How to build an LLM

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{html} <p><strong>Step 1: Choose a Model Architecture and Framework</strong></p> <ul> <li><strong>Architecture:</strong> <ul> <li><span>Simple RNN/GRU:</span><span> TensorFlow/Keras or PyTorch</span></li> <li><span>Single-headed Transformer Encoder:</span><span> TensorFlow/Keras or Hugging Face Transformers</span></li> </ul> </li> <li><strong>Resources:</strong> <ul> <li><span>TensorFlow Tutorials:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://www.tensorflow.org/tutorials">https://www.tensorflow.org/tutorials</a></li> <li><span>PyTorch Tutorials:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://pytorch.org/tutorials">https://pytorch.org/tutorials</a></li> <li><span>Hugging Face Transformers:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://huggingface.co/transformers/">https://huggingface.co/transformers/</a></li> </ul> </li> </ul> <br> <p><strong>Step 2: Prepare Your Training Dataset</strong></p> <ul> <li><strong>Dataset Size:</strong><span> Start with a small,</span><span> manageable corpus (e.</span><span>g.,</span><span> BookCorpus,</span><span> Twitter Sentiment,</span><span> or domain-specific datasets).</span></li> <li><strong>Preprocessing:</strong> <ul> <li><span>Tokenization:</span><span> NLTK or spaCy</span></li> <li><span>Cleaning:</span><span> pandas or NumPy</span></li> <li><span>Formatting:</span><span> TensorFlow/Keras or PyTorch data loading utilities</span></li> </ul> </li> <li><strong>Resources:</strong> <ul> <li><span>NLTK:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://www.nltk.org/">https://www.nltk.org/</a></li> <li><span>spaCy:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://spacy.io/">https://spacy.io/</a></li> <li><span>pandas:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://pandas.pydata.org/">https://pandas.pydata.org/</a></li> <li><span>NumPy:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://numpy.org/">https://numpy.org/</a></li> </ul> </li> </ul> <br> <p><strong>Step 3: Implement Model and Training Loop</strong></p> <ul> <li><strong>Framework:</strong><span> TensorFlow/Keras or PyTorch</span></li> <li><strong>Code Structure:</strong> <ul> <li><span>Define model architecture with chosen framework</span></li> <li><span>Implement loss function (e.</span><span>g.,</span><span> cross-entropy)</span></li> <li><span>Choose optimizer (e.</span><span>g.,</span><span> Adam)</span></li> <li><span>Set up mini-batch training loop</span></li> </ul> </li> <li><strong>Resources:</strong> <ul> <li><span>TensorFlow/Keras guides:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://www.tensorflow.org/guide">https://www.tensorflow.org/guide</a></li> <li><span>PyTorch tutorials:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://pytorch.org/tutorials">https://pytorch.org/tutorials</a></li> </ul> </li> </ul> <br> <p><strong>Step 4: Fine-tune and Evaluate</strong></p> <ul> <li><strong>Training:</strong> <ul> <li><span>Monitor loss and adjust hyperparameters</span></li> <li><span>Experiment with different learning rates and batch sizes</span></li> </ul> </li> <li><strong>Evaluation:</strong> <ul> <li><span>Design test tasks for your LLM's functionality</span></li> <li><span>Track performance metrics (e.</span><span>g.,</span><span> accuracy,</span><span> perplexity)</span></li> </ul> </li> </ul> <br> <p><strong>Step 5: Iterate and Improve</strong></p> <ul> <li><strong>Experimentation:</strong> <ul> <li><span>Try different model architectures or hyperparameters</span></li> <li><span>Explore diverse training data or techniques</span></li> </ul> </li> <li><strong>Interpretability:</strong> <ul> <li><span>Understand model behavior using techniques like attention visualization</span></li> <li><span>Address potential biases and limitations</span></li> </ul> </li> <li><strong>Resources:</strong> <ul> <li><span>JAX:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://github.com/google/jax">https://github.com/google/jax</a><span> (for advanced model optimization)</span></li> <li><span>TensorBoard:</span><span> </span><a class="traceable-link" target="_blank" rel="noopener noreferrer" href="https://www.tensorflow.org/tensorboard">https://www.tensorflow.org/tensorboard</a><span> (for visualization)</span></li> </ul> </li> </ul> <p><strong>Additional Tips:</strong></p> <ul> <li><span>Utilize cloud platforms (Google Colab,</span><span> Paperspace) for GPU/TPU access if needed.</span></li> <li><span>Consult open-source LLM projects for inspiration and code examples.</span></li> <li><span>Engage in online communities and forums for support and knowledge sharing.</span></li> </ul><span> <br> <p style="text-align: center"> <iframe width="560" height="315" src="https://www.youtube.com/embed/UU1WVnMk4E8?si=DXqG0dXca7v8YDpn" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen></iframe> <p>See: <a href="https://www.freecodecamp.org/news/how-to-build-a-large-language-model-from-scratch-using-python/">How to build a large language model from scratch using python</a></p> </p> {/html}
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