diff --git a/code/llm/llama3.1_local_rag/llama3.1_local_rag.py b/code/llm/llama3.1_local_rag/llama3.1_local_rag.py new file mode 100644 index 0000000..5664053 --- /dev/null +++ b/code/llm/llama3.1_local_rag/llama3.1_local_rag.py @@ -0,0 +1,52 @@ +import ollama +import streamlit as st +from langchain.text_splitter import RecursiveCharacterTextSplitter +from langchain_community.document_loaders import WebBaseLoader +from langchain_community.embeddings import OllamaEmbeddings +from langchain_community.vectorstores import Chroma + +st.title("Chat with Webpage 🌐") +st.caption("This app allows you to chat with a webpage using local llama3 and RAG") + +# Get the webpage URL from the user +webpage_url = st.text_input("Enter Webpage URL", type="default") + +if webpage_url: + # 1. Load the data + loader = WebBaseLoader(webpage_url) + docs = loader.load() + text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10) + splits = text_splitter.split_documents(docs) + + # 2. Create Ollama embeddings and vector store + embeddings = OllamaEmbeddings(model="llama3.1") + vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings) + + # 3. Call Ollama Llama3 model + def ollama_llm(question, context): + formatted_prompt = f"Question: {question}\n\nContext: {context}" + response = ollama.chat( + model="llama3.1", messages=[{"role": "user", "content": formatted_prompt}] + ) + return response["message"]["content"] + + # 4. RAG Setup + retriever = vectorstore.as_retriever() + + def combine_docs(docs): + return "\n\n".join(doc.page_content for doc in docs) + + def rag_chain(question): + retrieved_docs = retriever.invoke(question) + formatted_context = combine_docs(retrieved_docs) + return ollama_llm(question, formatted_context) + + st.success(f"Loaded {webpage_url} successfully!") + + # Ask a question about the webpage + prompt = st.text_input("Ask any question about the webpage") + + # Chat with the webpage + if prompt: + result = rag_chain(prompt) + st.write(result) diff --git a/code/llm/ollama/assistant.py b/code/llm/ollama/assistant.py new file mode 100644 index 0000000..dab3fd7 --- /dev/null +++ b/code/llm/ollama/assistant.py @@ -0,0 +1,11 @@ +from phi.assistant import Assistant +from phi.llm.ollama import Ollama +from rich.pretty import pprint + +assistant = Assistant( + llm=Ollama(model="llama3.1"), + description="You help people with their health and fitness goals.", +) +assistant.print_response("Share a quick healthy breakfast recipe.", markdown=True) +print("\n-*- Metrics:") +pprint(assistant.llm.metrics) # type: ignore diff --git a/code/llm/ollama/embedding.py b/code/llm/ollama/embedding.py new file mode 100644 index 0000000..93fdd54 --- /dev/null +++ b/code/llm/ollama/embedding.py @@ -0,0 +1,7 @@ +from phi.embedder.ollama import OllamaEmbedder + +embedder = OllamaEmbedder(model="llama3.1") +embeddings = embedder.get_embedding("Embed me") + +print(f"Embeddings: {embeddings}") +print(f"Dimensions: {len(embeddings)}") diff --git a/code/llm/openai/assistant.py b/code/llm/openai/assistant.py new file mode 100644 index 0000000..75c8810 --- /dev/null +++ b/code/llm/openai/assistant.py @@ -0,0 +1,10 @@ +from phi.assistant import Assistant +from phi.llm.openai import OpenAIChat +from phi.tools.duckduckgo import DuckDuckGo + +assistant = Assistant( + llm=OpenAIChat(model="gpt-4o", max_tokens=500, temperature=0.3), + tools=[DuckDuckGo()], + show_tool_calls=True, +) +assistant.print_response("Whats happening in Indonesia?", markdown=True) diff --git a/code/llm/openrouter/assistant.py b/code/llm/openrouter/assistant.py new file mode 100644 index 0000000..f8461e9 --- /dev/null +++ b/code/llm/openrouter/assistant.py @@ -0,0 +1,10 @@ +from phi.assistant import Assistant +from phi.llm.openrouter import OpenRouter + +assistant = Assistant( + llm=OpenRouter(model="mistralai/mistral-7b-instruct:free"), + description="You help people with their health and fitness goals.", +) +assistant.print_response( + "Share a 2 sentence quick and healthy breakfast recipe.", markdown=True +) diff --git a/code/llm/openrouter/pydantic_output.py b/code/llm/openrouter/pydantic_output.py new file mode 100644 index 0000000..0877d83 --- /dev/null +++ b/code/llm/openrouter/pydantic_output.py @@ -0,0 +1,34 @@ +from typing import List + +from phi.assistant import Assistant +from phi.llm.openrouter import OpenRouter +from pydantic import BaseModel, Field +from rich.pretty import pprint + + +class MovieScript(BaseModel): + setting: str = Field( + ..., description="Provide a nice setting for a blockbuster movie." + ) + ending: str = Field( + ..., + description="Ending of the movie. If not available, provide a happy ending.", + ) + genre: str = Field( + ..., + description="Genre of the movie. If not available, select action, thriller or romantic comedy.", + ) + name: str = Field(..., description="Give a name to this movie") + characters: List[str] = Field(..., description="Name of characters for this movie.") + storyline: str = Field( + ..., description="3 sentence storyline for the movie. Make it exciting!" + ) + + +movie_assistant = Assistant( + llm=OpenRouter(model="mistralai/mistral-7b-instruct:free"), + description="You help people write movie ideas.", + output_model=MovieScript, +) + +pprint(movie_assistant.run("New York")) diff --git a/docs/_toc.yml b/docs/_toc.yml index 96030d8..eb55f7c 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -113,9 +113,11 @@ parts: - file: deep_learning/keras_tuner - file: deep_learning/transfer_learning - - caption: LLM + - caption: Generative AI chapters: - - file: llm/intro_to_llm + - file: gen_ai/intro_to_gen_ai + - file: gen_ai/intro_to_llm + - file: gen_ai/local_llms - caption: Reference chapters: diff --git a/docs/deep_learning/keras_tuner.ipynb b/docs/deep_learning/keras_tuner.ipynb index 914afbb..6c36a7a 100644 --- a/docs/deep_learning/keras_tuner.ipynb +++ b/docs/deep_learning/keras_tuner.ipynb @@ -317,24431 +317,32 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "McO82AXOuxXh" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 85/1500\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.5589 - loss: 1.2810" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 128/1500\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.6045 - loss: 1.1472" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 171/1500\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.6325 - loss: 1.0642" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 215/1500\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.6533 - loss: 1.0027" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 258/1500\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.6684 - loss: 0.9575" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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accuracy: 0.8642 - loss: 0.3769" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1480/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8643 - loss: 0.3767" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8643 - loss: 0.3766 - val_accuracy: 0.8632 - val_loss: 0.3783\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 3/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.8955 - loss: 0.2839" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1481/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8955 - loss: 0.2839" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8955 - loss: 0.2840 - val_accuracy: 0.8817 - val_loss: 0.3297\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 6/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9101 - loss: 0.2420" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9101 - loss: 0.2421 - val_accuracy: 0.8718 - val_loss: 0.3550\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 9/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "\u001b[1m 1/1500\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:08\u001b[0m 46ms/step - accuracy: 0.9375 - loss: 0.1517" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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accuracy: 0.9089 - loss: 0.2353" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1493/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9089 - loss: 0.2353" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9089 - loss: 0.2353 - val_accuracy: 0.8920 - val_loss: 0.3055\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 10/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9223 - loss: 0.2075" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1496/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9223 - loss: 0.2076" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9223 - loss: 0.2076 - val_accuracy: 0.8936 - val_loss: 0.3115\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 13/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9432 - loss: 0.1508" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9431 - loss: 0.1509 - val_accuracy: 0.8903 - val_loss: 0.3748\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 22/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "\u001b[1m 1/1500\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:08\u001b[0m 46ms/step - accuracy: 0.9062 - loss: 0.1068" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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accuracy: 0.9454 - loss: 0.1430" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9454 - loss: 0.1431 - val_accuracy: 0.8960 - val_loss: 0.3444\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 23/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "\u001b[1m 1/1500\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:09\u001b[0m 46ms/step - accuracy: 0.9688 - loss: 0.0729" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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accuracy: 0.9493 - loss: 0.1340" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1497/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9492 - loss: 0.1342" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9492 - loss: 0.1342 - val_accuracy: 0.8918 - val_loss: 0.3569\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 25/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9558 - loss: 0.1169" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9558 - loss: 0.1169 - val_accuracy: 0.8903 - val_loss: 0.4025\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 32/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "\u001b[1m 1/1500\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:09\u001b[0m 46ms/step - accuracy: 0.9375 - loss: 0.0872" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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accuracy: 0.9624 - loss: 0.1005" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1484/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9623 - loss: 0.1006" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9623 - loss: 0.1007 - val_accuracy: 0.8935 - val_loss: 0.4260\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 35/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9633 - loss: 0.0963" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1474/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9633 - loss: 0.0964" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9633 - loss: 0.0964 - val_accuracy: 0.8891 - val_loss: 0.4603\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 38/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9652 - loss: 0.0902" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1488/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9652 - loss: 0.0903" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9652 - loss: 0.0903 - val_accuracy: 0.8904 - val_loss: 0.4852\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 42/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9721 - loss: 0.0724" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1474/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9720 - loss: 0.0726" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9720 - loss: 0.0728 - val_accuracy: 0.8953 - val_loss: 0.5138\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 48/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9717 - loss: 0.0750" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1401/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9717 - loss: 0.0751" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1441/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9717 - loss: 0.0751" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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accuracy: 0.9728 - loss: 0.0744" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1472/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9728 - loss: 0.0745" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9728 - loss: 0.0746 - val_accuracy: 0.8975 - val_loss: 0.5169\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 50/50\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9723 - loss: 0.0754" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1419/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9723 - loss: 0.0754" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1461/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9723 - loss: 0.0755" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9723 - loss: 0.0755 - val_accuracy: 0.8923 - val_loss: 0.5257\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Best epoch: 32\n" - ] - } - ], - "source": [ - "# Build the model with the optimal hyperparameters and train it on the data for 50 epochs\n", - "model = tuner.hypermodel.build(best_hps)\n", - "history = model.fit(img_train, label_train, epochs=50, validation_split=0.2)\n", - "\n", - "val_acc_per_epoch = history.history[\"val_accuracy\"]\n", - "best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1\n", - "print(\"Best epoch: %d\" % (best_epoch,))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uOTSirSTI3Gp" - }, - "source": [ - "Re-instantiate the hypermodel and train it with the optimal number of epochs from above." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "NoiPUEHmMhCe" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 301/1500\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.6902 - loss: 0.8786" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m 346/1500\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.6994 - loss: 0.8516" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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accuracy: 0.8679 - loss: 0.3663" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1477/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8678 - loss: 0.3663" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8678 - loss: 0.3664 - val_accuracy: 0.8658 - val_loss: 0.3632\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 3/32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9054 - loss: 0.2551" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9053 - loss: 0.2552 - val_accuracy: 0.8917 - val_loss: 0.3017\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 8/32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "\u001b[1m 1/1500\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:08\u001b[0m 46ms/step - accuracy: 0.9375 - loss: 0.1920" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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accuracy: 0.9086 - loss: 0.2475" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1410/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9085 - loss: 0.2475" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1453/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9085 - loss: 0.2475" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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accuracy: 0.9380 - loss: 0.1618" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1462/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9379 - loss: 0.1619" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9379 - loss: 0.1621 - val_accuracy: 0.8974 - val_loss: 0.3227\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 20/32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9468 - loss: 0.1450" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1461/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9467 - loss: 0.1451" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9467 - loss: 0.1451 - val_accuracy: 0.8979 - val_loss: 0.3486\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 23/32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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accuracy: 0.9537 - loss: 0.1233" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9537 - loss: 0.1233 - val_accuracy: 0.8942 - val_loss: 0.3985\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 30/32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "\u001b[1m 1/1500\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:09\u001b[0m 46ms/step - accuracy: 0.9688 - loss: 0.0652" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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accuracy: 0.9560 - loss: 0.1171" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1460/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9560 - loss: 0.1171" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9559 - loss: 0.1171 - val_accuracy: 0.8975 - val_loss: 0.3997\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 32/32\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1234/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9601 - loss: 0.1074" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1276/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9600 - loss: 0.1076" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1319/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9599 - loss: 0.1078" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1362/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9598 - loss: 0.1080" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m1406/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9597 - loss: 0.1082" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - 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"" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "metadata": {}, + "outputs": [], + "source": [ + "# Build the model with the optimal hyperparameters and train it on the data for 50 epochs\n", + "model = tuner.hypermodel.build(best_hps)\n", + "history = model.fit(img_train, label_train, epochs=50, validation_split=0.2)\n", + "\n", + "val_acc_per_epoch = history.history[\"val_accuracy\"]\n", + "best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1\n", + "print(\"Best epoch: %d\" % (best_epoch,))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uOTSirSTI3Gp" + }, + "source": [ + "Re-instantiate the hypermodel and train it with the optimal number of epochs from above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "hypermodel = tuner.hypermodel.build(best_hps)\n", "\n", @@ -24765,38 +366,6 @@ "id": "9E0BTp9Ealjb" }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m144/313\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8897 - loss: 0.4414" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m193/313\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8881 - loss: 0.4502" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m242/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8873 - loss: 0.4568" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", - "\u001b[1m290/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8872 - loss: 0.4588" - ] - }, { "name": "stdout", "output_type": "stream", diff --git a/docs/gen_ai/intro_to_gen_ai.md b/docs/gen_ai/intro_to_gen_ai.md new file mode 100644 index 0000000..a1abdd4 --- /dev/null +++ b/docs/gen_ai/intro_to_gen_ai.md @@ -0,0 +1,31 @@ +# Introduction to Generative AI + +Generative AI is artificial intelligence capable of generating text, images and other types of content. What makes it a fantastic technology is that it democratizes AI, anyone can use it with as little as a text prompt, a sentence written in a natural language. There's no need for you to learn a language like Python or SQL to accomplish something worthwhile, all you need is to use your language, state what you want and out comes a suggestion from an AI model. The applications and impact for this is huge, you write or understand reports, write applications and much more, all in seconds. + +## How did we get Generative AI? + +Despite the extraordinary hype created lately by the announcement of generative AI models, this technology is decades in the making, with the first research efforts dating back to 60s. We're now at a point with AI having human cognitive capabilities, like conversation as shown by for example OpenAI ChatGPT or Gemini. + +Backing up a bit, the very first prototypes of AI consisted of typewritten chatbots, relying on a knowledge base extracted from a group of experts and represented into a computer. The answers in the knowledge base were triggered by keywords appearing in the input text. However, it soon became clear that such approach, using typewritten chatbots, did not scale well. + +### A statistical approach to AI: Machine Learning + +A turning point arrived during the 90s, with the application of a statistical approach to text analysis. This led to the development of new algorithms – known with the name of machine learning - able to learn patterns from data, without being explicitly programmed. This approach allows a machine to simulate human language understanding: a statistical model is trained on text-label pairings, enabling the model to classify unknown input text with a pre-defined label representing the intention of the message. + +### Neural networks and modern virtual assistants + +In more recent times, the technological evolution of the hardware, capable of handling larger amounts of data and more complex computations, encouraged research in the AI fields, leading to the development of advanced machine learning algorithms – called neural networks or deep learning algorithms. + +Neural networks (and in particular Recurrent Neural Networks – RNNs) significantly enhanced natural language processing, enabling the representation of the meaning of text in a more meaningful way, valuing the context of a word in a sentence. + +This is the technology that powered the virtual assistants born in the first decade of the new century, very proficient in interpreting the human language, identifying a need, and performing an action to satisfy it – like answering with a pre-defined script or consuming a 3rd party service. + +### Present day, Generative AI + +So that’s how we came to Generative AI today, which can be seen as a subset of deep learning. + +![How did we get Gen AI](../images/gen_ai/gen_ai_history.png) + +After decades of research in the AI field, a new model architecture – called **Transformer** – overcame the limits of RNNs, being able to get much longer sequences of text as input. Transformers are based on the **attention mechanism**, enabling the model to give different weights to the inputs it receives, ‘paying more attention’ where the most relevant information is concentrated, regardless of their order in the text sequence. + +Most of the recent generative AI models – also known as Large Language Models (LLMs), since they work with textual inputs and outputs – are indeed based on this architecture. What’s interesting about these models – trained on a huge amount of unlabeled data from diverse sources like books, articles and websites – is that they can be adapted to a wide variety of tasks and generate grammatically correct text with a semblance of creativity. So, not only did they incredibly enhance the capacity of a machine to ‘understand’ an input text, but they enabled their capacity to generate an original response in human language. diff --git a/docs/gen_ai/intro_to_llm.md b/docs/gen_ai/intro_to_llm.md new file mode 100644 index 0000000..fb9cd94 --- /dev/null +++ b/docs/gen_ai/intro_to_llm.md @@ -0,0 +1,39 @@ +# Introduction to Large Language Model (LLM) + +## How do large language models work? + +Let’s have a look at how large language models work, with a focus on OpenAI GPT (Generative Pre-trained Transformer) models. + +- **Tokenizer, text to numbers**: Large Language Models receive a text as input and generate a text as output. However, being statistical models, they work much better with numbers than text sequences. That’s why every input to the model is processed by a tokenizer, before being used by the core model. A token is a chunk of text – consisting of a variable number of characters, so the tokenizer's main task is splitting the input into an array of tokens. Then, each token is mapped with a token index, which is the integer encoding of the original text chunk. + +- **Predicting output tokens**: Given n tokens as input (with max n varying from one model to another), the model is able to predict one token as output. This token is then incorporated into the input of the next iteration, in an expanding window pattern, enabling a better user experience of getting one (or multiple) sentence as an answer. This explains why, if you ever played with ChatGPT, you might have noticed that sometimes it looks like it stops in the middle of a sentence. + +- **Selection process, probability distribution**: The output token is chosen by the model according to its probability of occurring after the current text sequence. This is because the model predicts a probability distribution over all possible ‘next tokens’, calculated based on its training. However, not always the token with the highest probability is chosen from the resulting distribution. A degree of randomness is added to this choice, in a way that the model acts in a non-deterministic fashion - we do not get the exact same output for the same input. This degree of randomness is added to simulate the process of creative thinking and it can be tuned using a model parameter called temperature. + +Learn more about LLM concepts here: + +1. [Transformer Explainer](https://poloclub.github.io/transformer-explainer/) +1. [What Is ChatGPT Doing … and Why Does It Work?](https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work/) +1. [Jay Alammar's blog](https://jalammar.github.io/) + 1. [Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention)](https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/) + 1. [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/) + 1. [The Illustrated GPT-2 (Visualizing Transformer Language Models)](https://jalammar.github.io/illustrated-gpt2/) + 1. [How GPT3 Works - Visualizations and Animations](https://jalammar.github.io/how-gpt3-works-visualizations-animations/) + 1. [Interfaces for Explaining Transformer Language Models](https://jalammar.github.io/explaining-transformers/) + +## Improving LLM results + +There are several approaches a business can use to get the results they need from an LLM. You can select different types of models with different degrees of training when deploying an LLM in production, with different levels of complexity, cost, and quality. Here are some different approaches: + +- **Prompt engineering with context**. The idea is to provide enough context when you prompt to ensure you get the responses you need. + +- **Retrieval Augmented Generation**, RAG. Your data might exist in a database or web endpoint for example, to ensure this data, or a subset of it, is included at the time of prompting, you can fetch the relevant data and make that part of the user's prompt. + +- **Fine-tuned model**. Here, you trained the model further on your own data which leads to the model being more exact and responsive to your needs but might be costly. + +```{figure} ../images/gen_ai/improve_llms.png +--- +name: improve-llms +--- +[Four Ways that Enterprises Deploy LLMs | Fiddler AI Blog](https://www.fiddler.ai/blog/four-ways-that-enterprises-deploy-llms?WT.mc_id=academic-105485-koreyst) +``` diff --git a/docs/gen_ai/local_llms.md b/docs/gen_ai/local_llms.md new file mode 100644 index 0000000..c30d7b4 --- /dev/null +++ b/docs/gen_ai/local_llms.md @@ -0,0 +1,20 @@ +# Setup Local LLMs + +1. Download and install Ollama from [here](https://ollama.com/download) +2. Run Ollama: +3. Download large language models by running `ollama pull` command in terminal, for example: + ``` + ollama pull llama3.1 + ``` +4. Run the model + ``` + ollama run llama3.1 + ``` + +## Optional: Use Open WebUI + +1. Install Open WebUI, from [here](https://docs.openwebui.com/) +2. Run Open WebUI according the installation method you chose above +3. Open the browser and navigate to `http://localhost:8080` + +![Open WebUI](../images/gen_ai/open_webui.png) diff --git a/docs/images/gen_ai/gen_ai_history.png b/docs/images/gen_ai/gen_ai_history.png new file mode 100644 index 0000000..ee8be4f Binary files /dev/null and b/docs/images/gen_ai/gen_ai_history.png differ diff --git a/docs/images/gen_ai/improve_llms.png b/docs/images/gen_ai/improve_llms.png new file mode 100644 index 0000000..6081269 Binary files /dev/null and b/docs/images/gen_ai/improve_llms.png differ diff --git a/docs/images/gen_ai/open_webui.png b/docs/images/gen_ai/open_webui.png new file mode 100644 index 0000000..d65ddf1 Binary files /dev/null and b/docs/images/gen_ai/open_webui.png differ diff --git a/docs/llm/intro_to_llm.md b/docs/llm/intro_to_llm.md deleted file mode 100644 index 605ab3e..0000000 --- a/docs/llm/intro_to_llm.md +++ /dev/null @@ -1 +0,0 @@ -# 🚧 Introduction to Large Language Model diff --git a/poetry.lock b/poetry.lock index a79d73f..b56f772 100644 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