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llama-train.py
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| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "view-in-github", | |
| "colab_type": "text" | |
| }, | |
| "source": [ | |
| "<a href=\"https://colab.research.google.com/gist/ashwin-vx/3979356ccc8d01ba122e784469255118/llama-train-py.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
| ] | |
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| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "Q04TYypUs6Ug" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import torch # Import the pytorch library\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Lets take a quick look at what we are working with." | |
| ], | |
| "metadata": { | |
| "id": "Io5Mgt2YzhKS" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "gpu_info = !nvidia-smi\n", | |
| "gpu_info = '\\n'.join(gpu_info)\n", | |
| "if gpu_info.find('failed') >= 0:\n", | |
| " print('Not connected to a GPU')\n", | |
| "else:\n", | |
| " print(gpu_info)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "Cj_ciIUG8EHj", | |
| "outputId": "b9a5814d-5d67-42df-b2cf-bc1d16f2e2b7", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Wed May 15 23:45:20 2024 \n", | |
| "+---------------------------------------------------------------------------------------+\n", | |
| "| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n", | |
| "|-----------------------------------------+----------------------+----------------------+\n", | |
| "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", | |
| "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", | |
| "| | | MIG M. |\n", | |
| "|=========================================+======================+======================|\n", | |
| "| 0 NVIDIA L4 Off | 00000000:00:03.0 Off | 0 |\n", | |
| "| N/A 39C P8 12W / 72W | 1MiB / 23034MiB | 0% Default |\n", | |
| "| | | N/A |\n", | |
| "+-----------------------------------------+----------------------+----------------------+\n", | |
| " \n", | |
| "+---------------------------------------------------------------------------------------+\n", | |
| "| Processes: |\n", | |
| "| GPU GI CI PID Type Process name GPU Memory |\n", | |
| "| ID ID Usage |\n", | |
| "|=======================================================================================|\n", | |
| "| No running processes found |\n", | |
| "+---------------------------------------------------------------------------------------+\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "major_version, minor_version = torch.cuda.get_device_capability()\n", | |
| "print(major_version)\n", | |
| "print(minor_version)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "_wvCm8OXevfx", | |
| "outputId": "7a1c2002-9bf1-4345-b434-c2ba0841f14f", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "8\n", | |
| "9\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\" #Here we are installing Unsloth" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "QCLbn2-ShHT-", | |
| "outputId": "ca872180-8f6a-4919-9b0a-ee1c84e9c80a", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Collecting unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git\n", | |
| " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-install-6m6xapob/unsloth_ca727e3a051f4a33b681300bddf99d9c\n", | |
| " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-install-6m6xapob/unsloth_ca727e3a051f4a33b681300bddf99d9c\n", | |
| " Resolved https://github.com/unslothai/unsloth.git to commit 47ffd39abd02338e8a5f226d0f529347fb7e5f89\n", | |
| " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", | |
| " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", | |
| " Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n", | |
| " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", | |
| "Collecting tyro (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n", | |
| " Downloading tyro-0.8.4-py3-none-any.whl (102 kB)\n", | |
| "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m102.4/102.4 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
| "\u001b[?25hRequirement already satisfied: transformers>=4.38.2 in /usr/local/lib/python3.10/dist-packages (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git) (4.40.2)\n", | |
| "Collecting datasets>=2.16.0 (from unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git)\n", | |
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| "Building wheels for collected packages: unsloth\n", | |
| " Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for unsloth: filename=unsloth-2024.5-py3-none-any.whl size=104607 sha256=21ac3081fbecaf15fd2b2e57be83f303893b99ae64e5c114a4b8c2b707de6cbb\n", | |
| " Stored in directory: /tmp/pip-ephem-wheel-cache-o2wjs10z/wheels/ed/d4/e9/76fb290ee3df0a5fc21ce5c2c788e29e9607a2353d8342fd0d\n", | |
| "Successfully built unsloth\n", | |
| "Installing collected packages: xxhash, unsloth, shtab, dill, multiprocess, huggingface-hub, tyro, datasets\n", | |
| " Attempting uninstall: huggingface-hub\n", | |
| " Found existing installation: huggingface-hub 0.20.3\n", | |
| " Uninstalling huggingface-hub-0.20.3:\n", | |
| " Successfully uninstalled huggingface-hub-0.20.3\n", | |
| "Successfully installed datasets-2.19.1 dill-0.3.8 huggingface-hub-0.23.0 multiprocess-0.70.16 shtab-1.7.1 tyro-0.8.4 unsloth-2024.5 xxhash-3.4.1\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#Lets install our dependencies based on the GPU we are working with\n", | |
| "if major_version >= 8:\n", | |
| " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", | |
| " !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n", | |
| "else:\n", | |
| " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", | |
| " !pip install --no-deps xformers trl peft accelerate bitsandbytes\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "ZMfdBMeuhUca", | |
| "outputId": "116f3a2e-1ba8-4647-a5f0-fdf3c9995bea", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
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| "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
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| "\u001b[?25hBuilding wheels for collected packages: flash-attn\n", | |
| " Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n", | |
| " Created wheel for flash-attn: filename=flash_attn-2.5.8-cp310-cp310-linux_x86_64.whl size=120853537 sha256=53979129f883680327bf5d13027cd014e2d054f4fb5b8856916686ae315e57d6\n", | |
| " Stored in directory: /root/.cache/pip/wheels/9b/5b/2b/dea8af4e954161c49ef1941938afcd91bb93689371ed12a226\n", | |
| "Successfully built flash-attn\n", | |
| "Installing collected packages: ninja, bitsandbytes, xformers, trl, peft, flash-attn, einops, accelerate\n", | |
| "Successfully installed accelerate-0.30.1 bitsandbytes-0.43.1 einops-0.8.0 flash-attn-2.5.8 ninja-1.11.1.1 peft-0.10.0 trl-0.8.6 xformers-0.0.26.post1\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#Get the tokenizer based on the model we are going to finetune.\n", | |
| "\n", | |
| "from transformers import AutoTokenizer\n", | |
| "\n", | |
| "tokenizer = AutoTokenizer.from_pretrained(\n", | |
| " \"unsloth/llama-3-8b-bnb-4bit\"\n", | |
| ")\n", | |
| "\n", | |
| "#load the dataset that we are going to use to finetune (publicly available on HuggingFace)\n", | |
| "from datasets import load_dataset\n", | |
| "dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\")\n", | |
| "\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "id": "H41cEcn3Irl1", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 261, | |
| "referenced_widgets": [ | |
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| }, | |
| "outputId": "e2ccccbf-e7a5-4065-9cf7-9336e8a5828b", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", | |
| " warnings.warn(\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| "metadata": {} | |
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| "metadata": {} | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| } | |
| }, | |
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| }, | |
| { | |
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| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Generating train split: 0%| | 0/51760 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| "model_id": "01de519dbb5942f29818c33bd5e3b121" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(dataset[-1]) # Lets just havea quick look at the data" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "EBH6bC1g6Ubo", | |
| "outputId": "e69ef778-0584-4ceb-c179-2645fe037078" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "{'output': 'The statement made in the text is a fact.', 'input': 'Text: The sky was very cloudy today.', 'instruction': \"Given a piece of text, you need to output whether the statements made in the text are opinions or facts. An opinion is defined as a statement that cannot be proven true or false and is usually based on someone's beliefs. A fact is defined as a statement that can be proven true or false and is not based on someone's beliefs.\"}\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#Based on this we create a prompt\n", | |
| "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", | |
| "\n", | |
| "### Instruction:\n", | |
| "{}\n", | |
| "\n", | |
| "### Input:\n", | |
| "{}\n", | |
| "\n", | |
| "### Response:\n", | |
| "{}\"\"\"\n", | |
| "EOS_TOKEN = tokenizer.eos_token\n", | |
| "\n", | |
| "#This function takes the data and will covert it to a prompt as specified above\n", | |
| "def formatting_prompts_func(examples):\n", | |
| " instructions = examples[\"instruction\"]\n", | |
| " inputs = examples[\"input\"]\n", | |
| " outputs = examples[\"output\"]\n", | |
| " texts = []\n", | |
| " for instruction, input, output in zip(instructions, inputs, outputs):\n", | |
| " # Must add EOS_TOKEN, otherwise your generation will go on forever!\n", | |
| " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", | |
| " texts.append(text)\n", | |
| " return { \"text\" : texts, }\n", | |
| "\n", | |
| "#then we map each row of the dataset into the required prompt for our LLM\n", | |
| "dataset = dataset.map(formatting_prompts_func, batched = True,)\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 49, | |
| "referenced_widgets": [ | |
| "971c95bea1d141838e8972482b809e91", | |
| "d2ba8f0cd86048e48ffc35245d3e3e28", | |
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| "24ce5d94a85f459585bb4e3cdc169b15" | |
| ] | |
| }, | |
| "id": "3Uxx1D858Pdf", | |
| "outputId": "6e8765c2-1516-461e-ca35-ff2c2ea9c6d2", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Map: 0%| | 0/51760 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "971c95bea1d141838e8972482b809e91" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#Now lets look at the an examples in dataset and more specically the text which is the prompt to our model\n", | |
| "print(dataset[0][\"text\"])\n", | |
| "print(dataset[21][\"text\"])" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "OEZQ0yfh8fSv", | |
| "outputId": "ec0585a4-9e59-4fb3-8c3f-adaa6414be6b", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", | |
| "\n", | |
| "### Instruction:\n", | |
| "Give three tips for staying healthy.\n", | |
| "\n", | |
| "### Input:\n", | |
| "\n", | |
| "\n", | |
| "### Response:\n", | |
| "1. Eat a balanced and nutritious diet: Make sure your meals are inclusive of a variety of fruits and vegetables, lean protein, whole grains, and healthy fats. This helps to provide your body with the essential nutrients to function at its best and can help prevent chronic diseases.\n", | |
| "\n", | |
| "2. Engage in regular physical activity: Exercise is crucial for maintaining strong bones, muscles, and cardiovascular health. Aim for at least 150 minutes of moderate aerobic exercise or 75 minutes of vigorous exercise each week.\n", | |
| "\n", | |
| "3. Get enough sleep: Getting enough quality sleep is crucial for physical and mental well-being. It helps to regulate mood, improve cognitive function, and supports healthy growth and immune function. Aim for 7-9 hours of sleep each night.<|end_of_text|>\n", | |
| "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", | |
| "\n", | |
| "### Instruction:\n", | |
| "Generate a list of random words.\n", | |
| "\n", | |
| "### Input:\n", | |
| "\n", | |
| "\n", | |
| "### Response:\n", | |
| "Here is a list of ten random words: \n", | |
| "\n", | |
| "1. Quibble\n", | |
| "2. Avarice\n", | |
| "3. Embellish\n", | |
| "4. Resonance\n", | |
| "5. Mercurial\n", | |
| "6. Conflagration\n", | |
| "7. Indefatigable\n", | |
| "8. Mischievous\n", | |
| "9. Delineate\n", | |
| "10. Paragon<|end_of_text|>\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "import pandas as pd\n", | |
| "\n", | |
| "df = pd.DataFrame(dataset)\n", | |
| "\n", | |
| "# Calculate the maximum length of a specific column\n", | |
| "max_length = df[\"text\"].apply(len).max()\n", | |
| "\n", | |
| "# Print out the result\n", | |
| "print(f\"The maximum length of the text or prompt column is: {max_length}\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "GXy9v3wn-FRP", | |
| "outputId": "ed5eeac9-bd5b-4fcb-cefa-91fa99b75527" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "The maximum length of the text or prompt column is: 5233\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from unsloth import FastLanguageModel\n", | |
| "\n", | |
| "#from the above we can see that the biggest sequence (or instruction) is a length of 5233\n", | |
| "max_seq_length = 6144 #The model we have chosen can handle sequence lengths of 8192 so this is under that... due to RoPE scaling however we could go more than this.\n", | |
| "#we want to autodetect the precision based on the GPU so we set dtype = None\n", | |
| "dtype = None\n", | |
| "#\n", | |
| "load_in_4bit = True\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "6xkz5KRz6RVt", | |
| "outputId": "21d52d8d-5e39-4332-909c-4a52c13c45af", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "WARNING:xformers:WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:\n", | |
| " PyTorch 2.3.0+cu121 with CUDA 1201 (you have 2.2.1+cu121)\n", | |
| " Python 3.10.14 (you have 3.10.12)\n", | |
| " Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)\n", | |
| " Memory-efficient attention, SwiGLU, sparse and more won't be available.\n", | |
| " Set XFORMERS_MORE_DETAILS=1 for more details\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model, tokenizer = FastLanguageModel.from_pretrained(\n", | |
| " model_name = \"unsloth/llama-3-8b-bnb-4bit\",\n", | |
| " max_seq_length = max_seq_length,\n", | |
| " dtype = dtype,\n", | |
| " load_in_4bit = load_in_4bit\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 400, | |
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| "1184ffdee0a9469f9acb0a21a2ea878e", | |
| "2aa052e84cf44005885691617df4f865", | |
| "e7ad9074cbc0401fbe4df778e0c150ca", | |
| "c8279e5c2b8946a6bb7e8e668764455b", | |
| "bf54e53f4fea481c8b2edd4b83313ade", | |
| "caf9685ae1bc4a2c97b2dfc9de15adcc", | |
| "6554cffb079e4f5c992c047d48b98d9e", | |
| "5ade25d0a4374647a83f864dcffefb40", | |
| "c6a9a264712f4645986fb48080ba62c4", | |
| "765b15fb80e143258948dc8a10e206ad", | |
| "d4872437c64d4938952020be194108e9", | |
| "44dbc6db94a246d3ad1e7ee6cbb26c3c", | |
| "f54552db16264dbf8f95f17bdc37841c", | |
| "c06035683fb64bfd932b347ec264f4bb", | |
| "6af590142de8416b849aac9403ba9da4", | |
| "c544a7e605984dc1b08f1906e493f961", | |
| "42601b6425f14163ad6d90b74d6ee271", | |
| "70dcd9ce2a6f41e1b5e366b41dd21df3", | |
| "cb1d86bf49c24e23b97aadb3c1fd5e61" | |
| ] | |
| }, | |
| "id": "u7m6Y9qjpQHy", | |
| "outputId": "4f05e76f-be90-40c9-b2f7-2f0b7fbe33d2", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", | |
| " warnings.warn(\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "config.json: 0%| | 0.00/1.18k [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "b9a8590d0bd24aff8deefccfe229412a" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "==((====))== Unsloth: Fast Llama patching release 2024.5\n", | |
| " \\\\ /| GPU: NVIDIA L4. Max memory: 22.168 GB. Platform = Linux.\n", | |
| "O^O/ \\_/ \\ Pytorch: 2.2.1+cu121. CUDA = 8.9. CUDA Toolkit = 12.1.\n", | |
| "\\ / Bfloat16 = TRUE. Xformers = 0.0.26.post1. FA = True.\n", | |
| " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "a9877c3de00e4a84b4b180ec4b834f45" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
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| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
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| "model_id": "49b492af42844b5d974512e1e664793c" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "fc39fde64c5d49bf97d4a3a2882727ff" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "51551d53f6c9436c8b916dbc5986b7da" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
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| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "c6a9a264712f4645986fb48080ba62c4" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Unsloth: unsloth/llama-3-8b-bnb-4bit has no tokenizer.model file.\n", | |
| "Just informing you about this - this is not a critical error.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#Optional: Lets do test of the base model first\n", | |
| "FastLanguageModel.for_inference(model=model)\n", | |
| "inputs = tokenizer(\n", | |
| " [\n", | |
| " alpaca_prompt.format(\"List the top 10 movies of 2016\", \"\", \"\")\n", | |
| " ], return_tensors=\"pt\").to(\"cuda\")\n", | |
| "\n", | |
| "out = model.generate(**inputs, max_new_tokens=256, use_cache=True)\n", | |
| "tokenizer.batch_decode(out)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "3slxJxP5N1R2", | |
| "outputId": "1cda053c-9190-4d7b-86d3-834eeb1d6d88" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nList the top 10 movies of 2016\\n\\n### Input:\\n\\n\\n### Response:\\nSELECT * FROM movies WHERE year = 2016 ORDER BY rating DESC LIMIT 10;\\n<|end_of_text|>']" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 14 | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Lets print the model and see what the model looks like currently" | |
| ], | |
| "metadata": { | |
| "id": "7wNeJ7qdrNxK" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(model)" | |
| ], | |
| "metadata": { | |
| "id": "NAxVO9FM-vMU", | |
| "outputId": "93617af2-bca3-4a94-a01d-04b6b307cacf", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "LlamaForCausalLM(\n", | |
| " (model): LlamaModel(\n", | |
| " (embed_tokens): Embedding(128256, 4096)\n", | |
| " (layers): ModuleList(\n", | |
| " (0-31): 32 x LlamaDecoderLayer(\n", | |
| " (self_attn): LlamaSdpaAttention(\n", | |
| " (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", | |
| " (k_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n", | |
| " (v_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n", | |
| " (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", | |
| " (rotary_emb): LlamaRotaryEmbedding()\n", | |
| " )\n", | |
| " (mlp): LlamaMLP(\n", | |
| " (gate_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n", | |
| " (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n", | |
| " (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n", | |
| " (act_fn): SiLU()\n", | |
| " )\n", | |
| " (input_layernorm): LlamaRMSNorm()\n", | |
| " (post_attention_layernorm): LlamaRMSNorm()\n", | |
| " )\n", | |
| " )\n", | |
| " (norm): LlamaRMSNorm()\n", | |
| " )\n", | |
| " (lm_head): Linear(in_features=4096, out_features=128256, bias=False)\n", | |
| ")\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "From the above we can see the modules, the fact that its using RoPE - LLamaRotaryEmbedding\n", | |
| "\n", | |
| "We can also use this to see where we can apply LoRA." | |
| ], | |
| "metadata": { | |
| "id": "HaPFVHmwrVDR" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model = FastLanguageModel.get_peft_model(\n", | |
| " model,\n", | |
| " r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n", | |
| " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", | |
| " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", | |
| " lora_alpha = 16,\n", | |
| " lora_dropout = 0, # Supports any, but = 0 is optimized\n", | |
| " bias = \"none\", # Supports any, but = \"none\" is optimized\n", | |
| " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", | |
| " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", | |
| " random_state = 3407,\n", | |
| " use_rslora = False, # We support rank stabilized LoRA\n", | |
| " loftq_config = None, # And LoftQ\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "kgLVEfAJX8vF", | |
| "outputId": "0b0c33e8-24b3-4441-e32b-6990a5c50cde" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Unsloth 2024.5 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Now lets look at the model again\n" | |
| ], | |
| "metadata": { | |
| "id": "5M-Eknosr08q" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(model)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "eYajysJhr0Qt", | |
| "outputId": "69d07eb0-ccf6-4031-de09-d965ffec6c5f", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "PeftModelForCausalLM(\n", | |
| " (base_model): LoraModel(\n", | |
| " (model): LlamaForCausalLM(\n", | |
| " (model): LlamaModel(\n", | |
| " (embed_tokens): Embedding(128256, 4096)\n", | |
| " (layers): ModuleList(\n", | |
| " (0-31): 32 x LlamaDecoderLayer(\n", | |
| " (self_attn): LlamaSdpaAttention(\n", | |
| " (q_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=4096, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (k_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=1024, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (v_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=1024, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (o_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=4096, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (rotary_emb): LlamaRotaryEmbedding()\n", | |
| " )\n", | |
| " (mlp): LlamaMLP(\n", | |
| " (gate_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=14336, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (up_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=14336, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (down_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=14336, out_features=4096, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=14336, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=4096, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (act_fn): SiLU()\n", | |
| " )\n", | |
| " (input_layernorm): LlamaRMSNorm()\n", | |
| " (post_attention_layernorm): LlamaRMSNorm()\n", | |
| " )\n", | |
| " )\n", | |
| " (norm): LlamaRMSNorm()\n", | |
| " )\n", | |
| " (lm_head): Linear(in_features=4096, out_features=128256, bias=False)\n", | |
| " )\n", | |
| " )\n", | |
| ")\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "Different view - we can see the LoRA adapters added on all the modules we specified." | |
| ], | |
| "metadata": { | |
| "id": "c3N9OLnfsUzO" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "model.print_trainable_parameters()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "wUs_KaY69RRo", | |
| "outputId": "cd85e496-9235-4ae2-ac37-6a0f2783cec7" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "trainable params: 41,943,040 || all params: 8,072,204,288 || trainable%: 0.5195983464188562\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "source": [ | |
| "And a view of the trainable parameters shows that we will only be training ~0.52% of the paramaters which is a massive reduction and will help with the fact that we are only using one GPU." | |
| ], | |
| "metadata": { | |
| "id": "3f1E_YYYsYpW" | |
| } | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from trl import SFTTrainer\n", | |
| "from transformers import TrainingArguments" | |
| ], | |
| "metadata": { | |
| "id": "OTMF36SaXOr5" | |
| }, | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "trainer = SFTTrainer(\n", | |
| " model = model,\n", | |
| " tokenizer = tokenizer,\n", | |
| " train_dataset = dataset,\n", | |
| " dataset_text_field = \"text\",\n", | |
| " max_seq_length = max_seq_length,\n", | |
| " dataset_num_proc = 2,\n", | |
| " packing = False, # Can make training 5x faster for short sequences.\n", | |
| " args = TrainingArguments(\n", | |
| " per_device_train_batch_size = 2,\n", | |
| " gradient_accumulation_steps = 4,\n", | |
| " warmup_steps = 5,\n", | |
| " max_steps = 60,\n", | |
| " learning_rate = 2e-4,\n", | |
| " fp16 = not torch.cuda.is_bf16_supported(),\n", | |
| " bf16 = torch.cuda.is_bf16_supported(),\n", | |
| " logging_steps = 1,\n", | |
| " optim = \"adamw_8bit\",\n", | |
| " weight_decay = 0.01,\n", | |
| " lr_scheduler_type = \"linear\",\n", | |
| " seed = 3407,\n", | |
| " output_dir = \"outputs\",\n", | |
| " ),\n", | |
| ")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 101, | |
| "referenced_widgets": [ | |
| "f9c0dfd6d5ef4672957b50f8236ae3a2", | |
| "d5124a9f3f024aa68545ea9a1e2abba4", | |
| "bc1149cc47754cba96b3dbf9beb14d85", | |
| "e6e974b5407a46bda6e54cb05dff4fe0", | |
| "c0a516bbb99140f78f720d47ac94102f", | |
| "e587e791318f404cb8c5854a486d6c2c", | |
| "53f99799554247de8d82fc4406d48d09", | |
| "549728ef61154b328282fa3f1c515eb2", | |
| "b8550185c75d45039f66a7a1d78cc9ff", | |
| "1385f5e9d7d947189c44c80c1b9e6e93", | |
| "8fc3d1a75d7543e48581729538d30b0c" | |
| ] | |
| }, | |
| "id": "Vx98IuJaXTR1", | |
| "outputId": "a4ffb406-172e-45b5-ddc7-7a4d0313cb2b", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", | |
| " self.pid = os.fork()\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "Map (num_proc=2): 0%| | 0/51760 [00:00<?, ? examples/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "f9c0dfd6d5ef4672957b50f8236ae3a2" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "max_steps is given, it will override any value given in num_train_epochs\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#@title Show current memory stats\n", | |
| "gpu_stats = torch.cuda.get_device_properties(0)\n", | |
| "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", | |
| "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n", | |
| "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n", | |
| "print(f\"{start_gpu_memory} GB of memory reserved.\")" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "EDax26rAYNtF", | |
| "outputId": "48cb4727-690d-4645-d341-52627ca1c2f3" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "GPU = NVIDIA L4. Max memory = 22.168 GB.\n", | |
| "5.748 GB of memory reserved.\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "trainer_stats = trainer.train()" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 1000 | |
| }, | |
| "id": "c9v_H5EjYSW3", | |
| "outputId": "24f1b7e8-eb23-4890-b95b-af9646bd6954", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", | |
| " \\\\ /| Num examples = 51,760 | Num Epochs = 1\n", | |
| "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n", | |
| "\\ / Total batch size = 8 | Total steps = 60\n", | |
| " \"-____-\" Number of trainable parameters = 41,943,040\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "<IPython.core.display.HTML object>" | |
| ], | |
| "text/html": [ | |
| "\n", | |
| " <div>\n", | |
| " \n", | |
| " <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", | |
| " [60/60 03:42, Epoch 0/1]\n", | |
| " </div>\n", | |
| " <table border=\"1\" class=\"dataframe\">\n", | |
| " <thead>\n", | |
| " <tr style=\"text-align: left;\">\n", | |
| " <th>Step</th>\n", | |
| " <th>Training Loss</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <td>1</td>\n", | |
| " <td>1.819400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>2</td>\n", | |
| " <td>2.294600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>3</td>\n", | |
| " <td>1.707400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>4</td>\n", | |
| " <td>2.027900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>5</td>\n", | |
| " <td>1.714600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>6</td>\n", | |
| " <td>1.665900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>7</td>\n", | |
| " <td>1.190300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>8</td>\n", | |
| " <td>1.273200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>9</td>\n", | |
| " <td>1.135700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>10</td>\n", | |
| " <td>1.195400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>11</td>\n", | |
| " <td>0.981100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>12</td>\n", | |
| " <td>0.999900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>13</td>\n", | |
| " <td>0.939900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>14</td>\n", | |
| " <td>1.058800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>15</td>\n", | |
| " <td>0.912700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>16</td>\n", | |
| " <td>0.914900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>17</td>\n", | |
| " <td>1.028700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>18</td>\n", | |
| " <td>1.290900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>19</td>\n", | |
| " <td>1.017900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>20</td>\n", | |
| " <td>0.905700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>21</td>\n", | |
| " <td>0.957400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>22</td>\n", | |
| " <td>1.022900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>23</td>\n", | |
| " <td>0.893400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>24</td>\n", | |
| " <td>1.004800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>25</td>\n", | |
| " <td>1.077100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>26</td>\n", | |
| " <td>1.016200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>27</td>\n", | |
| " <td>1.044200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>28</td>\n", | |
| " <td>0.875600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>29</td>\n", | |
| " <td>0.838600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>30</td>\n", | |
| " <td>0.901300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>31</td>\n", | |
| " <td>0.867300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>32</td>\n", | |
| " <td>0.864400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>33</td>\n", | |
| " <td>0.984800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>34</td>\n", | |
| " <td>0.860800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>35</td>\n", | |
| " <td>0.964300</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>36</td>\n", | |
| " <td>0.861200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>37</td>\n", | |
| " <td>0.886100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>38</td>\n", | |
| " <td>0.767500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>39</td>\n", | |
| " <td>1.084900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>40</td>\n", | |
| " <td>1.158700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>41</td>\n", | |
| " <td>0.904700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>42</td>\n", | |
| " <td>0.984000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>43</td>\n", | |
| " <td>0.959400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>44</td>\n", | |
| " <td>0.900700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>45</td>\n", | |
| " <td>0.922900</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>46</td>\n", | |
| " <td>0.998600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>47</td>\n", | |
| " <td>0.867500</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>48</td>\n", | |
| " <td>1.215800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>49</td>\n", | |
| " <td>0.910000</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>50</td>\n", | |
| " <td>1.045800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>51</td>\n", | |
| " <td>1.020200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>52</td>\n", | |
| " <td>0.924400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>53</td>\n", | |
| " <td>1.004200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>54</td>\n", | |
| " <td>1.167600</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>55</td>\n", | |
| " <td>0.799100</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>56</td>\n", | |
| " <td>1.027400</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>57</td>\n", | |
| " <td>0.884700</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>58</td>\n", | |
| " <td>0.827200</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>59</td>\n", | |
| " <td>0.860800</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <td>60</td>\n", | |
| " <td>0.905600</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table><p>" | |
| ] | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(trainer_stats)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "wozBYJtTJsYC", | |
| "outputId": "21a091af-fb19-4b1f-bc9e-e560e7141191" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "TrainOutput(global_step=60, training_loss=1.0689479072888692, metrics={'train_runtime': 231.5817, 'train_samples_per_second': 2.073, 'train_steps_per_second': 0.259, 'total_flos': 5726714157219840.0, 'train_loss': 1.0689479072888692, 'epoch': 0.00927357032457496})\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "from google.colab import userdata\n", | |
| "HF_TOKEN = userdata.get('HF_TOKEN')\n", | |
| "HF_LOCATION = userdata.get('HF_LOCATION')\n", | |
| "\n", | |
| "model.push_to_hub(HF_LOCATION, HF_TOKEN)\n", | |
| "tokenizer.push_to_hub(HF_LOCATION, HF_TOKEN)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 133, | |
| "referenced_widgets": [ | |
| "477391525336492594f213e060c2d158", | |
| "505526b2261e4d6d98284383a6ba3247", | |
| "de75c049395e431987884ab468f5596a", | |
| "d6a53c70ffda4576a39843a68aba2643", | |
| "471a781202f44e4083ff3d7830172e6f", | |
| "cfb286d7a92140b98f15a2f77020db82", | |
| "0d91029981e6442aa85c28eaebe1259c", | |
| "636fb60ea79e48a49af586270d495fe4", | |
| "ad42e6fedcae48a2848fcde88a0dc336", | |
| "effbb1eaef4747568e3560ff03afc9cf", | |
| "8184bf1ca0af4c49aeca54b57ae608a1", | |
| "d4818c7c701e4131bee110d2d0f5f091", | |
| "02c41118266448afb626d8a3ec7e49ad", | |
| "40990a6a193d4ff398021f9a52f7ede3", | |
| "616d9c6bd65a439eacbf94c8c99f9a8c", | |
| "8799c6be99854c0095b34af06cef4b13", | |
| "732370165ea34468b166b431281a7ef2", | |
| "26ddebc9f7e94729aa35ffd36203ec32", | |
| "c835fb5e85a944eb9310a6f254f9e5a6", | |
| "a228ad62f0454a369132c340129ccf52", | |
| "9fda1470e5384c079714debf3cc69415", | |
| "90996facb8724081994cbcb5d797557e" | |
| ] | |
| }, | |
| "id": "G5Bpu4qVMB-m", | |
| "outputId": "b65d416a-6952-4391-8a9e-151d86a8575f", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "README.md: 0%| | 0.00/579 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "477391525336492594f213e060c2d158" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n", | |
| " warnings.warn(\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "adapter_model.safetensors: 0%| | 0.00/168M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "d4818c7c701e4131bee110d2d0f5f091" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Saved model to https://huggingface.co/ashekhar1976/finetuned_qlora_llama3_instruct\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "#Optional - Test the load using the unsloth loading of the saved model\n", | |
| "\n", | |
| "instruct_saved_model, instruct_saved_tokenizer = FastLanguageModel.from_pretrained(\n", | |
| " model_name = HF_LOCATION,\n", | |
| " max_seq_length = max_seq_length,\n", | |
| " dtype = dtype,\n", | |
| " load_in_4bit = load_in_4bit,\n", | |
| " token = HF_TOKEN\n", | |
| ")\n", | |
| "\n", | |
| "\n" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/", | |
| "height": 237, | |
| "referenced_widgets": [ | |
| "af78be746a06452e82fc2a780b1c2437", | |
| "44f5069169aa416fb12b9be9fd57c56f", | |
| "78aa0a8f23d14e64bdf339b1c894d919", | |
| "5a1d77ddbbe148fd87e9ec63d9d2e422", | |
| "aa650347b4aa4982bf351898c38ab76a", | |
| "b86fdb1ec6ef4231aec3d958e03abe85", | |
| "7ffe1c5650d044558648d434fba5569b", | |
| "b0bf537c8b834ba5b031c257e1120490", | |
| "341f256bc059467d98ab04462e02285a", | |
| "2c27f3613ffa45768e97e0c7b96a9e88", | |
| "1c6540c1521d41ffb1949f50e1b08639", | |
| "22550369bf624334ae5d92daff5b173b", | |
| "a848c57280074f47922aa9b7ec2f58d8", | |
| "4c47909b02f14c519eacd3e5c9412bd4", | |
| "ddd75e7dad7048d8a020af4e590b19d1", | |
| "9de21cde966f4d519e5a235324175eb0", | |
| "023a57a2fe344a57bb755a9f1085648d", | |
| "187d63feef79468e935337c9c691eaa9", | |
| "2b434ce21c664b94ab0ff52a26796321", | |
| "acc07882aada462190d561b7e08588bc", | |
| "838c4ac3d0e64ce586409b0b070fd7f8", | |
| "cd43a49815154a9a9e842d76322d40d2" | |
| ] | |
| }, | |
| "id": "kETkMDo9J9cW", | |
| "outputId": "fff36b37-15dd-4575-9aac-de5015f19217" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "adapter_config.json: 0%| | 0.00/732 [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "af78be746a06452e82fc2a780b1c2437" | |
| } | |
| }, | |
| "metadata": {} | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "==((====))== Unsloth: Fast Llama patching release 2024.5\n", | |
| " \\\\ /| GPU: NVIDIA L4. Max memory: 22.168 GB. Platform = Linux.\n", | |
| "O^O/ \\_/ \\ Pytorch: 2.2.1+cu121. CUDA = 8.9. CUDA Toolkit = 12.1.\n", | |
| "\\ / Bfloat16 = TRUE. Xformers = 0.0.26.post1. FA = True.\n", | |
| " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Unused kwargs: ['_load_in_4bit', '_load_in_8bit', 'quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "Unsloth: unsloth/llama-3-8b-bnb-4bit has no tokenizer.model file.\n", | |
| "Just informing you about this - this is not a critical error.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "data": { | |
| "text/plain": [ | |
| "adapter_model.safetensors: 0%| | 0.00/168M [00:00<?, ?B/s]" | |
| ], | |
| "application/vnd.jupyter.widget-view+json": { | |
| "version_major": 2, | |
| "version_minor": 0, | |
| "model_id": "22550369bf624334ae5d92daff5b173b" | |
| } | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "print(instruct_saved_model)" | |
| ], | |
| "metadata": { | |
| "id": "nR4yW3g7by0R", | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "outputId": "8d923ebe-d887-4d86-b271-8b192e665ac8", | |
| "collapsed": true | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stdout", | |
| "text": [ | |
| "PeftModelForCausalLM(\n", | |
| " (base_model): LoraModel(\n", | |
| " (model): LlamaForCausalLM(\n", | |
| " (model): LlamaModel(\n", | |
| " (embed_tokens): Embedding(128256, 4096)\n", | |
| " (layers): ModuleList(\n", | |
| " (0-31): 32 x LlamaDecoderLayer(\n", | |
| " (self_attn): LlamaSdpaAttention(\n", | |
| " (q_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=4096, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (k_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=1024, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (v_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=1024, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (o_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=4096, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (rotary_emb): LlamaRotaryEmbedding()\n", | |
| " )\n", | |
| " (mlp): LlamaMLP(\n", | |
| " (gate_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=14336, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (up_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=4096, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=14336, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (down_proj): lora.Linear4bit(\n", | |
| " (base_layer): Linear4bit(in_features=14336, out_features=4096, bias=False)\n", | |
| " (lora_dropout): ModuleDict(\n", | |
| " (default): Identity()\n", | |
| " )\n", | |
| " (lora_A): ModuleDict(\n", | |
| " (default): Linear(in_features=14336, out_features=16, bias=False)\n", | |
| " )\n", | |
| " (lora_B): ModuleDict(\n", | |
| " (default): Linear(in_features=16, out_features=4096, bias=False)\n", | |
| " )\n", | |
| " (lora_embedding_A): ParameterDict()\n", | |
| " (lora_embedding_B): ParameterDict()\n", | |
| " )\n", | |
| " (act_fn): SiLU()\n", | |
| " )\n", | |
| " (input_layernorm): LlamaRMSNorm()\n", | |
| " (post_attention_layernorm): LlamaRMSNorm()\n", | |
| " )\n", | |
| " )\n", | |
| " (norm): LlamaRMSNorm()\n", | |
| " )\n", | |
| " (lm_head): Linear(in_features=4096, out_features=128256, bias=False)\n", | |
| " )\n", | |
| " )\n", | |
| ")\n" | |
| ] | |
| } | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "source": [ | |
| "FastLanguageModel.for_inference(model=instruct_saved_model)\n", | |
| "inputs = instruct_saved_tokenizer(\n", | |
| " [\n", | |
| " alpaca_prompt.format(\"List the top 10 movies of 2016\", \"\", \"\")\n", | |
| " ], return_tensors=\"pt\").to(\"cuda\")\n", | |
| "\n", | |
| "out = instruct_saved_model.generate(**inputs, max_new_tokens=256, use_cache=True)\n", | |
| "instruct_saved_tokenizer.batch_decode(out)" | |
| ], | |
| "metadata": { | |
| "colab": { | |
| "base_uri": "https://localhost:8080/" | |
| }, | |
| "id": "q9IGf9hXbFGP", | |
| "outputId": "922b3178-77a7-495b-e081-735fab6a986c" | |
| }, | |
| "execution_count": null, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "name": "stderr", | |
| "text": [ | |
| "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "data": { | |
| "text/plain": [ | |
| "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nList the top 10 movies of 2016\\n\\n### Input:\\n\\n\\n### Response:\\n1. La La Land (2016)\\n2. Moonlight (2016)\\n3. Manchester by the Sea (2016)\\n4. Hacksaw Ridge (2016)\\n5. Arrival (2016)\\n6. Deadpool (2016)\\n7. Rogue One: A Star Wars Story (2016)\\n8. Hidden Figures (2016)\\n9. Lion (2016)\\n10. Sing Street (2016)<|end_of_text|>']" | |
| ] | |
| }, | |
| "metadata": {}, | |
| "execution_count": 27 | |
| } | |
| ] | |
| } | |
| ] | |
| } |
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