<iframe class="ql-video" frameborder="0" allowfullscreen="true" src="https://www.youtube.com/embed/VPRSBzXzavo?showinfo=0"></iframe><p></p><h2><strong>Step 1: Data Collection and Preprocessing</strong></h2><p>The first step in developing a small-scale version of ChatGPT is to collect and preprocess the data. The model will be trained on a text dataset, such as articles, books, or social media posts. The more available data, the better the model will be at understanding and generating natural language.</p><p></p><p></p><pre data-language="plain"> import pandas as pd # Load data into a pandas dataframe data = pd.read_csv(&quot;your_text_data.file&quot;) # Preprocessing the data data = data.dropna() # remove missing values data = data.drop_duplicates() # remove duplicate values data = data.sample(frac=1) # shuffle the data </pre><h2>Step 2: Tokenization</h2><p>Once the data has been collected and preprocessed, the next step is to tokenize the text. Tokenization is breaking down the text into individual words or subwords. This can be done using a library such as <strong>NLTK</strong> or <strong>Hugging Face&#39;s tokenizers</strong>.</p><p></p><p></p><pre data-language="plain"> import transformers from transformers import AutoTokenizer # Instantiate a tokenizer tokenizer = AutoTokenizer.from_pretrained(&quot;distilgpt2&quot;) # Tokenize the text text = data[&#39;text&#39;].values tokenized_text = tokenizer(text, padding=True, truncation=True) </pre><h2>Step 3: Model Architecture</h2><p>The next step is to design the model architecture. In this case, we will use a transformer-based architecture, which is well-suited for NLP tasks. The transformer-based architecture consists of an encoder and a decoder, both of which are made up of multiple layers of multi-head self-attention and feed-forward neural networks.</p><p></p><p></p><pre data-language="plain"> from transformers import AutoModelWithLMHead # Instantiate a model model = AutoModelWithLMHead.from_pretrained(&quot;distilgpt2&quot;) </pre><h2>Step 4: Training</h2><p>Once the model architecture is designed, the model can be trained. This involves feeding the tokenized text into the model and adjusting the model&#39;s parameters to minimize the difference between the model&#39;s output and the expected output.</p><p></p><p></p><pre data-language="plain"> from transformers import TrainingArguments, Trainer # Define training arguments training_args = TrainingArguments( output_dir=&#39;./results&#39;, evaluation_strategy=&#39;steps&#39;, eval_steps = 1000, per_device_train_batch_size=1, per_device_eval_batch_size=1, num_train_epochs=1, save_steps=1000, save_total_limit=2 ) # Instantiate a trainer trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_text, eval_dataset=tokenized_text ) # Train the model trainer.train() </pre><h2>Step 5: Evaluation</h2><p>After the model is trained, it is evaluated to see how well it performs on a test data set. This can involve comparing the model’s output to human-generated text to see how similar they are or using other metrics, such as <strong>perplexity</strong> or <strong>BLEU</strong> scores, to measure the model’s performance.</p><p></p><p><strong>Perplexity</strong> evaluates how well a model predicts a given text, and <strong>BLEU</strong> score evaluates how well a generated text matches a reference text; the higher the score the better the text generated is.</p><p><strong>Perplexity</strong> measures how well a probability distribution or language model predicts a sample. It is defined as the exponential of the cross-entropy of the model’s predictions for the sample. A lower perplexity score indicates that the model’s predictions are more certain and that the model is better at predicting the sample.</p><p><strong>BLEU</strong> <strong>(Bilingual Evaluation Understudy)</strong> is a method for evaluating the quality of text generated by machine translation systems, but it can also be used for other text-generation tasks. BLEU compares the generated text to a set of reference translations and calculates a score based on the number of n-grams that match the generated text and the reference translations. A higher BLEU score indicates that the generated text is more similar to the reference translations and is considered higher quality.</p><p></p><pre data-language="plain"> from transformers import EvalPrediction # Evaluate the model eval_result = trainer.evaluate() # Extract the perplexity score perplexity = eval_result[&#39;perplexity&#39;] # Extract the BLEU score bleu_score = eval_result[&#39;bleu&#39;] </pre><h2>Step 6: Deployment</h2><p>Once the model performs well, it can be deployed for use. This can be done using a cloud service like AWS, GCP or Azure or by deploying the model to an on-premises server. The server will have the hardware, such as high-performance GPUs, to run the model in real time. 1</p><p></p><p></p><pre data-language="plain"> from transformers import pipeline # Instantiate a text generation pipeline text_generator = pipeline(&quot;text-generation&quot;, model=model) # Generate text generated_text = text_generator(&quot;The brown fine fox jumps over the lazy frog.&quot;, max_length=100) </pre><h2></h2>