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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "pkfTZfRqlVmz",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "7cabd189-eb8a-43ee-96a0-b9c80cc1e41d"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: torch==2.5.1 in /usr/local/lib/python3.11/dist-packages (2.5.1+cu124)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1) (3.17.0)\n",
"Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1) (4.12.2)\n",
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"Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1) (3.1.5)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1) (2024.10.0)\n",
"Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch==2.5.1)\n",
" Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
"Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch==2.5.1)\n",
" Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
"Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch==2.5.1)\n",
" Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
"Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch==2.5.1)\n",
" Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
"Collecting nvidia-cublas-cu12==12.4.5.8 (from torch==2.5.1)\n",
" Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
"Collecting nvidia-cufft-cu12==11.2.1.3 (from torch==2.5.1)\n",
" Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
"Collecting nvidia-curand-cu12==10.3.5.147 (from torch==2.5.1)\n",
" Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
"Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch==2.5.1)\n",
" Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
"Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch==2.5.1)\n",
" Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB)\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1) (2.21.5)\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1) (12.4.127)\n",
"Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch==2.5.1)\n",
" Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n",
"Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1) (3.1.0)\n",
"Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch==2.5.1) (1.13.1)\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch==2.5.1) (1.3.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch==2.5.1) (3.0.2)\n",
"Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB)\n",
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"\u001b[?25hDownloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl (127.9 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m127.9/127.9 MB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl (207.5 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m207.5/207.5 MB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
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"\u001b[?25hInstalling collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
" Attempting uninstall: nvidia-nvjitlink-cu12\n",
" Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n",
" Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n",
" Attempting uninstall: nvidia-curand-cu12\n",
" Found existing installation: nvidia-curand-cu12 10.3.6.82\n",
" Uninstalling nvidia-curand-cu12-10.3.6.82:\n",
" Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n",
" Attempting uninstall: nvidia-cufft-cu12\n",
" Found existing installation: nvidia-cufft-cu12 11.2.3.61\n",
" Uninstalling nvidia-cufft-cu12-11.2.3.61:\n",
" Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n",
" Attempting uninstall: nvidia-cuda-runtime-cu12\n",
" Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cuda-nvrtc-cu12\n",
" Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cuda-cupti-cu12\n",
" Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n",
" Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n",
" Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n",
" Attempting uninstall: nvidia-cublas-cu12\n",
" Found existing installation: nvidia-cublas-cu12 12.5.3.2\n",
" Uninstalling nvidia-cublas-cu12-12.5.3.2:\n",
" Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n",
" Attempting uninstall: nvidia-cusparse-cu12\n",
" Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n",
" Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n",
" Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n",
" Attempting uninstall: nvidia-cudnn-cu12\n",
" Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n",
" Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n",
" Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n",
" Attempting uninstall: nvidia-cusolver-cu12\n",
" Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n",
" Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n",
" Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n",
"Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n",
"Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.48.3)\n",
"Collecting datasets\n",
" Downloading datasets-3.3.2-py3-none-any.whl.metadata (19 kB)\n",
"Requirement already satisfied: accelerate in /usr/local/lib/python3.11/dist-packages (1.3.0)\n",
"Collecting bitsandbytes\n",
" Downloading bitsandbytes-0.45.2-py3-none-manylinux_2_24_x86_64.whl.metadata (5.8 kB)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.17.0)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.11/dist-packages (from transformers) (0.28.1)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from transformers) (1.26.4)\n",
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"Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.11/dist-packages (from datasets) (17.0.0)\n",
"Collecting dill<0.3.9,>=0.3.0 (from datasets)\n",
" Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from datasets) (2.2.2)\n",
"Collecting xxhash (from datasets)\n",
" Downloading xxhash-3.5.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
"Collecting multiprocess<0.70.17 (from datasets)\n",
" Downloading multiprocess-0.70.16-py311-none-any.whl.metadata (7.2 kB)\n",
"Requirement already satisfied: fsspec<=2024.12.0,>=2023.1.0 in /usr/local/lib/python3.11/dist-packages (from fsspec[http]<=2024.12.0,>=2023.1.0->datasets) (2024.10.0)\n",
"Requirement already satisfied: aiohttp in /usr/local/lib/python3.11/dist-packages (from datasets) (3.11.12)\n",
"Requirement already satisfied: psutil in /usr/local/lib/python3.11/dist-packages (from accelerate) (5.9.5)\n",
"Requirement already satisfied: torch>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from accelerate) (2.5.1+cu124)\n",
"Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (2.4.6)\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.3.2)\n",
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (25.1.0)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.5.0)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (6.1.0)\n",
"Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (0.2.1)\n",
"Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp->datasets) (1.18.3)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (4.12.2)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (3.4.1)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->transformers) (2025.1.31)\n",
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"Requirement already satisfied: jinja2 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (3.1.5)\n",
"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=2.0.0->accelerate) (12.4.127)\n",
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"\u001b[?25hDownloading torchvision-0.21.0-cp311-cp311-manylinux1_x86_64.whl (7.2 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m64.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading tyro-0.9.16-py3-none-any.whl (117 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.2/117.2 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading shtab-1.7.1-py3-none-any.whl (14 kB)\n",
"Downloading cut_cross_entropy-25.1.1-py3-none-any.whl (22 kB)\n",
"Installing collected packages: triton, nvidia-cusparselt-cu12, shtab, protobuf, hf_transfer, tyro, torch, xformers, torchvision, cut_cross_entropy, trl, unsloth_zoo, unsloth\n",
" Attempting uninstall: triton\n",
" Found existing installation: triton 3.1.0\n",
" Uninstalling triton-3.1.0:\n",
" Successfully uninstalled triton-3.1.0\n",
" Attempting uninstall: protobuf\n",
" Found existing installation: protobuf 4.25.6\n",
" Uninstalling protobuf-4.25.6:\n",
" Successfully uninstalled protobuf-4.25.6\n",
" Attempting uninstall: torch\n",
" Found existing installation: torch 2.5.1+cu124\n",
" Uninstalling torch-2.5.1+cu124:\n",
" Successfully uninstalled torch-2.5.1+cu124\n",
" Attempting uninstall: torchvision\n",
" Found existing installation: torchvision 0.20.1+cu124\n",
" Uninstalling torchvision-0.20.1+cu124:\n",
" Successfully uninstalled torchvision-0.20.1+cu124\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"tensorflow-metadata 1.16.1 requires protobuf<6.0.0dev,>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.3 which is incompatible.\n",
"torchaudio 2.5.1+cu124 requires torch==2.5.1, but you have torch 2.6.0 which is incompatible.\n",
"fastai 2.7.18 requires torch<2.6,>=1.10, but you have torch 2.6.0 which is incompatible.\n",
"grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed cut_cross_entropy-25.1.1 hf_transfer-0.1.9 nvidia-cusparselt-cu12-0.6.2 protobuf-3.20.3 shtab-1.7.1 torch-2.6.0 torchvision-0.21.0 triton-3.2.0 trl-0.15.1 tyro-0.9.16 unsloth-2025.2.15 unsloth_zoo-2025.2.7 xformers-0.0.29.post3\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"google"
]
},
"id": "533506afd8e44fe2b8905846565e956a"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: peft in /usr/local/lib/python3.11/dist-packages (0.14.0)\n",
"Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.11/dist-packages (from peft) (1.26.4)\n",
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"Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface-hub>=0.25.0->peft) (3.17.0)\n",
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"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n",
"Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (9.1.0.70)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (12.4.5.8)\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (11.2.1.3)\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (10.3.5.147)\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (11.6.1.9)\n",
"Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (12.3.1.170)\n",
"Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (0.6.2)\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (2.21.5)\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (12.4.127)\n",
"Requirement already satisfied: triton==3.2.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (3.2.0)\n",
"Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.13.0->peft) (1.13.1)\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.13.0->peft) (1.3.0)\n",
"Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.11/dist-packages (from transformers->peft) (2024.11.6)\n",
"Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.11/dist-packages (from transformers->peft) (0.21.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.13.0->peft) (3.0.2)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (3.4.1)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (3.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2.3.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface-hub>=0.25.0->peft) (2025.1.31)\n"
]
}
],
"source": [
"#Install required packages\n",
"!pip install torch==2.5.1\n",
"!pip install transformers datasets accelerate bitsandbytes\n",
"!pip install unsloth\n",
"!pip install peft"
]
},
{
"cell_type": "code",
"source": [
"#import the necessary libraries\n",
"import torch\n",
"from datasets import Dataset\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"import pandas as pd\n",
"from datetime import datetime\n",
"from transformers import TrainingArguments, Trainer\n",
"\n",
"# Verify GPU\n",
"print(f\"CUDA Available: {torch.cuda.is_available()}\")\n",
"if torch.cuda.is_available():\n",
" print(f\"GPU Device: {torch.cuda.get_device_name(0)}\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DY7my1flAYp1",
"outputId": "5d85981e-e6d5-4428-feba-a73cc2ef3715"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"CUDA Available: False\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"tokenizer = AutoTokenizer.from_pretrained(\"TinyLlama/TinyLlama-1.1B-Chat-v1.0\")\n",
"# Use AutoModelForCausalLM for decoder-only models like Llama\n",
"model = AutoModelForCausalLM.from_pretrained(\"TinyLlama/TinyLlama-1.1B-Chat-v1.0\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RKDVN_nMDEbT",
"outputId": "96593e9d-5e7b-40de-8731-7410b47dbd10"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"#Install Hugging Face Hub\n",
"!pip install huggingface_hub"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZcE_a0f2Ppao",
"outputId": "ffd6f90e-8d11-4d64-bcb6-edd463936e3f"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.11/dist-packages (0.28.1)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (3.17.0)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (2024.10.0)\n",
"Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (24.2)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (6.0.2)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (2.32.3)\n",
"Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.67.1)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.11/dist-packages (from huggingface_hub) (4.12.2)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.4.1)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (3.10)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2.3.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->huggingface_hub) (2025.1.31)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17,
"referenced_widgets": [
"db61b3f340434aafb100c7e599d77f7f",
"b9af1a2944fe4d128540a42d8dfdd3d9",
"ab4dd8c00ff145b28ebcb9f0a31ffe37",
"f861fc357911420ea5e8b56e2e84d14e",
"b70cdf3f892a4b39a97e0c2e3985690b",
"8087417ad0164d3aa8f2ea910332a68c",
"d39f9f8d3f8d42869a10fc906eb35643",
"94e362dcfdce4488aef3d0dd8a08aff3",
"2d75a6161b614f3db6ff29a86fcf97e1",
"81ef37ab498c44818ec17f013fae4835",
"e10492c157014804b80a41fe9c1be3a5",
"29d027e3934648dd8131b00ce558ceac",
"2a857628c877440391c4f5a6a2a4fd95",
"21950235754e4228a46b53662870d9e4",
"dc672e082e744b7c8da4054868ffa497",
"f8b7dc98a78a45f0a41455321a49516d",
"c74d881e14d748c88be20bcab053dc02",
"b6ba7c7d3a2f45bb8888d747655823f2",
"4ae4d60cd5804ac9be32720ce60aad8b",
"98b3659391fb46748614792d89937fc2"
]
},
"id": "sD3YvBY6QUML",
"outputId": "df7f1184-0660-4bd7-b312-8ebc6e894fc7"
},
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "db61b3f340434aafb100c7e599d77f7f"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from datasets import load_dataset\n",
"\n",
"# Load the dataset from Hugging Face Hub\n",
"dataset = load_dataset(\"fathimazulaikha/SAWiT-Tamil-Colloquial-Dataset\", data_files=\"my_tamil_english_dataset.csv\", split=\"train\") # Assuming the CSV is in the \"train\" split"
],
"metadata": {
"id": "zeFsp-PsQZjM"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Adjust max_length in the preprocessing function\n",
"def preprocess_function(examples):\n",
" inputs = [ex for ex in examples[\"English Text\"]]\n",
" targets = [ex for ex in examples[\"Tamil Text\"]]\n",
" model_inputs = tokenizer(inputs, max_length=64, truncation=True, padding=\"max_length\") # Reduced max_length\n",
" labels = tokenizer(targets, max_length=64, truncation=True, padding=\"max_length\") # Reduced max_length\n",
" model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
" return model_inputs\n",
"\n",
"tokenized_datasets = dataset.map(preprocess_function, batched=True)"
],
"metadata": {
"id": "k1NKoEGKT7JH"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Split the dataset into train and test sets\n",
"tokenized_datasets = tokenized_datasets.train_test_split(test_size=0.2) # Adjust test_size as needed"
],
"metadata": {
"id": "ZIxIKkYGU2ON"
},
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Ensure the data is loaded properly\n",
"print(tokenized_datasets['train'][0])\n",
"print(tokenized_datasets['train'][4])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OSESSVD5YFiW",
"outputId": "137549f4-8794-4f98-bc39-2ecc266cef6a"
},
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'English Text': 'Please complete this.', 'Tamil Text': 'Dayaavu seithu idhai mudichidunga.\\t', 'Category': 'Formal Phrases\\n', 'input_ids': [1, 3529, 4866, 445, 29889, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], 'attention_mask': [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [1, 8373, 29874, 485, 29884, 409, 389, 29884, 1178, 23535, 17439, 436, 333, 686, 29874, 29889, 12, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]}\n",
"{'English Text': 'Thank you very much, your help was very useful.', 'Tamil Text': 'Romba nandri, ungaloda help romba useful ah irundhuchu.\\t', 'Category': 'Formal Phrases\\n', 'input_ids': [1, 3374, 366, 1407, 1568, 29892, 596, 1371, 471, 1407, 5407, 29889, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'labels': [1, 6033, 2291, 302, 392, 374, 29892, 443, 23014, 8887, 1371, 6017, 2291, 5407, 21023, 3805, 870, 29882, 987, 29884, 29889, 12, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]}\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import gc\n",
"\n",
"# Before starting training\n",
"gc.collect()\n",
"torch.cuda.empty_cache()"
],
"metadata": {
"id": "aK4SkrJZbz1e"
},
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"source": [
"training_args = TrainingArguments(\n",
" output_dir=\"./results\",\n",
" per_device_train_batch_size=2, # Reduced batch size further\n",
" per_device_eval_batch_size=2, # Reduced batch size further\n",
" num_train_epochs=3,\n",
" gradient_accumulation_steps=8, # Increased gradient accumulation further\n",
" report_to=\"none\",\n",
" fp16=True,\n",
" gradient_checkpointing=True,\n",
")\n",
"\n",
"# Initialize the Trainer with the model and training arguments\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=tokenized_datasets[\"train\"], # Provide the training dataset\n",
" eval_dataset=tokenized_datasets[\"test\"] # Provide the evaluation dataset\n",
")\n",
"\n",
"# Before starting training\n",
"torch.cuda.empty_cache()\n",
"\n",
"# Start training\n",
"trainer.train()\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5cJMUW6vVhWV",
"outputId": "89f1eb91-a047-43f3-abe1-a50d56975bb6"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Some sample English inputs for testing\n",
"english_sentences = [\n",
" \"What are you doing?\",\n",
" \"What is your name?\",\n",
" \"Please wait for a moment.\",\n",
" \"He's such a fool.\",\n",
"]"
],
"metadata": {
"id": "6U_mNvAMXge5"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(tokenizer(\"What are you doing?\"))\n",
"print(tokenizer(\"enna panra?\")) # Or the expected Tanglish translation"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 158
},
"id": "81_uxYlFYkaI",
"outputId": "955d1bc4-015d-4f28-8e2b-fcd6d8816c07"
},
"execution_count": 1,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "name 'tokenizer' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-362437221024>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"What are you doing?\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"enna panra?\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Or the expected Tanglish translation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'tokenizer' is not defined"
]
}
]
},
{
"cell_type": "code",
"source": [
"for sentence in english_sentences:\n",
" inputs = tokenizer(sentence, return_tensors=\"pt\").to(model.device) # Move inputs to the same device as the model\n",
"\n",
" # Remove num_beams, num_return_sequences to use defaults\n",
" # The error occurs because 'past_key_values' might not have the expected values during beam search.\n",
" # Force model to not use past key values for each sentence to avoid the error.\n",
" translated_tokens = model.generate(**inputs, use_cache=False)\n",
" translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)\n",
" print(f\"English: {sentence}\")\n",
" print(f\"Tamil: {translated_text}\")\n",
" print(\"-\" * 20) # Separator for clarity"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 443
},
"id": "hW6x2Xp-XyTI",
"outputId": "ff807488-c25f-4a60-d6bc-54b21d209ee7"
},
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.11/dist-packages/torch/utils/checkpoint.py:87: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
" warnings.warn(\n"
]
},
{
"output_type": "error",
"ename": "OutOfMemoryError",
"evalue": "CUDA out of memory. Tried to allocate 22.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 6.12 MiB is free. Process 2300 has 14.73 GiB memory in use. Of the allocated memory 14.27 GiB is allocated by PyTorch, and 336.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-44-ab0e3d17753b>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# The error occurs because 'past_key_values' might not have the expected values during beam search.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Force model to not use past key values for each sentence to avoid the error.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mtranslated_tokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muse_cache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mtranslated_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtranslated_tokens\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_special_tokens\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[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"English: {sentence}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mctx_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)\u001b[0m\n\u001b[1;32m 2253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2254\u001b[0m \u001b[0;31m# 12. run sample (it degenerates to greedy search when `generation_config.do_sample=False`)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2255\u001b[0;31m result = self._sample(\n\u001b[0m\u001b[1;32m 2256\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2257\u001b[0m \u001b[0mlogits_processor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprepared_logits_processor\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/generation/utils.py\u001b[0m in \u001b[0;36m_sample\u001b[0;34m(self, input_ids, logits_processor, stopping_criteria, generation_config, synced_gpus, streamer, **model_kwargs)\u001b[0m\n\u001b[1;32m 3252\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3253\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_prefill\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3254\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mmodel_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_dict\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[0m\u001b[1;32m 3255\u001b[0m \u001b[0mis_prefill\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3256\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1738\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1739\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1740\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1741\u001b[0m \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1748\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1749\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1750\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1751\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1752\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 818\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 821\u001b[0m \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 817\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 818\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 821\u001b[0m \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/transformers/models/llama/modeling_llama.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, cache_position, num_logits_to_keep, **kwargs)\u001b[0m\n\u001b[1;32m 832\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 833\u001b[0m \u001b[0;31m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 834\u001b[0;31m outputs = self.model(\n\u001b[0m\u001b[1;32m 835\u001b[0m \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 836\u001b[0m \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m 123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 124\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 125\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 127\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 22.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 6.12 MiB is free. Process 2300 has 14.73 GiB memory in use. Of the allocated memory 14.27 GiB is allocated by PyTorch, and 336.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
]
}
]
}
]
} |