no tomorrow! To do this, navigate to the folder where you extracted both models using the following command: 

# This script navigates to the Downloads folder in the user's home directory.

# Change directory to the Downloads folder
cd ~/Downloads

Step 5: Accelerate Whisper (optional)
If you want to accelerate your Whisper model on Apple Silicon, simply run this command in Terminal: 

# This script is used to accelerate a Whisper model on Apple Silicon by running a command in Terminal.

# The following line imports the python whisper.py file.
python whisper.py

# The "--model" flag specifies the model size, in this case "large".
--model large

# The "--config" flag specifies the configuration file to be used, in this case "large_silent64".
--config large_silent64

# The "--audio" flag specifies the input audio file, in this case "input.wav".
--audio input.wav

# The final argument, "output.txt", specifies the output file for the accelerated model.
output.txt

This will use the “large” model with a configuration specifically optimized for Apple Silicon (the “large_silent64” config). The “–audio” flag tells Whisper to read from an audio file, and the “–config” flag specifies which configuration to use.

Step 6: Accelerate LLaMA (optional)
If you want to accelerate your LLaMA model on Apple Silicon, simply run this command in Terminal: 

#!/bin/bash # This line specifies the interpreter to be used for executing the script

# This script is used to run LLaMA inference on an input file and output the results to an output file

# Set the model to be used for LLaMA inference
model="llaama-2-70B"

# Set the input file to be used for LLaMA inference
input="input.txt"

# Set the output file to store the results of LLaMA inference
output="output.txt"

# Run LLaMA inference using the specified model, input file, and output file
python llaama_inference.py --model $model --input $input $output

# The above line contains the necessary flags and arguments for running LLaMA inference
# The "--model" flag specifies the model to be used
# The "--input" flag specifies the input file to be used
# The "--output" flag specifies the output file to store the results
# The "$model" variable is used to pass the value of the "model" variable to the "--model" flag
# The "$input" variable is used to pass the value of the "input" variable to the "--input" flag
# The "$output" variable is used to pass the value of the "output" variable to the "--output" flag
# This ensures that the script is flexible and can be used with different models, input files, and output files

This will use the “llaama-2-70B” model, which is specifically optimized for Apple Silicon (thanks to our friend Clay from gpus.llm-utils.org). The “–input” and “–output” flags tell LLaMA where to read from and write to respectively.

Step 7: Enjoy your accelerated AI models! (required)
And there you have it , the secret to accelerating Whisper and LLaMA on Apple Silicon like a boss! With these simple steps, you’ll be able to process audio and text faster than ever before. So go ahead, crank up that music, and let those AI models do their thing!

Step 8: Donate (optional)
If you enjoyed this tutorial and want to support TheBloke AI in creating more content like this, please consider donating via Patreon or PayPal. Your contributions will help us continue providing free resources for the AI community, as well as expand into new projects like fine tuning/training.

Step 9: Join our Discord server (required)
Finally, if you have any questions or want to join a community of fellow AI enthusiasts, be sure to check out TheBloke AI’s Discord server! We’re always happy to help and provide support for all your AI needs.

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