{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "malpM0sXFn9K" }, "source": [ "Install required packages" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "id": "bvHV20Q0vGkE" }, "outputs": [], "source": [ "from IPython.display import clear_output\n", "\n", "!pip install -qqq --upgrade bitsandbytes transformers peft accelerate datasets trl flash_attn\n", "!pip install huggingface_hub\n", "!pip install python-dotenv\n", "!pip install absl-py nltk rouge_score\n", "!pip list | grep transformers.\n", "\n", "clear_output()" ] }, { "cell_type": "markdown", "metadata": { "id": "fx_7c7MJoqmW" }, "source": [ "Import packages" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "id": "Mrn1Z3usdTEG" }, "outputs": [], "source": [ "import numpy as np\n", "import torch\n", "import pandas as pd\n", "from datasets import Dataset, load_metric\n", "from peft import LoraConfig, prepare_model_for_kbit_training, PeftModel, get_peft_model\n", "from transformers import (AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, set_seed, pipeline)\n", "from trl import SFTTrainer" ] }, { "cell_type": "markdown", "metadata": { "id": "qa80IqnWJL_m" }, "source": [ "Define parameters" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "id": "Y5dL-Zn6gDk5" }, "outputs": [], "source": [ "# Name of the model to use as parent model\n", "base_model_name = \"microsoft/Phi-3-mini-4k-instruct\"\n", "\n", "# Name of the new model\n", "model_name=\"acorreal/project-management-tutor\"\n", "\n", "# Set the model configuration\n", "use_4bit = True\n", "bnb_4bit_quant_type = \"nf4\"\n", "use_double_quant = True\n", "set_seed(1234)" ] }, { "cell_type": "markdown", "metadata": { "id": "Z3pJIh4h3Usa" }, "source": [ "Connect to Huggingface Hub" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GgZlM-ov461d", "outputId": "f0fe1cf2-3627-43cd-f0c4-4f59cecff75a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n", "Token is valid (permission: write).\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ], "source": [ "from huggingface_hub import login\n", "from google.colab import userdata\n", "\n", "login(token=userdata.get('HF_TOKEN'))" ] }, { "cell_type": "markdown", "metadata": { "id": "HJjE6hP3vt_Z" }, "source": [ "Load the dataset with the instruction set" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 293 }, "id": "_Rum2HpfdTEH", "outputId": "33c2d7df-ac76-44ef-ea8b-ff8a1573dee5" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "summary": "{\n \"name\": \"df\",\n \"rows\": 1042,\n \"fields\": [\n {\n \"column\": \"question\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 807,\n \"samples\": [\n \"How do you ensure project deliverables are accepted by stakeholders?\",\n \"What aspect of word relationships do word embeddings understand better than traditional methods?\",\n \"How does understanding probability help in natural language processing?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"answer\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 843,\n \"samples\": [\n \"PRINCE2 (Projects IN Controlled Environments) is a process-based project management methodology.\",\n \"It enables developers to accurately analyze data distributions and make informed predictions based on statistical models.\",\n \"A project assumption log is a document that records all project assumptions, tracking their status and impact on the project.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"instruction\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"You are an expert in project management, tasked with helping students master project management principles, including the Unified Process. Provide guidance on planning, executing, and closing projects, covering key concepts like scope, time, cost, quality, risk management, and stakeholder engagement. Use practical examples, case studies, and interactive discussions to enhance their knowledge and application of these skills in real-world scenarios.:\\n\\n\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "df" }, "text/html": [ "\n", "
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0What is the primary focus of the book \"CogiMes...The book focuses on teaching AI model engineer...You are an expert in project management, taske...
1Who is Aria Gray, and what is her role at Nebu...Aria Gray is a fresh graduate in Computational...You are an expert in project management, taske...
2What is MLflow, and why is it important in AI ...MLflow is a platform that manages the machine ...You are an expert in project management, taske...
3What is MLOps, and how does it relate to AI de...MLOps is a practice for collaboration between ...You are an expert in project management, taske...
4What are the four main components of MLflow?The four main components of MLflow are Trackin...You are an expert in project management, taske...
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\n" ], "text/plain": [ " question \\\n", "0 What is the primary focus of the book \"CogiMes... \n", "1 Who is Aria Gray, and what is her role at Nebu... \n", "2 What is MLflow, and why is it important in AI ... \n", "3 What is MLOps, and how does it relate to AI de... \n", "4 What are the four main components of MLflow? \n", "\n", " answer \\\n", "0 The book focuses on teaching AI model engineer... \n", "1 Aria Gray is a fresh graduate in Computational... \n", "2 MLflow is a platform that manages the machine ... \n", "3 MLOps is a practice for collaboration between ... \n", "4 The four main components of MLflow are Trackin... \n", "\n", " instruction \n", "0 You are an expert in project management, taske... \n", "1 You are an expert in project management, taske... \n", "2 You are an expert in project management, taske... \n", "3 You are an expert in project management, taske... \n", "4 You are an expert in project management, taske... " ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('dataset.csv')\n", "df.columns = ['question', 'answer']\n", "df['instruction'] = \"You are an expert in project management, tasked with helping students master project management principles, including the Unified Process. Provide guidance on planning, executing, and closing projects, covering key concepts like scope, time, cost, quality, risk management, and stakeholder engagement. Use practical examples, case studies, and interactive discussions to enhance their knowledge and application of these skills in real-world scenarios.:\\n\\n\"\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qbbH23N9vXh2", "outputId": "25e80255-2d8b-4ec7-f59e-eeaebd30c844" }, "outputs": [ { "data": { "text/plain": [ "Dataset({\n", " features: ['question', 'answer', 'instruction'],\n", " num_rows: 1042\n", "})" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load the dataset\n", "dataset = Dataset.from_pandas(df)\n", "dataset" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WdlOkzJyFEKq", "outputId": "8d4d8470-791c-4352-91bc-d8d5503df484" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'question': 'Who is Aria Gray, and what is her role at NebulaTech?', 'answer': 'Aria Gray is a fresh graduate in Computational Linguistics starting her career at NebulaTech, where she explores various AI and ML concepts.', 'instruction': 'You are an expert in project management, tasked with helping students master project management principles, including the Unified Process. Provide guidance on planning, executing, and closing projects, covering key concepts like scope, time, cost, quality, risk management, and stakeholder engagement. Use practical examples, case studies, and interactive discussions to enhance their knowledge and application of these skills in real-world scenarios.:\\n\\n'}\n" ] } ], "source": [ "print(dataset[1])" ] }, { "cell_type": "markdown", "metadata": { "id": "4Yk2MqEJi81c" }, "source": [ "Load the tokenizer to prepare the dataset" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "id": "4e7dmUBujAAM" }, "outputs": [], "source": [ "tokenizer = AutoTokenizer.from_pretrained(base_model_name)\n", "tokenizer.padding_side = 'right' # to prevent warnings" ] }, { "cell_type": "markdown", "metadata": { "id": "oMkbuNypTgD9" }, "source": [ "Function to generate the suitable format for our model." ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "id": "LTQGocjjjOg7" }, "outputs": [], "source": [ "def create_message_column(row):\n", " messages = []\n", " user = {\n", " \"content\": f\"{row['instruction']}\\n Input: {row['question']}\",\n", " \"role\": \"user\"\n", " }\n", " messages.append(user)\n", " assistant = {\n", " \"content\": f\"{row['answer']}\",\n", " \"role\": \"assistant\"\n", " }\n", " messages.append(assistant)\n", " return {\"messages\": messages}\n", "\n", "def format_dataset_chatml(row):\n", " return {\"text\": tokenizer.apply_chat_template(row[\"messages\"], add_generation_prompt=False, tokenize=False)}" ] }, { "cell_type": "markdown", "metadata": { "id": "S79BrDDPTt3X" }, "source": [ "Implement the ChatML format on our dataset." ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "cfb4f33aea5e49f48dccad575d082ffc", "28441ee9b2df4bf8abacc9d166217c76", "a21bb19e666a4babae044897836728f5", "9ed6b7c7023e4493ad9745adfd3b2074", "92ddd872cc65454a8963c70873689f6f", "a7a3628339b64f8a8e4f1d795234ae5c", "b08adcf1cff94949b97263daa1057c11", "7e424eea36914ae9869782b14526eb12", "c0a958819bfb4e5d8cf1c8b0879a75ac", "11292b70dd874dc3be734701c90443c7", "cad977610daa439b94894483d7a6ac9d", "beaea4abd70b40a1ac571333b1d2002a", "ed16ef5fe8704e3d825f557917662d68", "12a46e84998d421380f2452b218e741a", "6a1db9c8da0d4590b0de75241885c454", "edfd18f8c7534a67ab7fe7ed30a2d1c8", "c4a05e7d1bea4f91b4fa4f356b6dacec", "211b9625a5574de38a5e602965070e8a", "239c40f4cf284c3e987e3327447d6817", "27b459f0310e462f92042a94576258ba", "9875ede714e34892914c24c4d1c182c8", "e1c0157d5f2b46e1bd7d70611fe67533" ] }, "id": "reLTRh8mjwN6", "outputId": "7fc786d3-2f0c-421b-8584-148e5fcc85ad" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cfb4f33aea5e49f48dccad575d082ffc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/1042 [00:00\\nYou are an expert in project management, tasked with helping students master project management principles, including the Unified Process. Provide guidance on planning, executing, and closing projects, covering key concepts like scope, time, cost, quality, risk management, and stakeholder engagement. Use practical examples, case studies, and interactive discussions to enhance their knowledge and application of these skills in real-world scenarios.:\\n\\n\\n Input: What is the primary focus of the book \"CogiMesh, Nexing, AdaptScenes, and the Unified Model Engineering Process (UMEP)\"?<|end|>\\n<|assistant|>\\nThe book focuses on teaching AI model engineering using the Unified Model Engineering Process (UMEP), a methodology designed for AI applications.<|end|>\\n<|endoftext|>'}" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_chatml[0]" ] }, { "cell_type": "markdown", "metadata": { "id": "AsQw1d5FrFgk" }, "source": [ "Print dataset" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YGvvmV_8k2-H", "outputId": "53d6ba10-3252-452b-efca-b3d559bec738" }, "outputs": [ { "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['question', 'answer', 'instruction', 'messages', 'text'],\n", " num_rows: 989\n", " })\n", " test: Dataset({\n", " features: ['question', 'answer', 'instruction', 'messages', 'text'],\n", " num_rows: 53\n", " })\n", "})" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_chatml = dataset_chatml.train_test_split(test_size=0.05, seed=1234)\n", "dataset_chatml" ] }, { "cell_type": "markdown", "metadata": { "id": "RJivw-mLwyDI" }, "source": [ "Recognize GPU" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "id": "fc_LpaoqlVhi" }, "outputs": [], "source": [ "compute_dtype = torch.bfloat16\n", "attn_implementation = 'flash_attention_2'" ] }, { "cell_type": "markdown", "metadata": { "id": "uR17n-POlq7C" }, "source": [ "Load the tokenizer and model to finetune" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "0f541f0c3a8c44d89c4c4444e6b86dd1", "13da589ca7a14879a164c627e5e43cbf", "8ac73d8ce9184875acf177c30b2a7d63", "96940b26278c4a0286a0b21464437330", "998753f1273448679c490b08b5551dab", "5b7e2e99128f483a9471d037eecc5259", "1b82a415d7b34da5a601646f14343be5", "26ff033fa5584cb88f93420b1b374b02", "75b7215676eb4bb2bb3d4da8a9781ed1", "61ce23452cdd4d2b90493a57d9cc13bb", "9cb1031c8b4b482c87363852642ff299" ] }, "id": "5-OL7AW-xE_r", "outputId": "129eec60-4609-4c82-b923-bf7f1b7ccd62" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0f541f0c3a8c44d89c4c4444e6b86dd1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00\n", " \n", " \n", " [31/31 07:13, Epoch 1/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation Loss
1No logNo log

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "peft_model = get_peft_model(model, peft_config)\n", "peft_model.config.use_cache = False\n", "trainer.train()\n", "trainer.save_model()" ] }, { "cell_type": "markdown", "metadata": { "id": "nnO3eM42roIF" }, "source": [ "Login in to Hugging Face" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 103, "referenced_widgets": [ "0707788838e24cfe8861e94043bc8c48", "286032cb13804f17b2a76c020f1ce6d7", "5d37df5586b34179aabe1ae158b85b14", "75248a11e7d84f5397292479cd65edc5", "64779974d8b8487caa11bb15a591548a", "b0cfb803e019420b8cc1621b362b4014", "91bdb4ac14d44a43ade1cae70671b5fa", "bc686ff706364cf29f9eb06455b09e4b", "88376a2b93b64969a441b2094c75e12b", "07997ae121d84673844d4364151c17b0", "30e0ea4ee07143e5a3f177c6ae3a8929" ] }, "id": "pIIR67O862DH", "outputId": "da3fc748-5e96-46fb-c686-0281e7e22088" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0707788838e24cfe8861e94043bc8c48", "version_major": 2, "version_minor": 0 }, "text/plain": [ "adapter_model.safetensors: 0%| | 0.00/3.15M [00:00=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\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) (2024.5.15)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n", "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n", "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n", "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n", "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2024.5.0)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.12.2)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n" ] } ], "source": [ "%pip install --upgrade transformers huggingface_hub" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QHiuGFv8A0rd", "outputId": "d74acde3-57cf-4010-b98b-54e3863818a2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n", "Token is valid (permission: write).\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in .\n", "/usr/local/lib/python3.10/dist-packages/peft/tuners/lora/bnb.py:336: UserWarning: Merge lora module to 4-bit linear may get different generations due to rounding errors.\n", " warnings.warn(\n" ] } ], "source": [ "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "from huggingface_hub import login\n", "from google.colab import userdata\n", "login(token=userdata.get('HF_TOKEN'))\n", "\n", "pm_adapter_name = \"acorreal/phi3-project-management-adapter\"\n", "pm_model_name = \"acorreal/phi3-project-management\"\n", "\n", "pm_model = AutoModelForCausalLM.from_pretrained(pm_model_name,\n", " trust_remote_code=True,\n", " device_map=\"auto\")\n", "\n", "pm_model = PeftModel.from_pretrained(model, pm_adapter_name)\n", "pm_model = pm_model.merge_and_unload()" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "id": "GiCVIrwYCbdf" }, "outputs": [], "source": [ "pm_tokenizer = AutoTokenizer.from_pretrained(pm_adapter_name)" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 71 }, "id": "S2mkb2toCkp-", "outputId": "1d83f536-ee0f-423d-d4a4-f2050e756c9a" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "CommitInfo(commit_url='https://huggingface.co/acorreal/phi3-project-management/commit/cc10becf9c9636142cc63902ac00787cf5cb131c', commit_message='Upload tokenizer', commit_description='', oid='cc10becf9c9636142cc63902ac00787cf5cb131c', pr_url=None, pr_revision=None, pr_num=None)" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pm_model.push_to_hub(pm_model_name)\n", "pm_tokenizer.push_to_hub(pm_model_name)" ] }, { "cell_type": "markdown", "metadata": { "id": "NPFsF-OrfBZc" }, "source": [ "Evaluation" ] }, { "cell_type": "markdown", "metadata": { "id": "ppxqEpgoU1hu" }, "source": [ "Retrieve the model and tokenizer from the Hub." ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "id": "bZwALfOTBAOx" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 105, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X0M3cv12_YRt", "outputId": "2da6f247-9016-4fad-bbdb-e476a1e71303" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: bitsandbytes in /usr/local/lib/python3.10/dist-packages (0.43.3)\n", "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (2.3.1+cu121)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bitsandbytes) (1.26.4)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.15.4)\n", "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (4.12.2)\n", "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (1.13.1)\n", "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.3)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (3.1.4)\n", "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2024.5.0)\n", "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n", "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n", "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n", "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (8.9.2.26)\n", "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.3.1)\n", "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.0.2.54)\n", "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (10.3.2.106)\n", "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (11.4.5.107)\n", "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.0.106)\n", "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.20.5)\n", "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (12.1.105)\n", "Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->bitsandbytes) (2.3.1)\n", "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch->bitsandbytes) (12.6.20)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->bitsandbytes) (2.1.5)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->bitsandbytes) (1.3.0)\n" ] } ], "source": [ "!pip install bitsandbytes" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oJ6pB5b9U3xt", "outputId": "17999274-f911-48e1-bb1c-7c97a774db4c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in .\n", "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n" ] } ], "source": [ "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "from huggingface_hub import login\n", "from google.colab import userdata\n", "\n", "pm_model_name = \"acorreal/phi3-project-management\"\n", "\n", "test_model = AutoModelForCausalLM.from_pretrained(pm_model_name)\n", "test_tokenizer = AutoTokenizer.from_pretrained(pm_model_name, trust_remote_code=True)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "L4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0707788838e24cfe8861e94043bc8c48": { 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