Generative AI: Techniques to Combine multiple LLMs into 1 Master LLM

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.