Prerequisites
- TensorFlow Serving: Install TensorFlow Serving on your server or cloud platform. You can use a Docker container or install it from source.
- Llama 3.1 model: Download the Llama 3.1 model weights from the Meta AI website.
- Python: Install Python 3.7 or later on your server or cloud platform.
- TensorFlow: Install TensorFlow 2.4 or later on your server or cloud platform.
- Docker (optional): Install Docker on your server or cloud platform to use a containerized TensorFlow Serving setup.
Step 1: Prepare the Llama 3.1 model
- Download the Llama 3.1 model weights from the Meta AI website.
- Extract the model weights to a directory on your server or cloud platform, e.g.,
/models/llama_3_1. - Create a
model_config.json file in the same directory with the following content:
{
"model_name": "llama_3_1",
"model_type": "transformer",
"num_layers": 12,
"hidden_size": 768,
"num_heads": 12,
"vocab_size": 32000
}
Step 2: Create a TensorFlow Serving model
- Create a new directory for your TensorFlow Serving model, e.g.,
/models/tfserving_llama_3_1. - Copy the
model_config.json file from the previous step into this directory. - Create a
model.py file in this directory with the following content:
import tensorflow as tf
def llama_3_1_model(input_ids, attention_mask):
# Load the pre-trained Llama 3.1 model
model = tf.keras.models.load_model('/models/llama_3_1/model_weights.h5')
# Create a new input layer for the model input_layer = tf.keras.layers.Input(shape=(input_ids.shape[1],), name='input_ids') attention_mask_layer = tf.keras.layers.Input(shape=(attention_mask.shape[1],), name='attention_mask') # Create a new output layer for the model output_layer = model(input_layer, attention_mask=attention_mask_layer) # Create a new model with the input and output layers model = tf.keras.Model(inputs=[input_layer, attention_mask_layer], outputs=output_layer) return model
Step 3: Compile the TensorFlow Serving model
- Run the following command to compile the TensorFlow Serving model:
tensorflow_model_server --port=8501 --rest_api_port=8502 --model_config_file=model_config.json --model_base_path=/models/tfserving_llama_3_1
Step 4: Start the TensorFlow Serving server
- Run the following command to start the TensorFlow Serving server:
tensorflow_model_server --port=8501 --rest_api_port=8502 --model_config_file=model_config.json --model_base_path=/models/tfserving_llama_3_1
Step 5: Test the TensorFlow Serving model
- Use a tool like
curl to test the TensorFlow Serving model:
curl -X POST -H "Content-Type: application/json" -d '{"input_ids": [1, 2, 3], "attention_mask": [1, 1, 1]}' http://localhost:8501/v1/models/llama_3_1:predict
This should return a response with the predicted output.
Step 6: Integrate with your chatbot
- Use a programming language like Python to create a chatbot that sends input to the TensorFlow Serving model and receives the predicted output.
- Use a library like
requests to send HTTP requests to the TensorFlow Serving model.
Here's an example Python code snippet that demonstrates how to integrate with the TensorFlow Serving model:
import requests
def get_response(input_text):
input_ids = [1, 2, 3] # Replace with actual input IDs
attention_mask = [1, 1, 1] # Replace with actual attention mask
payload = {'input_ids': input_ids, 'attention_mask': attention_mask} response = requests.post('http://localhost:8501/v1/models/llama_3_1:predict', json=payload) return response.json()
input_text = "Hello, how are you?"
response = get_response(input_text)
print(response)
This code snippet sends the input text to the TensorFlow Serving model and prints the predicted output.
That's it! You've successfully set up Llama 3.1 using TensorFlow
No comments:
Post a Comment