{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "wM8MRkf8Dr94" }, "source": [ "## Describe your model -> fine-tuned LLaMA 2\n", "By Matt Shumer (https://twitter.com/mattshumer_)\n", "\n", "The goal of this notebook is to experiment with a new way to make it very easy to build a task-specific model for your use-case.\n", "\n", "First, use the best GPU available (go to Runtime -> change runtime type)\n", "\n", "To create your model, just go to the first code cell, and describe the model you want to build in the prompt. Be descriptive and clear.\n", "\n", "Select a temperature (high=creative, low=precise), and the number of training examples to generate to train the model. From there, just run all the cells.\n", "\n", "You can change the model you want to fine-tune by changing `model_name` in the `Define Hyperparameters` cell." ] }, { "cell_type": "markdown", "metadata": { "id": "Way3_PuPpIuE" }, "source": [ "#Data generation step" ] }, { "cell_type": "markdown", "metadata": { "id": "lY-3DvlIpVSl" }, "source": [ "Write your prompt here. Make it as descriptive as possible!\n", "\n", "Then, choose the temperature (between 0 and 1) to use when generating data. Lower values are great for precise tasks, like writing code, whereas larger values are better for creative tasks, like writing stories.\n", "\n", "Finally, choose how many examples you want to generate. The more you generate, a) the longer it takes and b) the more expensive data generation will be. But generally, more examples will lead to a higher-quality model. 100 is usually the minimum to start." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "R7WKZyxtpUPS" }, "outputs": [], "source": [ "# prompt = \"A model that takes in a puzzle-like reasoning-heavy question in English, and responds with a well-reasoned, step-by-step thought out response in Spanish.\"\n", "prompt = \"A model that takes in question about University of South Florida, and responds with correct answer base on trustworthy prove and evidence.\"\n", "temperature = .4\n", "number_of_examples = 10" ] }, { "cell_type": "markdown", "metadata": { "id": "1snNou5PrIci" }, "source": [ "Run this to generate the dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "zuL2UaqlsmBD", "outputId": "e5949c40-14f6-44bb-d2ff-4fcecdc951f2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (0.27.10)\n", "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai) (2.31.0)\n", "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai) (4.66.1)\n", "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai) (3.8.5)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (3.2.0)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (3.4)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (2.0.4)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai) (2023.7.22)\n", "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (23.1.0)\n", "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (6.0.4)\n", "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (4.0.3)\n", "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.9.2)\n", "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.4.0)\n", "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai) (1.3.1)\n" ] } ], "source": [ "!pip install openai" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Rdsd82ngpHCG" }, "outputs": [], "source": [ "# import os\n", "# import openai\n", "# import random\n", "\n", "# openai.api_key = \"\"\n", "\n", "# def generate_example(prompt, prev_examples, temperature=.5):\n", "# messages=[\n", "# {\n", "# \"role\": \"system\",\n", "# \"content\": f\"You are generating data which will be used to train a machine learning model.\\n\\nYou will be given a high-level description of the model we want to train, and from that, you will generate data samples, each with a prompt/response pair.\\n\\nYou will do so in this format:\\n```\\nprompt\\n-----------\\n$prompt_goes_here\\n-----------\\n\\nresponse\\n-----------\\n$response_goes_here\\n-----------\\n```\\n\\nOnly one prompt/response pair should be generated per turn.\\n\\nFor each turn, make the example slightly more complex than the last, while ensuring diversity.\\n\\nMake sure your samples are unique and diverse, yet high-quality and complex enough to train a well-performing model.\\n\\nHere is the type of model we want to train:\\n`{prompt}`\"\n", "# }\n", "# ]\n", "\n", "# if len(prev_examples) > 0:\n", "# if len(prev_examples) > 10:\n", "# prev_examples = random.sample(prev_examples, 10)\n", "# for example in prev_examples:\n", "# messages.append({\n", "# \"role\": \"assistant\",\n", "# \"content\": example\n", "# })\n", "\n", "# response = openai.ChatCompletion.create(\n", "# model=\"gpt-3.5-turbo-16k\",\n", "# messages=messages,\n", "# temperature=temperature,\n", "# max_tokens=350,\n", "# )\n", "\n", "# return response.choices[0].message['content']\n", "\n", "# # Generate examples\n", "# prev_examples = []\n", "# for i in range(number_of_examples):\n", "# print(f'Generating example {i}')\n", "# example = generate_example(prompt, prev_examples, temperature)\n", "# prev_examples.append(example)\n", "\n", "# print(prev_examples)" ] }, { "cell_type": "markdown", "metadata": { "id": "KC6iJzXjugJ-" }, "source": [ "We also need to generate a system message." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xMcfhW6Guh2E", "outputId": "a5ba11ee-6ced-4f47-bbaa-5d68d7047d7b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The system message is: `Given a question about the University of South Florida, provide a correct answer based on trustworthy proof and evidence.`. Feel free to re-run this cell if you want a better result.\n" ] } ], "source": [ "import openai\n", "\n", "\n", "def generate_system_message(prompt):\n", "\n", " response = openai.ChatCompletion.create(\n", " model=\"gpt-4\",\n", " messages=[\n", " {\n", " \"role\": \"system\",\n", " \"content\": \"You will be given a high-level description of the model we are training, and from that, you will generate a simple system prompt for that model to use. Remember, you are not generating the system message for data generation -- you are generating the system message to use for inference. A good format to follow is `Given $INPUT_DATA, you will $WHAT_THE_MODEL_SHOULD_DO.`.\\n\\nMake it as concise as possible. Include nothing but the system prompt in your response.\\n\\nFor example, never write: `\\\"$SYSTEM_PROMPT_HERE\\\"`.\\n\\nIt should be like: `$SYSTEM_PROMPT_HERE`.\"\n", " },\n", " {\n", " \"role\": \"user\",\n", " \"content\": prompt.strip(),\n", " }\n", " ],\n", " temperature=temperature,\n", " max_tokens=500,\n", " )\n", "\n", " return response.choices[0].message['content']\n", "\n", "# system_message = generate_system_message(prompt)\n", "system_message = \"Given a question about the University of South Florida, provide a correct answer based on trustworthy proof and evidence.\"\n", "\n", "print(f'The system message is: `{system_message}`. Feel free to re-run this cell if you want a better result.')" ] }, { "cell_type": "markdown", "metadata": { "id": "G6BqZ-hjseBF" }, "source": [ "Now let's put our examples into a dataframe and turn them into a final pair of datasets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7CEdkYeRsdmB" }, "outputs": [], "source": [ "# import pandas as pd\n", "\n", "# # Initialize lists to store prompts and responses\n", "# prompts = []\n", "# responses = []\n", "\n", "# # Parse out prompts and responses from examples\n", "# for example in prev_examples:\n", "# try:\n", "# split_example = example.split('-----------')\n", "# prompts.append(split_example[1].strip())\n", "# responses.append(split_example[3].strip())\n", "# except:\n", "# pass\n", "\n", "# # Create a DataFrame\n", "# df = pd.DataFrame({\n", "# 'prompt': prompts,\n", "# 'response': responses\n", "# })\n", "\n", "# # Remove duplicates\n", "# df = df.drop_duplicates()\n", "\n", "# print('There are ' + str(len(df)) + ' successfully-generated examples. Here are the first few:')\n", "\n", "# df.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "A-8dt5qqtpgM" }, "source": [ "Split into train and test sets." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "GFPEn1omtrXM" }, "outputs": [], "source": [ "# # Split the data into train and test sets, with 90% in the train set\n", "# train_df = df.sample(frac=0.9, random_state=42)\n", "# test_df = df.drop(train_df.index)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IdTBJPivCwNP" }, "outputs": [], "source": [ "# # Save the dataframes to .jsonl files\n", "# train_df.to_json('train.jsonl', orient='records', lines=True)\n", "# test_df.to_json('test.jsonl', orient='records', lines=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SNDdOvqYCWLd", "outputId": "d97cdd4f-77ed-42db-e0e3-50f7a23dfea3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'prompt': 'What are the admission requirements for the University of South Florida?', 'response': \"The University of South Florida has specific admission requirements for undergraduate and graduate programs. Here are the general guidelines:\\n\\nUndergraduate Admission:\\n- High School Diploma or Equivalent: Applicants must have a high school diploma or its equivalent, such as a GED.\\n- GPA: USF considers the applicant's high school GPA. The average GPA of admitted students varies by program and can range from 3.0 to 4.5 on a weighted scale.\\n- Standardized Tests: USF requires either the SAT or ACT scores. The average scores of admitted students also vary by program.\\n- Course Requirements: USF has specific course requirements, including English, mathematics, science, social sciences, and foreign language. These requirements ensure that students have a strong foundation in key subject areas.\\n\\nGraduate Admission:\\n- Bachelor's Degree: Applicants must hold a bachelor's degree from an accredited institution or its equivalent.\\n- GPA: USF considers the applicant's undergraduate GPA. The minimum GPA requirement varies by program, but most programs require a GPA of 3.0 or higher.\\n- Standardized Tests: Some graduate programs may require standardized test scores, such as the GRE or GMAT. The specific test requirements vary by program.\\n- Letters of Recommendation: Many graduate programs require letters of recommendation from professors or professionals who can speak to the applicant's academic abilities and potential for success in the program.\\n- Statement of Purpose: Applicants may be required to submit a statement of purpose or personal statement, outlining their academic and career goals.\\n\\nIt's important to note that admission requirements can vary by program and may be subject to change. Prospective students are encouraged to visit the University of South\"}\n", "{'prompt': 'What programs does the University of South Florida offer?', 'response': 'The University of South Florida offers a wide range of programs across various disciplines. It has 14 colleges and schools, including the College of Arts and Sciences, College of Engineering, College of Business, College of Education, and College of Medicine, among others. These colleges offer undergraduate, graduate, and professional degree programs in fields such as arts, sciences, engineering, business, education, health sciences, and more. The university is known for its strong programs in areas like marine science, nursing, engineering, and public health.'}\n", "{'prompt': 'What resources are available for students at the University of South Florida?', 'response': \"The University of South Florida provides a wide range of resources and support services to its students. Here are some of the key resources available:\\n\\n1. Academic Support: USF offers academic advising, tutoring services, and study skills workshops to help students succeed academically. The university also has libraries and research centers that provide access to a vast collection of resources, including books, journals, and online databases.\\n\\n2. Career Services: USF has a dedicated career center that offers career counseling, job search assistance, resume and interview preparation, and networking opportunities. The university also hosts career fairs and employer events to connect students with potential employers.\\n\\n3. Student Organizations: USF has a vibrant campus life with over 600 student organizations. These organizations cover a wide range of interests, including academic, cultural, recreational, and professional areas. Joining a student organization is a great way for students to get involved, make friends, and develop leadership skills.\\n\\n4. Health and Wellness: USF provides comprehensive health and wellness services to its students. This includes a student health center, counseling services, wellness programs, and recreational facilities. The university promotes a healthy lifestyle and offers resources to support students' physical and mental well-being.\\n\\n5. Diversity and Inclusion: USF is committed to fostering a diverse and inclusive campus community. The university has offices and programs that promote diversity, equity, and inclusion, including multicultural affairs, LGBTQ+ resources, and international student services.\\n\\nThese resources and support services contribute to a positive and enriching student experience at the University of South Florida. The university strives to provide a supportive environment where students can thrive academically, personally,\"}\n", "{'prompt': 'What is the location of the University of South Florida?', 'response': 'The University of South Florida is located in Tampa, Florida. It is situated on a sprawling campus that spans over 1,900 acres. The campus is known for its beautiful green spaces, modern architecture, and state-of-the-art facilities.'}\n", "{'prompt': \"What is the University of South Florida's commitment to sustainability?\", 'response': \"The University of South Florida is committed to sustainability and has implemented various initiatives to promote environmental stewardship and reduce its carbon footprint. Here are some examples of USF's commitment to sustainability:\\n\\n1. Green Building Practices: USF follows green building practices in the construction and renovation of its facilities. The university incorporates energy-efficient designs, uses sustainable materials, and implements water conservation measures. Several buildings on campus have achieved LEED (Leadership in Energy and Environmental Design) certification.\\n\\n2. Renewable Energy: USF is actively working towards increasing its use of renewable energy sources. The university has installed solar panels on campus buildings, including the Marshall Student Center, to generate clean energy. USF also participates in initiatives to promote energy conservation and efficiency.\\n\\n3. Recycling and Waste Reduction: USF has implemented recycling programs across campus to minimize waste and promote recycling. The university encourages students, faculty, and staff to recycle paper, plastic, glass, and other materials. USF also promotes composting and has implemented initiatives to reduce single-use plastics on campus.\\n\\n4. Sustainable Transportation: USF encourages sustainable transportation options to reduce carbon emissions. The university provides bike racks and bike-sharing programs to promote cycling as an alternative mode of transportation. USF also offers electric vehicle charging stations on campus.\\n\\n5. Sustainability Education and Research: USF integrates sustainability into its curriculum and offers courses and programs focused on environmental studies, renewable energy, and sustainable development. The university also supports research initiatives related to sustainability and collaborates with local and global partners to address environmental challenges.\\n\\nThese initiatives reflect USF's commitment to creating a sustainable campus community and preparing students to become environmentally responsible citizens.\"}\n", "{'prompt': 'Can you tell me about the history of the University of South Florida?', 'response': 'Certainly! The University of South Florida was founded in 1956 and is a public research university. It was established to serve the growing population of the Tampa Bay area and has since become one of the largest universities in the state of Florida. Over the years, USF has grown in both size and reputation, earning recognition for its research contributions and academic programs. The university has a diverse student body and faculty, with a focus on promoting inclusivity and innovation. USF has played a significant role in the development of the region and continues to contribute to the advancement of knowledge and the community.'}\n", "{'prompt': \"What is the University of South Florida's commitment to diversity and inclusion?\", 'response': \"The University of South Florida is committed to fostering a diverse and inclusive campus community. Here are some ways in which USF promotes diversity and inclusion:\\n\\n1. Office of Diversity, Inclusion, and Equal Opportunity: USF has an Office of Diversity, Inclusion, and Equal Opportunity that works to promote diversity and ensure equal opportunity for all members of the university community. The office provides resources, training, and support to foster a welcoming and inclusive environment.\\n\\n2. Multicultural Affairs: USF's Office of Multicultural Affairs supports and advocates for underrepresented students. The office offers programs, services, and resources to promote cultural awareness, inclusivity, and social justice. It also organizes events and activities that celebrate diversity and promote cross-cultural understanding.\\n\\n3. LGBTQ+ Resources: USF has resources and support services for LGBTQ+ students, faculty, and staff. The university's LGBTQ+ Resource Center provides a safe and inclusive space, offers educational programs, and advocates for LGBTQ+ rights and equality.\\n\\n4. International Student Services: USF is home to a diverse international student community. The university's Office of International Services provides support and resources to international students, including immigration advising, cultural programs, and assistance with integration into campus life.\\n\\n5. Diversity in Curriculum: USF promotes diversity in its curriculum by offering courses that explore issues of race, ethnicity, gender, sexuality, and other aspects of identity. The university encourages faculty to incorporate diverse perspectives and voices into their teaching and research.\\n\\nThese are just a few examples of USF's commitment to diversity and inclusion. The university recognizes that a diverse and inclusive campus community enriches the educational experience and prepares\"}\n", "{'prompt': 'What are some research initiatives at the University of South Florida?', 'response': \"The University of South Florida is actively involved in a wide range of research initiatives across various disciplines. Here are a few examples:\\n\\n1. Health Sciences: USF is a leader in health sciences research, with a focus on areas such as cancer, cardiovascular disease, neurosciences, and public health. The university collaborates with renowned institutions and organizations to advance medical knowledge and improve patient care.\\n\\n2. Sustainability and Environmental Sciences: USF is committed to sustainability and conducts research in areas such as renewable energy, climate change, water resources, and environmental conservation. The university's researchers work towards finding innovative solutions to global environmental challenges.\\n\\n3. Marine Science: USF is home to the College of Marine Science, which conducts cutting-edge research in oceanography, marine biology, and coastal sciences. The university's researchers study marine ecosystems, climate change impacts on oceans, and sustainable coastal development.\\n\\n4. Engineering and Technology: USF's College of Engineering is involved in research initiatives related to robotics, artificial intelligence, cybersecurity, and advanced materials. The university collaborates with industry partners to develop innovative technologies and solutions.\\n\\nThese are just a few examples of the research initiatives at the University of South Florida. The university's commitment to research and innovation ensures that its students and faculty are at the forefront of knowledge creation and discovery.\"}\n", "{'prompt': 'What are some notable achievements of the University of South Florida?', 'response': 'The University of South Florida has achieved numerous notable accomplishments throughout its history. Here are a few examples:\\n\\n1. Research Excellence: USF is classified as a \"Preeminent State Research University\" by the Florida Board of Governors, highlighting its commitment to research and innovation. The university is known for its contributions in areas such as health sciences, marine science, sustainability, and engineering.\\n\\n2. Carnegie Classification: USF has been recognized as an R1 research university by the Carnegie Classification of Institutions of Higher Education. This classification is reserved for institutions with the highest level of research activity.\\n\\n3. National Rankings: USF consistently ranks among the top universities in the nation. It has been recognized by prestigious publications such as U.S. News & World Report, Times Higher Education, and Forbes.\\n\\n4. Community Engagement: USF is deeply involved in the local community and has been recognized for its commitment to public service. The university has received the Community Engagement Classification from the Carnegie Foundation for the Advancement of Teaching.\\n\\nThese achievements reflect the dedication and excellence of the faculty, staff, and students at the University of South Florida.'}\n" ] } ], "source": [ "import json\n", "\n", "jsonl_file_path = \"train.jsonl\"\n", "\n", "# Open the JSONL file\n", "with open(jsonl_file_path, \"r\") as jsonl_file:\n", " for line in jsonl_file:\n", " # Remove leading/trailing whitespace and newline characters\n", " line = line.strip()\n", "\n", " # Parse the line as a JSON object\n", " try:\n", " json_object = json.loads(line)\n", " # Process the JSON object as needed\n", " print(json_object)\n", " except json.JSONDecodeError as e:\n", " print(f\"Error decoding JSON: {e}\")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "AbrFgrhG_xYi" }, "source": [ "# Install necessary libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lPG7wEPetFx2", "outputId": "75a97a42-4d1a-4589-d780-95c07d0f767a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/244.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.6/244.2 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.9/72.9 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m19.9 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PeftModel\n", "from trl import SFTTrainer" ] }, { "cell_type": "markdown", "metadata": { "id": "moVo0led-6tu" }, "source": [ "# Define Hyperparameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true, "base_uri": "https://localhost:8080/" }, "id": "PCbe-jYxKIpv", "outputId": "57c5e663-09dc-4f7e-a294-c7943f908d16" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 160, "referenced_widgets": [ "148c359ef205419ebb13032452aba71a", "89632697d9fb47c6beb01190e6bbc418", "b933028c34dc43d7aff5ddc567e7c776", "426d49ad655740e9bcdb6c27f6902d25", "be5d5346dd5d455eaefa004ac8dd0d37", "7f0998cd48a54dcdaf79ae7ba8a2df99", "9a2759acebb2453cbf409ea781acde67", "795ab9aef3ea4ebd8eac74745ff437ac", "cd2f4582295f446a86d4fc61fbfae5c2", "a2f2898c927b4d82ae7b78819eb79ce3", "e350bad7d4ef492f9006df5d23f507eb", "4e044017c5174239b61f038027e78cd4", "102c2bef6480489cbd4e828e9aa4610e", "edbffc39ad3241a6957e6f64346d8dc7" ] }, "id": "EYK7F8OWKI0O", "outputId": "5e83c0e6-5c15-41d3-b85b-ddc9396476bb" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "148c359ef205419ebb13032452aba71a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='
\n", " \n", " \n", " [3/3 00:02, Epoch 1/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining LossValidation Loss

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n", " warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[INST] <>\n", "Given a question about the University of South Florida, provide a correct answer based on trustworthy proof and evidence.\n", "<>\n", "\n", "Write a function that reverses a string. [/INST] Sure! Here is a function in Python that reverses a string:\n", "```\n", "def reverse_string(s):\n", " return s[::-1]\n", "```\n", "Explanation:\n", "\n", "* The syntax `[::-1]` is called \"negative indexing\" and it returns the characters of the string in reverse order.\n", "* For example, if we call the function with the string \"hello\", it will return the string \"lohel\".\n", "\n", "Testing the function:\n", "```\n", "print(reverse_string(\"hello\")) # should print \"lohel\"\n", "print(reverse_string(\"world\")) # should print \"dlrow\"\n", "```\n", "Note: This function only works for strings,\n" ] } ], "source": [ "# Load datasets\n", "train_dataset = load_dataset('json', data_files='/content/train.jsonl', split=\"train\")\n", "valid_dataset = load_dataset('json', data_files='/content/test.jsonl', split=\"train\")\n", "\n", "# Preprocess datasets\n", "train_dataset_mapped = train_dataset.map(lambda examples: {'text': [f'[INST] <>\\n{system_message.strip()}\\n<>\\n\\n' + prompt + ' [/INST] ' + response for prompt, response in zip(examples['prompt'], examples['response'])]}, batched=True)\n", "valid_dataset_mapped = valid_dataset.map(lambda examples: {'text': [f'[INST] <>\\n{system_message.strip()}\\n<>\\n\\n' + prompt + ' [/INST] ' + response for prompt, response in zip(examples['prompt'], examples['response'])]}, batched=True)\n", "\n", "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=use_4bit,\n", " bnb_4bit_quant_type=bnb_4bit_quant_type,\n", " bnb_4bit_compute_dtype=compute_dtype,\n", " bnb_4bit_use_double_quant=use_nested_quant,\n", ")\n", "model = AutoModelForCausalLM.from_pretrained(\n", " model_name,\n", " quantization_config=bnb_config,\n", " device_map=device_map\n", ")\n", "model.config.use_cache = False\n", "model.config.pretraining_tp = 1\n", "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n", "tokenizer.pad_token = tokenizer.eos_token\n", "tokenizer.padding_side = \"right\"\n", "peft_config = LoraConfig(\n", " lora_alpha=lora_alpha,\n", " lora_dropout=lora_dropout,\n", " r=lora_r,\n", " bias=\"none\",\n", " task_type=\"CAUSAL_LM\",\n", ")\n", "# Set training parameters\n", "training_arguments = TrainingArguments(\n", " output_dir=output_dir,\n", " num_train_epochs=num_train_epochs,\n", " per_device_train_batch_size=per_device_train_batch_size,\n", " gradient_accumulation_steps=gradient_accumulation_steps,\n", " optim=optim,\n", " save_steps=save_steps,\n", " logging_steps=logging_steps,\n", " learning_rate=learning_rate,\n", " weight_decay=weight_decay,\n", " fp16=fp16,\n", " bf16=bf16,\n", " max_grad_norm=max_grad_norm,\n", " max_steps=max_steps,\n", " warmup_ratio=warmup_ratio,\n", " group_by_length=group_by_length,\n", " lr_scheduler_type=lr_scheduler_type,\n", " report_to=\"all\",\n", " evaluation_strategy=\"steps\",\n", " eval_steps=5 # Evaluate every 20 steps\n", ")\n", "# Set supervised fine-tuning parameters\n", "trainer = SFTTrainer(\n", " model=model,\n", " train_dataset=train_dataset_mapped,\n", " eval_dataset=valid_dataset_mapped, # Pass validation dataset here\n", " peft_config=peft_config,\n", " dataset_text_field=\"text\",\n", " max_seq_length=max_seq_length,\n", " tokenizer=tokenizer,\n", " args=training_arguments,\n", " packing=packing,\n", ")\n", "trainer.train()\n", "trainer.model.save_pretrained(new_model)\n", "\n", "# Cell 4: Test the model\n", "logging.set_verbosity(logging.CRITICAL)\n", "prompt = f\"[INST] <>\\n{system_message}\\n<>\\n\\nWrite a function that reverses a string. [/INST]\" # replace the command here with something relevant to your task\n", "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n", "result = pipe(prompt)\n", "print(result[0]['generated_text'])" ] }, { "cell_type": "markdown", "metadata": { "id": "F6fux9om_c4-" }, "source": [ "#Run Inference" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7hxQ_Ero2IJe", "outputId": "f93a4274-1084-41ab-9b14-0ae5b87011df" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Sure! Here's an example of a function that reverses a string in JavaScript:\n", "```\n", "function reverseString(str) {\n", " return str.split(\"\").reverse().join(\"\");\n", "}\n", "\n", "// Example usage:\n", "const originalString = \"hello world\";\n", "const reversedString = reverseString(originalString);\n", "console.log(reversedString); // Output: \"world hello\"\n", "```\n", "This function works by splitting the input string into an\n" ] } ], "source": [ "from transformers import pipeline\n", "\n", "prompt = f\"[INST] <>\\n{system_message}\\n<>\\n\\nWrite a function that reverses a string. [/INST]\" # replace the command here with something relevant to your task\n", "num_new_tokens = 100 # change to the number of new tokens you want to generate\n", "\n", "# Count the number of tokens in the prompt\n", "num_prompt_tokens = len(tokenizer(prompt)['input_ids'])\n", "\n", "# Calculate the maximum length for the generation\n", "max_length = num_prompt_tokens + num_new_tokens\n", "\n", "gen = pipeline('text-generation', model=model, tokenizer=tokenizer, max_length=max_length)\n", "result = gen(prompt)\n", "print(result[0]['generated_text'].replace(prompt, ''))" ] }, { "cell_type": "markdown", "metadata": { "id": "Ko6UkINu_qSx" }, "source": [ "#Merge the model and store in Google Drive" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 242 }, "id": "AgKCL7fTyp9u", "outputId": "f6780e7e-34b5-41d1-cceb-74e13adb19e2" }, "outputs": [ { "ename": "NameError", "evalue": "ignored", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m# Reload model in FP16 and merge it with LoRA weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m base_model = AutoModelForCausalLM.from_pretrained(\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mlow_cpu_mem_usage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'AutoModelForCausalLM' is not defined" ] } ], "source": [ "# Merge and save the fine-tuned model\n", "# from google.colab import drive\n", "# drive.mount('/content/drive')\n", "\n", "model_path = \"/content/drive/MyDrive/llama-2-7b-custom\" # change to your preferred path\n", "\n", "# Reload model in FP16 and merge it with LoRA weights\n", "base_model = AutoModelForCausalLM.from_pretrained(\n", " model_name,\n", " low_cpu_mem_usage=True,\n", " return_dict=True,\n", " torch_dtype=torch.float16,\n", " device_map=device_map,\n", ")\n", "model = PeftModel.from_pretrained(base_model, new_model)\n", "model = model.merge_and_unload()\n", "\n", "# Reload tokenizer to save it\n", "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n", "tokenizer.pad_token = tokenizer.eos_token\n", "tokenizer.padding_side = \"right\"\n", "\n", "# Save the merged model\n", "model.save_pretrained(model_path)\n", "tokenizer.save_pretrained(model_path)" ] }, { "cell_type": "markdown", "metadata": { "id": "do-dFdE5zWGO" }, "source": [ "# Load a fine-tuned model from Drive and run inference" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "hVKQ8Uu0Y8xR", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "55d76ff4-db79-435d-8b13-eb9f99058390" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.32.1)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.15.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.16.4)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n", "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n", "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.3.3)\n", "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n", "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.7.1)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n" ] } ], "source": [ "!pip install transformers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xg6nHPsLzMw-" }, "outputs": [], "source": [ "from google.colab import drive\n", "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "\n", "# drive.mount('/content/drive')\n", "\n", "model_path = \"/content/drive/MyDrive/llama-2-7b-custom\" # change to the path where your model is saved\n", "\n", "model = AutoModelForCausalLM.from_pretrained(model_path)\n", "tokenizer = AutoTokenizer.from_pretrained(model_path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fBK2aE2KzZ05" }, "outputs": [], "source": [ "from transformers import pipeline\n", "\n", "prompt = \"Tell me about Computer Science at USF? How to apply? \" # change to your desired prompt\n", "gen = pipeline('text-generation', model=model, tokenizer=tokenizer)\n", "result = gen(prompt)\n", "print(result[0]['generated_text'])" ] } ], "metadata": { "accelerator": "GPU", "colab": { "machine_shape": "hm", "provenance": [], "gpuType": "V100" }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "00511ee9b091469b96fe0af34f72a0fb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, 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