Generative AI: Combining multiple LLMs into a Master LLM using Knowledge Distillation

Combining multiple open-source large language models (LLMs) into a single, more powerful model assuming the constraints of basic coding skills, a simple PC, and internet connection using knowledge distillation.

What is Knowledge Distillation?

Knowledge distillation involves training a smaller, student model to mimic the behavior of a larger, more complex teacher model (or an ensemble of models). This process transfers knowledge from the teacher(s) to the student, resulting in a model that retains much of the accuracy and performance of the original models but is more efficient. This process leverages the strengths of each individual model and transfers that knowledge to a more efficient model that can be deployed on simpler hardware.

Steps to Combine LLMs Using Knowledge Distillation

Step 1: Prepare the Environment

1. Install Python and Libraries: Ensure you have Python installed. Install the necessary libraries:

   bash
   pip install torch transformers datasets
   

2. Select the Models: Choose three top-performing open-source LLMs. For this example, let's use:

   • GPT-NeoX
   • BLOOM
   • LLaMA

Step 2: Load and Prepare the Models

1. Load the Models: Create a Python script to load the models.

   python
   from transformers import AutoModelForCausalLM, AutoTokenizer
   
   # Load GPT-NeoX
   gpt_neox_model_name = "EleutherAI/gpt-neox-20B"
   gpt_neox_tokenizer = AutoTokenizer.from_pretrained(gpt_neox_model_name)
   gpt_neox_model = AutoModelForCausalLM.from_pretrained(gpt_neox_model_name)
   
   # Load BLOOM
   bloom_model_name = "bigscience/bloom"
   bloom_tokenizer = AutoTokenizer.from_pretrained(bloom_model_name)
   bloom_model = AutoModelForCausalLM.from_pretrained(bloom_model_name)
   
   # Load LLaMA
   llama_model_name = "meta-llama/LLaMA-13B"
   llama_tokenizer = AutoTokenizer.from_pretrained(llama_model_name)
   llama_model = AutoModelForCausalLM.from_pretrained(llama_model_name)
   

Step 3: Create a Student Model

1. Define the Student Model: Use a smaller model architecture suitable for your hardware. You might start with a smaller GPT-2 model.

   python
   from transformers import GPT2LMHeadModel
   
   student_model_name = "gpt2"
   student_tokenizer = AutoTokenizer.from_pretrained(student_model_name)
   student_model = GPT2LMHeadModel.from_pretrained(student_model_name)
   

2. Prepare a Dataset: Use a dataset for training the student model. You can use an open dataset like the WikiText dataset.

   python
   from datasets import load_dataset
   
   dataset = load_dataset("wikitext", "wikitext-2-raw-v1")
   

Step 4: Distillation Training Loop

1. Training Script: Create a training script for knowledge distillation.

   python
   import torch
   from torch.utils.data import DataLoader
   from transformers import AdamW
   
   # Function to generate logits from the teacher models
   def generate_teacher_logits(teacher_models, tokenizer, inputs):
       with torch.no_grad():
           logits = [model(**inputs).logits for model in teacher_models]
       return torch.mean(torch.stack(logits), dim=0)
   
   # Training loop
   def train_student_model(student_model, teacher_models, tokenizer, dataset, epochs=1, batch_size=2, lr=5e-5):
       student_model.train()
       optimizer = AdamW(student_model.parameters(), lr=lr)
   
       train_loader = DataLoader(dataset["train"], batch_size=batch_size)
   
       for epoch in range(epochs):
           for batch in train_loader:
               inputs = tokenizer(batch['text'], return_tensors='pt', padding=True, truncation=True)
               teacher_logits = generate_teacher_logits(teacher_models, tokenizer, inputs)
   
               student_outputs = student_model(**inputs)
               student_logits = student_outputs.logits
   
               loss = torch.nn.functional.mse_loss(student_logits, teacher_logits)
   
               optimizer.zero_grad()
               loss.backward()
               optimizer.step()
   
               print(f"Epoch: {epoch}, Loss: {loss.item()}")
   
   teacher_models = [gpt_neox_model, bloom_model, llama_model]
   train_student_model(student_model, teacher_models, student_tokenizer, dataset)
   

Step 5: Evaluate the Student Model

1. Evaluation Script: Evaluate the performance of the distilled student model.

   python
   def evaluate_model(model, tokenizer, dataset, num_samples=100):
       model.eval()
       correct = 0
       total = 0
   
       eval_loader = DataLoader(dataset["test"], batch_size=1)
   
       for i, batch in enumerate(eval_loader):
           if i >= num_samples:
               break
   
           inputs = tokenizer(batch['text'], return_tensors='pt', padding=True, truncation=True)
           with torch.no_grad():
               outputs = model(**inputs)
           predictions = torch.argmax(outputs.logits, dim=-1)
           labels = inputs['input_ids']
   
           correct += (predictions == labels).sum().item()
           total += labels.numel()
   
       accuracy = correct / total
       print(f"Accuracy: {accuracy:.2f}")
   
   evaluate_model(student_model, student_tokenizer, dataset)
   

Step 6: Deploy the Student Model

1. Update the Flask App: Modify the app.py to use the distilled student model.

   python
   from flask import Flask, request, jsonify, send_from_directory
   from transformers import GPT2LMHeadModel, AutoTokenizer
   import torch
   
   app = Flask(__name__)
   
   student_model_name = "gpt2"
   tokenizer = AutoTokenizer.from_pretrained(student_model_name)
   model = GPT2LMHeadModel.from_pretrained(student_model_name)
   
   model.to("cuda" if torch.cuda.is_available() else "cpu")
   model.eval()
   
   @app.route('/chat', methods=['POST'])
   def chat():
       data = request.json
       user_input = data.get("message")
       inputs = tokenizer(user_input, return_tensors='pt', padding=True, truncation=True)
       inputs = {key: val.to("cuda" if torch.cuda.is_available() else "cpu") for key, val in inputs.items()}
       with torch.no_grad():
           outputs = model(**inputs)
       response = tokenizer.decode(torch.argmax(outputs.logits, dim=-1)[0], skip_special_tokens=True)
       return jsonify({"response": response})
   
   @app.route('/')
   def index():
       return send_from_directory('', 'index.html')
   
   if __name__ == '__main__':
       app.run(host='0.0.0.0', port=5000)
   

2. Deploy Follow the deployment steps for Heroku or VPS.