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| import streamlit as st | |
| import torch | |
| import os | |
| from GPTLanguageModelClass import hyperparams | |
| st.set_page_config(page_title="LLM from Scratch Demo") | |
| st.title("LLM from Scratch Demo") | |
| block_size = hyperparams.block_size | |
| device = hyperparams.device | |
| if not os.path.exists("./vocab.txt"): | |
| st.error("Please run extract.py first") | |
| st.stop() | |
| with open("./vocab.txt", "r", encoding="utf-8") as f: | |
| chars = sorted(list(set(f.read()))) | |
| string_to_int = {ch: i for i, ch in enumerate(chars)} | |
| int_to_string = {i: ch for i, ch in enumerate(chars)} | |
| def encode(s): | |
| return [string_to_int[ch] for ch in s] | |
| def decode(x): | |
| return "".join([int_to_string[i] for i in x]) | |
| def load_model(): | |
| model_pickle_path = "./model.pt" | |
| with open(model_pickle_path, "rb") as f: | |
| model = torch.load(f, map_location=device, weights_only=False) | |
| return model | |
| model = load_model() | |
| if "result" not in st.session_state: | |
| st.session_state.result = None | |
| if "prompt" not in st.session_state: | |
| st.session_state.prompt = "" | |
| def clear_results(): | |
| st.session_state.result = None | |
| st.session_state.prompt = "" | |
| st.subheader("About") | |
| st.markdown( | |
| 'This is a demo of a language model built from scratch using PyTorch. It generates text continuations based on a *character*-level GPT architecture trained on the [OpenWebText dataset](https://github.com/jcpeterson/openwebtext). What this means is that this model will "predict" the next character based on all previous characters. This model was built from scratch using PyTorch, following the [paper](https://arxiv.org/abs/1706.03762) "Attention is all you need". The goal of this project was to gain a deep familiarity with the underlying structure of an LLM. The model was trained on commodity hardware and utilized a comparatively small dataset size and model size.' | |
| ) | |
| st.subheader("Model") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.write(f"**Device:** {device}") | |
| st.write(f"**Vocab size:** {len(chars)}") | |
| st.write(f"**Block size:** {block_size}") | |
| st.write(f"**Batch size:** {hyperparams.batch_size}") | |
| with col2: | |
| st.write(f"**Max iters:** {hyperparams.max_iters}") | |
| st.write(f"**Learning rate:** {hyperparams.learning_rate}") | |
| st.write(f"**Eval every:** {hyperparams.eval_every}") | |
| st.write(f"**n_embd:** {hyperparams.n_embd}") | |
| with col3: | |
| st.write(f"**n_head:** {hyperparams.n_head}") | |
| st.write(f"**n_layer:** {hyperparams.n_layer}") | |
| st.write(f"**Dropout:** {hyperparams.dropout}") | |
| st.subheader("Demo") | |
| st.write( | |
| "Enter some text (up to 127 characters) and click 'Generate' to see " | |
| "the model's continuation" | |
| ) | |
| prompt = st.text_area( | |
| "Enter text to autocomplete:", | |
| height=50, | |
| max_chars=block_size - 1, | |
| key="prompt", | |
| placeholder="Type here...", | |
| ) | |
| generate_clicked = st.button("Generate") | |
| clear_clicked = st.button("Clear Results", on_click=clear_results) | |
| if generate_clicked or len(prompt) != 0: | |
| if prompt.strip(): | |
| context = torch.tensor(encode(prompt), dtype=torch.long, device=device) | |
| max_new_tokens = block_size - len(prompt) | |
| generated = model.generate(context.unsqueeze(0), max_new_tokens=max_new_tokens)[ | |
| 0 | |
| ] | |
| full_text = decode(generated.tolist()) | |
| st.session_state.result = { | |
| "input": prompt, | |
| "continuation": full_text[len(prompt) :], | |
| "full": full_text, | |
| } | |
| else: | |
| st.warning("Please enter some text to autocomplete.") | |
| st.session_state.result = None | |
| if st.session_state.result: | |
| st.subheader("Result") | |
| st.write("**Your input:**") | |
| st.write(st.session_state.result["input"]) | |
| st.write("**Generated continuation:**") | |
| st.write(st.session_state.result["continuation"]) | |
| st.write("**Full text:**") | |
| st.write(st.session_state.result["full"]) | |
| st.markdown("---") | |
| st.markdown( | |
| "Connect with me" | |
| ": [GitHub](https://github.com/ibrahimmkhalid/llm-from-scratch) " | |
| "| [LinkedIn](https://linkedin.com/in/ibrahimmkhalid) " | |
| "| [Website](https://ibrahimkhalid.me) " | |
| "| [ibrahimmkhalid@gmail.com](mailto:ibrahimmkhalid@gmail.com)" | |
| ) | |