1445 lines
42 KiB
Plaintext
1445 lines
42 KiB
Plaintext
{
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"cells": [
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"cell_type": "markdown",
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"id": "14af33bd",
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"status": "completed"
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},
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"tags": []
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||
},
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||
"source": [
|
||
"# Training GPT2 on a wikipedia data to understand how to finetune a foundational model\n",
|
||
"\n",
|
||
"## Tokenization of the data\n",
|
||
"\n",
|
||
"So we need to tokenize the data using the byte pair encoding method to get the training data ready. The model does not understand UTF-8 characters but can make sense of the raw UTF-8 bytes that you can encode using the byte pair encoding method."
|
||
]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "35498829",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-04-10T18:41:26.391038949Z",
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"start_time": "2026-04-10T18:41:23.168674202Z"
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},
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},
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"tags": []
|
||
},
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||
"outputs": [],
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||
"source": [
|
||
"from datasets import load_dataset\n",
|
||
"\n",
|
||
"dataset = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\n",
|
||
"dataset = dataset.filter(lambda x: len(x[\"text\"].strip()) > 0)"
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||
]
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},
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{
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"cell_type": "markdown",
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"id": "cda2e2f4",
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"metadata": {
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"status": "pending"
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},
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"tags": []
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},
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"source": [
|
||
"Let's see if there's some data for us to use"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3125c85f",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-04-10T18:41:26.542937056Z",
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"start_time": "2026-04-10T18:41:26.411012195Z"
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"id": "b1efb38c36d2dd42",
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"outputId": "826d3ede-0e4b-483f-de7d-1fd2f294cb14",
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"duration": null,
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"status": "pending"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
|
||
"for i in range(10):\n",
|
||
" print(dataset[\"train\"][i])"
|
||
]
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},
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{
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"cell_type": "markdown",
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"id": "c3cf0915",
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"metadata": {
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"id": "e8119df124aca910",
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"papermill": {
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"status": "pending"
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},
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"tags": []
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||
},
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||
"source": [
|
||
"## Now time to tokenize the data and then chunk the data\n",
|
||
"We need to tokenize the data so that it can be understood by the model. This is an important step as the model doesn't understnad plain text"
|
||
]
|
||
},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4b81928a",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2026-04-10T18:53:46.503479303Z",
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"start_time": "2026-04-10T18:53:43.641312576Z"
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},
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"execution": {
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},
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"id": "252fb89c3351bf1e",
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"outputId": "06f9f922-c233-4445-bedd-e616bd871dbb",
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"papermill": {
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"status": "pending"
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||
},
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"tags": []
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||
},
|
||
"outputs": [],
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||
"source": [
|
||
"from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
|
||
"\n",
|
||
"tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
|
||
"tokenizer.pad_token = tokenizer.eos_token\n",
|
||
"\n",
|
||
"\n",
|
||
"def tokenize(data):\n",
|
||
" return tokenizer(data[\"text\"], max_length=128)\n",
|
||
"\n",
|
||
"\n",
|
||
"def combine(data):\n",
|
||
" concatenated = {}\n",
|
||
" # Let's concat the data (tokens)\n",
|
||
" for k, lists in data.items():\n",
|
||
" combined = []\n",
|
||
" for lst in lists:\n",
|
||
" combined.extend(lst)\n",
|
||
" concatenated[k] = combined\n",
|
||
"\n",
|
||
" total_length = len(concatenated[\"input_ids\"])\n",
|
||
"\n",
|
||
" total_length = (total_length // 128) * 128\n",
|
||
"\n",
|
||
" result = {}\n",
|
||
"\n",
|
||
" # split into chunks\n",
|
||
" for k, lst in concatenated.items():\n",
|
||
" chunks = []\n",
|
||
" for l in range(0, total_length, 128):\n",
|
||
" chunks.append(lst[l:l + 128])\n",
|
||
" result[k] = chunks\n",
|
||
"\n",
|
||
" result[\"labels\"] = result[\"input_ids\"].copy()\n",
|
||
" return result\n",
|
||
"\n",
|
||
"\n",
|
||
"tokenized_datasets = dataset.map(tokenize, batched=True, remove_columns=[\"text\"])\n",
|
||
"\n",
|
||
"training_dataset = tokenized_datasets.map(combine, batched=True)"
|
||
]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "72c4e93f",
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"metadata": {
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"end_time": "2026-04-10T18:41:33.790641588Z",
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"start_time": "2026-04-10T18:41:33.561714562Z"
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},
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},
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"id": "b47c2da1cef52c2e",
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"outputId": "a5ef3e2d-defa-4a1a-f040-03ccfc7a91df",
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"papermill": {
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"duration": null,
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"exception": null,
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"start_time": null,
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"status": "pending"
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||
},
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"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"for i in range(10):\n",
|
||
" print(len(training_dataset[\"train\"][i][\"input_ids\"]))"
|
||
]
|
||
},
|
||
{
|
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"cell_type": "markdown",
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"id": "af6d3ad7",
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"metadata": {
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"id": "bfd3873f9f578429",
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"papermill": {
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"duration": null,
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"status": "pending"
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},
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"tags": []
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||
},
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||
"source": [
|
||
"## Model loop\n",
|
||
"Now we need to create our training loop for GPT2 using the pretrained model account for back propagation, loss and number of epochs"
|
||
]
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||
},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "41b3d0ab",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-11T12:31:01.902695Z",
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"iopub.status.busy": "2026-04-11T12:31:01.902379Z"
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},
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"id": "8208e77b670a0eb2",
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"outputId": "005113d2-3641-47a9-9c34-fbff7d36ac05",
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"papermill": {
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"duration": null,
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"exception": null,
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"status": "pending"
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},
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"tags": []
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},
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||
"outputs": [],
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||
"source": [
|
||
"import torch\n",
|
||
"import time\n",
|
||
"import torch.optim as optim\n",
|
||
"from torch.utils.data import DataLoader\n",
|
||
"\n",
|
||
"model = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n",
|
||
"model.resize_token_embeddings(len(tokenizer))\n",
|
||
"\n",
|
||
"training_data = DataLoader(training_dataset[\"train\"], batch_size=2, shuffle=True)\n",
|
||
"training_dataset.set_format(type=\"torch\")\n",
|
||
"optimizer = optim.AdamW(model.parameters(), lr=5e-5)\n",
|
||
"\n",
|
||
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
||
"model.to(device)\n",
|
||
"\n",
|
||
"num_epochs = 10\n",
|
||
"total_start = time.time()\n",
|
||
"\n",
|
||
"for epoch in range(num_epochs):\n",
|
||
" start = time.time()\n",
|
||
"\n",
|
||
" model.train()\n",
|
||
" running_loss = 0.0\n",
|
||
"\n",
|
||
" for batch in training_data:\n",
|
||
" input_ids = batch[\"input_ids\"].to(device)\n",
|
||
" attention_mask = batch[\"attention_mask\"].to(device)\n",
|
||
" labels = batch[\"labels\"].to(device)\n",
|
||
"\n",
|
||
" optimizer.zero_grad()\n",
|
||
" outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
|
||
" loss = outputs.loss\n",
|
||
" loss.backward()\n",
|
||
" optimizer.step()\n",
|
||
"\n",
|
||
" running_loss += loss.item()\n",
|
||
"\n",
|
||
" if device.type == \"cuda\": torch.cuda.synchronize()\n",
|
||
"\n",
|
||
" epoch_time = time.time() - start\n",
|
||
" avg_time = (time.time() - total_start) / (epoch + 1)\n",
|
||
" eta = avg_time * (num_epochs - epoch - 1)\n",
|
||
"\n",
|
||
" print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(training_data):.4f}, Time: {epoch_time:.2f}s, ETA: {eta/60:.2f}m\")"
|
||
]
|
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "65323f48",
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"metadata": {
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"papermill": {
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"duration": null,
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"status": "pending"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
|
||
"output_dir = \"./gpt2_finetuned\"\n",
|
||
"model.save_pretrained(output_dir)\n",
|
||
"\n",
|
||
"tokenizer.save_pretrained(output_dir)\n",
|
||
"\n",
|
||
"print(f\"Model saved to {output_dir}\")"
|
||
]
|
||
},
|
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{
|
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"cell_type": "code",
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"execution_count": null,
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"id": "27001a9a",
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"metadata": {
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"status": "pending"
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},
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"tags": []
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||
},
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"outputs": [],
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"source": [
|
||
"import shutil\n",
|
||
"\n",
|
||
"shutil.make_archive('model_output', 'zip', './gpt2_finetuned')\n",
|
||
"\n",
|
||
"print(\"Model zipped and ready for download!\")"
|
||
]
|
||
}
|
||
],
|
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"metadata": {
|
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"accelerator": "GPU",
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"colab": {
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"gpuType": "T4",
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