{ "cells": [ { "cell_type": "markdown", "id": "a9fd592d-0ad0-4f1b-94f4-0e5ae9dbeec7", "metadata": { "id": "a9fd592d-0ad0-4f1b-94f4-0e5ae9dbeec7" }, "source": [ "# Fine-Tune a Causal Language Model for Dialogue Summarization" ] }, { "cell_type": "markdown", "id": "976f3255-166f-4e6f-aaf0-ef6c4703b9f7", "metadata": { "id": "976f3255-166f-4e6f-aaf0-ef6c4703b9f7" }, "source": [ "Fine-tune Meta's Llama 2 base version for enhanced topic summarization creation of mutlitple choice question (MCQ). Llama 2 is a large language model (LLM) free for research and commercial use. It is one of the top-performing open-source LLM comparable to GPT-3.5 on several benchmarks.\n", "\n", "We will explore the use of Parameter Efficient Fine-Tuning (PEFT - lora) for fine-tuning, and evaluate the resulting model using ROUGE metrics." ] }, { "cell_type": "markdown", "id": "a3b6ce61-1b18-412e-8ea9-b1aa4b6ea33d", "metadata": { "id": "a3b6ce61-1b18-412e-8ea9-b1aa4b6ea33d" }, "source": [ "## Install the pre-requisites\n", "\n", "Uncomment the following if these python packages have not been installed" ] }, { "cell_type": "code", "execution_count": null, "id": "4da7e887-227f-4cce-983a-83ab3405de65", "metadata": { "tags": [], "id": "4da7e887-227f-4cce-983a-83ab3405de65", "outputId": "0e0d4037-fecc-497f-e670-c8ec02e4ec32", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n", "Collecting datasets\n", " 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rouge_score\n", " Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for rouge_score: filename=rouge_score-0.1.2-py3-none-any.whl size=24933 sha256=97db1dbbde83d29517ebbf637d3e9665143ece23701de0adadeb3b10706c43fe\n", " Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n", "Successfully built rouge_score\n", "Installing collected packages: pyarrow, dill, rouge_score, responses, multiprocess, bitsandbytes, accelerate, datasets, peft, evaluate\n", " Attempting uninstall: pyarrow\n", " Found existing installation: pyarrow 10.0.1\n", " Uninstalling pyarrow-10.0.1:\n", " Successfully uninstalled pyarrow-10.0.1\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed accelerate-0.27.2 bitsandbytes-0.42.0 datasets-2.17.0 dill-0.3.8 evaluate-0.4.1 multiprocess-0.70.16 peft-0.8.2 pyarrow-15.0.0 responses-0.18.0 rouge_score-0.1.2\n" ] } ], "source": [ "!pip install transformers datasets accelerate sentencepiece scipy peft bitsandbytes evaluate rouge_score" ] }, { "cell_type": "markdown", "id": "a39feac3-3c0f-4057-83e2-898a0329cbee", "metadata": { "id": "a39feac3-3c0f-4057-83e2-898a0329cbee" }, "source": [ "## Request access to Llama-2 weights\n", "\n", "You need to request for access to download the Llama 2 weights. You can either do so through this [link at Meta](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) or through your huggingface account at this [link](https://huggingface.co/meta-llama/Llama-2-7b). Once your request is approved, you will receive an email from Meta with instruction to download the Llama 2 weights, or email from Hugging Face informing you access has been granted.\n", "\n", "If you download the weights from Meta directly, you need to run a conversion script to convert the weights to huggingface format for use with huggingface transformer library." ] }, { "cell_type": "code", "execution_count": null, "id": "4178ec51-ccf8-4d01-8224-e57c8b0683f1", "metadata": { "tags": [], "id": "4178ec51-ccf8-4d01-8224-e57c8b0683f1" }, "outputs": [], "source": [ "# %%bash\n", "# TRANSFORM=`python -c \"import transformers;print('/'.join(transformers.__file__.split('/')[:-1])+'/models/llama/convert_llama_weights_to_hf.py')\"`\n", "# python ${TRANSFORM} --input_dir models --model_size 7B --output_dir models_hf/7B" ] }, { "cell_type": "code", "execution_count": null, "id": "00149073-36b6-4317-818b-c8e0485cd8d2", "metadata": { "tags": [], "id": "00149073-36b6-4317-818b-c8e0485cd8d2", "outputId": "367401cb-334c-430e-efdf-d2f795f4d5ba", "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "c438af202cdf45519dc13ffe6f22037d", "99b5ebef5fe54a82b20e1f9e196111d0", "e463243dece7453da77c8775a4fe5f94", "44344f5882fd4e3aadc05cc18b15d571", "eb26fc1a3e6047078528f3f92ccd27b9", "53927a52e58544b6bdbb71382a3f0d45", "1e74458632d2490685769a7a1f46dce9", "e4686d7a955b4ad9a65b323285430838", "dc3b4d57e6544e1082ac417062306808", "de3d1e34487d4763a2b19c8f7567c8f3", "07bc154a336b402f9156c30cba0ed696", "ac978db16d5f442d8930984fa4498717", "5e0f629e3e094748b2f03675e4370989", "498c2dac23ff4355b3475328c7d77deb", "96f915a83bb843b5913508c7cf7debe4", "315c11cdbd3b4fcf93881deae18b8a4b", "8e1c860e75bd42a2ae54047ba436ea01", "97cc7d3fd82646cf8a64985c4257ec47", "84b644b814bd4638803555c93102b329", "2c7386d8b79846d8a56821837d2faa82", "bb5c7c3283de41f385d4a394da65c3d6", "84bc91a6c29a4963996a93ecf3b9bcd4", "6ebbe9df3902460686665a4a9a10bd5f", "38905af71dc2456aab7ab6298a3de610", "2e579d901ca440c38ecd38e99b0fdd5d", "6672ac14178549349bfb740c1e5fe754", "8dd8fda1fa314f048022dd17e432c59b", "45331f2eed8f45ea95f25fea8faf2446", "bd6c352f84364c14a0f9c7a81d2f2ea6", "e287c06077904002925102bdfcb9e223", "0f6031eaf46b4d24b66d94ba5e8a49c0", "43a1107155bf475cb47376268954c2ec" ] } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
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