In the rapidly evolving field of artificial intelligence, Meta has recently unveiled Llama 3, a groundbreaking large language model that promises to revolutionize the way we interact with and leverage AI technology. This powerful tool, trained on an unprecedented 15 trillion tokens of data, boasts remarkable capabilities in understanding and generating human-like text, making it a game-changer for a wide range of applications.
With all the information above, Let's get started!
Before getting started, make sure you have the following:
The first step is to install the Llama 3 language model on your local machine. Llama 3 is open-source and can be found on GitHub.
git clone https://github.com/facebookresearch/llama.git
cd llama
pip install -r requirements.txt
.pth file in the llama/models directory.Next, we need to fine-tune Llama 3 on a dataset of code so it learns to understand and generate source code.
wget https://s3.amazonaws.com/code-search-net/CodeSearchNet/v2/python.zip
unzip python.zip
finetune.py script to train Llama 3 on the code dataset:python finetune.py \
--base_model decapoda-research/llama-7b-hf \
--data_path python/train \
--output_dir python_model \
--num_train_epochs 3 \
--batch_size 128
This will fine-tune Llama 3 on the Python code dataset for 3 epochs and save the fine-tuned model to the python_model directory.
Now that we have a code-savvy language model, let's build a user interface for our AI coding assistant. We'll create a simple web app using the Flask framework.
pip install flask
app.py with the following code:from flask import Flask, request, jsonify
from transformers import AutoModelForCausalLM, AutoTokenizer
app = Flask(__name__)
model_path = "python_model"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
@app.route('/complete', methods=['POST'])
def complete():
data = request.get_json()
code = data['code']
input_ids = tokenizer.encode(code, return_tensors='pt')
output = model.generate(input_ids, max_length=100, num_return_sequences=1)
generated_code = tokenizer.decode(output[0])
return jsonify({'generated_code': generated_code})
if __name__ == '__main__':
app.run()
This creates a Flask web server with a /complete endpoint. It loads the fine-tuned Llama 3 model and tokenizer, and uses them to generate code completions when a POST request is sent to /complete with the current code as JSON data.
flask run
Finally, let's create a basic frontend for our coding assistant.
index.html with the following:<!DOCTYPE html>
<html>
<head>
<title>AI Coding Assistant</title>
</head>
<body>
<h1>AI Coding Assistant</h1>
<textarea id="code" rows="10" cols="50"></textarea>
<br>
<button onclick="complete()">Complete Code</button>
<p>Generated Code:</p>
<pre id="generated-code"></pre>
<script>
function complete() {
var code = document.getElementById("code").value;
fetch('/complete', {
method: 'POST',
headers: {
'Content-Type': 'application/json'
},
body: JSON.stringify({code: code})
})
.then(response => response.json())
.then(data => {
document.getElementById("generated-code").textContent = data.generated_code;
});
}
</script>
</body>
</html>
This creates a simple HTML page with a textarea where the user can enter their code, and a button to send the code to the /complete API endpoint. The generated code completion is then displayed below.
index.html in a web browser and start coding with the help of your AI assistant!Congratulations, you now have a working AI coding assistant powered by Llama 3! Some additional things to try:
With the incredible capabilities of large language models like Llama 3, the possibilities are endless. I hope this guide has helped you get started with building your own AI coding tools. Happy coding!
