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"# Training GPT2 on a wikipedia data to understand how to finetune a foundational model\n",
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"\n",
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"## Tokenization of the data\n",
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"\n",
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"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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"from datasets import load_dataset\n",
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"from transformers import GPT2Tokenizer, GPT2LMHeadModel"
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],
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"dataset = load_dataset(\"wikitext\", \"wikitext-2-raw-v1\")\n",
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],
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"/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
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||
"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"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"README.md: 0.00B [00:00, ?B/s]"
|
||
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|
||
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|
||
"version_major": 2,
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|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
|
||
"WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"wikitext-2-raw-v1/test-00000-of-00001.pa(…): 0%| | 0.00/733k [00:00<?, ?B/s]"
|
||
],
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||
"application/vnd.jupyter.widget-view+json": {
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|
||
}
|
||
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||
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|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"wikitext-2-raw-v1/train-00000-of-00001.p(…): 0%| | 0.00/6.36M [00:00<?, ?B/s]"
|
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||
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||
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||
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||
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|
||
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|
||
"data": {
|
||
"text/plain": [
|
||
"wikitext-2-raw-v1/validation-00000-of-00(…): 0%| | 0.00/657k [00:00<?, ?B/s]"
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||
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||
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||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
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||
"text/plain": [
|
||
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|
||
],
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||
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||
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||
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||
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||
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||
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|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
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|
||
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||
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||
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|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
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||
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||
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||
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"output_type": "display_data",
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||
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||
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||
"output_type": "display_data",
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||
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"text/plain": [
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],
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||
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|
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"version_major": 2,
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"version_minor": 0,
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|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
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|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
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|
||
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||
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||
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||
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||
}
|
||
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|
||
"execution_count": 2
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"Let's see if there's some data for us to use"
|
||
],
|
||
"metadata": {
|
||
"id": "27c55eaf29e555a2"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"for i in range(10):\n",
|
||
" print(dataset[\"train\"][i])"
|
||
],
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-10T18:41:26.542937056Z",
|
||
"start_time": "2026-04-10T18:41:26.411012195Z"
|
||
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||
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||
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|
||
"trusted": true,
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"shell.execute_reply": "2026-04-12T18:19:18.073955Z"
|
||
},
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"{'text': ' = Valkyria Chronicles III = \\n'}\n",
|
||
"{'text': ' Senjō no Valkyria 3 : Unrecorded Chronicles ( Japanese : 戦場のヴァルキュリア3 , lit . Valkyria of the Battlefield 3 ) , commonly referred to as Valkyria Chronicles III outside Japan , is a tactical role @-@ playing video game developed by Sega and Media.Vision for the PlayStation Portable . Released in January 2011 in Japan , it is the third game in the Valkyria series . Employing the same fusion of tactical and real @-@ time gameplay as its predecessors , the story runs parallel to the first game and follows the \" Nameless \" , a penal military unit serving the nation of Gallia during the Second Europan War who perform secret black operations and are pitted against the Imperial unit \" Calamaty Raven \" . \\n'}\n",
|
||
"{'text': \" The game began development in 2010 , carrying over a large portion of the work done on Valkyria Chronicles II . While it retained the standard features of the series , it also underwent multiple adjustments , such as making the game more forgiving for series newcomers . Character designer Raita Honjou and composer Hitoshi Sakimoto both returned from previous entries , along with Valkyria Chronicles II director Takeshi Ozawa . A large team of writers handled the script . The game 's opening theme was sung by May 'n . \\n\"}\n",
|
||
"{'text': \" It met with positive sales in Japan , and was praised by both Japanese and western critics . After release , it received downloadable content , along with an expanded edition in November of that year . It was also adapted into manga and an original video animation series . Due to low sales of Valkyria Chronicles II , Valkyria Chronicles III was not localized , but a fan translation compatible with the game 's expanded edition was released in 2014 . Media.Vision would return to the franchise with the development of Valkyria : Azure Revolution for the PlayStation 4 . \\n\"}\n",
|
||
"{'text': ' = = Gameplay = = \\n'}\n",
|
||
"{'text': \" As with previous Valkyira Chronicles games , Valkyria Chronicles III is a tactical role @-@ playing game where players take control of a military unit and take part in missions against enemy forces . Stories are told through comic book @-@ like panels with animated character portraits , with characters speaking partially through voiced speech bubbles and partially through unvoiced text . The player progresses through a series of linear missions , gradually unlocked as maps that can be freely scanned through and replayed as they are unlocked . The route to each story location on the map varies depending on an individual player 's approach : when one option is selected , the other is sealed off to the player . Outside missions , the player characters rest in a camp , where units can be customized and character growth occurs . Alongside the main story missions are character @-@ specific sub missions relating to different squad members . After the game 's completion , additional episodes are unlocked , some of them having a higher difficulty than those found in the rest of the game . There are also love simulation elements related to the game 's two main heroines , although they take a very minor role . \\n\"}\n",
|
||
"{'text': ' The game \\'s battle system , the BliTZ system , is carried over directly from Valkyira Chronicles . During missions , players select each unit using a top @-@ down perspective of the battlefield map : once a character is selected , the player moves the character around the battlefield in third @-@ person . A character can only act once per @-@ turn , but characters can be granted multiple turns at the expense of other characters \\' turns . Each character has a field and distance of movement limited by their Action Gauge . Up to nine characters can be assigned to a single mission . During gameplay , characters will call out if something happens to them , such as their health points ( HP ) getting low or being knocked out by enemy attacks . Each character has specific \" Potentials \" , skills unique to each character . They are divided into \" Personal Potential \" , which are innate skills that remain unaltered unless otherwise dictated by the story and can either help or impede a character , and \" Battle Potentials \" , which are grown throughout the game and always grant boons to a character . To learn Battle Potentials , each character has a unique \" Masters Table \" , a grid @-@ based skill table that can be used to acquire and link different skills . Characters also have Special Abilities that grant them temporary boosts on the battlefield : Kurt can activate \" Direct Command \" and move around the battlefield without depleting his Action Point gauge , the character Reila can shift into her \" Valkyria Form \" and become invincible , while Imca can target multiple enemy units with her heavy weapon . \\n'}\n",
|
||
"{'text': \" Troops are divided into five classes : Scouts , Shocktroopers , Engineers , Lancers and Armored Soldier . Troopers can switch classes by changing their assigned weapon . Changing class does not greatly affect the stats gained while in a previous class . With victory in battle , experience points are awarded to the squad , which are distributed into five different attributes shared by the entire squad , a feature differing from early games ' method of distributing to different unit types . \\n\"}\n",
|
||
"{'text': ' = = Plot = = \\n'}\n",
|
||
"{'text': ' The game takes place during the Second Europan War . Gallian Army Squad 422 , also known as \" The Nameless \" , are a penal military unit composed of criminals , foreign deserters , and military offenders whose real names are erased from the records and thereon officially referred to by numbers . Ordered by the Gallian military to perform the most dangerous missions that the Regular Army and Militia will not do , they are nevertheless up to the task , exemplified by their motto , Altaha Abilia , meaning \" Always Ready . \" The three main characters are No.7 Kurt Irving , an army officer falsely accused of treason who wishes to redeem himself ; Ace No.1 Imca , a female Darcsen heavy weapons specialist who seeks revenge against the Valkyria who destroyed her home ; and No.13 Riela Marcellis , a seemingly jinxed young woman who is unknowingly a descendant of the Valkyria . Together with their fellow squad members , these three are tasked to fight against a mysterious Imperial unit known as Calamity Raven , consisting of mostly Darcsen soldiers . \\n'}\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 3
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"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"
|
||
],
|
||
"metadata": {
|
||
"id": "e8119df124aca910"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"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)"
|
||
],
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-10T18:53:46.503479303Z",
|
||
"start_time": "2026-04-10T18:53:43.641312576Z"
|
||
},
|
||
"id": "252fb89c3351bf1e",
|
||
"trusted": true,
|
||
"execution": {
|
||
"iopub.status.busy": "2026-04-12T18:19:18.076870Z",
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"iopub.execute_input": "2026-04-12T18:19:18.077378Z",
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"iopub.status.idle": "2026-04-12T18:19:20.016846Z",
|
||
"shell.execute_reply.started": "2026-04-12T18:19:18.077354Z",
|
||
"shell.execute_reply": "2026-04-12T18:19:20.016103Z"
|
||
},
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||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
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"height": 337,
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"tokenizer_config.json: 0%| | 0.00/26.0 [00:00<?, ?B/s]"
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"output_type": "display_data",
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],
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}
|
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},
|
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"metadata": {}
|
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},
|
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{
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"output_type": "display_data",
|
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"data": {
|
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"text/plain": [
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],
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"version_major": 2,
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"version_minor": 0,
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"model_id": "0694ce3572b7468880b8df47a9d0b4e9"
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}
|
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},
|
||
"metadata": {}
|
||
}
|
||
],
|
||
"execution_count": 4
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"for i in range(10):\n",
|
||
" print(len(training_dataset[\"train\"][i][\"input_ids\"]))"
|
||
],
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2026-04-10T18:41:33.790641588Z",
|
||
"start_time": "2026-04-10T18:41:33.561714562Z"
|
||
},
|
||
"id": "b47c2da1cef52c2e",
|
||
"outputId": "b57fed60-4cd3-4a93-e713-ad3b49dbe030",
|
||
"trusted": true,
|
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"execution": {
|
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"iopub.status.busy": "2026-04-12T18:19:20.017977Z",
|
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"iopub.execute_input": "2026-04-12T18:19:20.018271Z",
|
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"iopub.status.idle": "2026-04-12T18:19:20.025401Z",
|
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"shell.execute_reply.started": "2026-04-12T18:19:20.018240Z",
|
||
"shell.execute_reply": "2026-04-12T18:19:20.024542Z"
|
||
},
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"128\n",
|
||
"128\n",
|
||
"128\n",
|
||
"128\n",
|
||
"128\n",
|
||
"128\n",
|
||
"128\n",
|
||
"128\n",
|
||
"128\n",
|
||
"128\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 5
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"## The model loop and getting the model ready\n",
|
||
"Now we need to create our training loop for GPT2 using the pretrained model account for back propagation, loss and number of epochs\n",
|
||
"\n",
|
||
"### Model with a full fine tune\n",
|
||
"We will first do it for a full fine tine\n"
|
||
],
|
||
"metadata": {
|
||
"id": "ab1d43d2e57374c2"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"model_full = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n",
|
||
"model_full.resize_token_embeddings(len(tokenizer))\n",
|
||
"\n",
|
||
"training_dataset.set_format(type=\"torch\")\n",
|
||
"training_data = DataLoader(training_dataset[\"train\"], batch_size=2, shuffle=True)\n",
|
||
"testing_data = DataLoader(training_dataset[\"test\"], batch_size=2, shuffle=True)\n",
|
||
"optimiser = optim.AdamW(filter(lambda p: p.requires_grad, model_full.parameters()), lr=5e-5)\n",
|
||
"\n",
|
||
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
||
"model_full.to(device)\n",
|
||
"\n",
|
||
"trainable_params = sum(p.numel() for p in model_full.parameters() if p.requires_grad)\n",
|
||
"all_param = sum(p.numel() for p in model_full.parameters())\n",
|
||
"print(f\"trainable params: {trainable_params:,} || all params: {all_param:,} || trainable%: {100 * trainable_params / all_param:.4f}\")"
|
||
],
|
||
"metadata": {
|
||
"trusted": true,
|
||
"execution": {
|
||
"iopub.status.busy": "2026-04-12T18:19:20.026557Z",
|
||
"iopub.execute_input": "2026-04-12T18:19:20.026883Z",
|
||
"iopub.status.idle": "2026-04-12T18:19:20.652002Z",
|
||
"shell.execute_reply.started": "2026-04-12T18:19:20.026846Z",
|
||
"shell.execute_reply": "2026-04-12T18:19:20.651280Z"
|
||
},
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 296,
|
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|
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|
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"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"config.json: 0%| | 0.00/665 [00:00<?, ?B/s]"
|
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|
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}
|
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},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"model.safetensors: 0%| | 0.00/548M [00:00<?, ?B/s]"
|
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],
|
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"application/vnd.jupyter.widget-view+json": {
|
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"version_minor": 0,
|
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|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Loading weights: 0%| | 0/148 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "da0d3e7ece104fd1b5593f02de1feca7"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"GPT2LMHeadModel LOAD REPORT from: gpt2\n",
|
||
"Key | Status | | \n",
|
||
"---------------------+------------+--+-\n",
|
||
"h.{0...11}.attn.bias | UNEXPECTED | | \n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"generation_config.json: 0%| | 0.00/124 [00:00<?, ?B/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "0be98837bab7493099aad16d9459134c"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"trainable params: 124,439,808 || all params: 124,439,808 || trainable%: 100.0000\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 6
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"num_epochs = 15\n",
|
||
"total_start = time.time()\n",
|
||
"\n",
|
||
"for epoch in range(num_epochs):\n",
|
||
" start = time.time()\n",
|
||
"\n",
|
||
" model_full.train()\n",
|
||
" train_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",
|
||
" optimiser.zero_grad()\n",
|
||
" outputs = model_full(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
|
||
" loss = outputs.loss\n",
|
||
" loss.backward()\n",
|
||
" optimiser.step()\n",
|
||
"\n",
|
||
" train_loss += loss.item()\n",
|
||
"\n",
|
||
" model_full.eval()\n",
|
||
" validation_loss = 0.0\n",
|
||
"\n",
|
||
" with torch.no_grad():\n",
|
||
" for batch in testing_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",
|
||
" outputs = model_full(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
|
||
" validation_loss += outputs.loss.item()\n",
|
||
"\n",
|
||
" avg_train_loss = train_loss / len(training_data)\n",
|
||
" avg_val_loss = validation_loss / len(testing_data)\n",
|
||
" perplexity = torch.exp(torch.tensor(avg_val_loss))\n",
|
||
"\n",
|
||
" if device.type == \"cuda\":\n",
|
||
" torch.cuda.synchronize()\n",
|
||
"\n",
|
||
" epoch_time = time.time() - start\n",
|
||
" avg_time_per_epoch = (time.time() - total_start) / (epoch + 1)\n",
|
||
" eta = avg_time_per_epoch * (num_epochs - epoch - 1)\n",
|
||
"\n",
|
||
" print(f\"Epoch {epoch+1}/{num_epochs}\")\n",
|
||
" print(f\"Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | PPL: {perplexity:.2f}\")\n",
|
||
" print(f\"Time: {epoch_time:.2f}s | ETA: {eta/60:.2f}m\")\n",
|
||
" print(\"-\" * 30)"
|
||
],
|
||
"metadata": {
|
||
"id": "8208e77b670a0eb2",
|
||
"outputId": "5d1b394d-49bf-4711-88f7-26cb070367ac",
|
||
"trusted": true,
|
||
"execution": {
|
||
"iopub.status.busy": "2026-04-12T18:19:20.652803Z",
|
||
"iopub.execute_input": "2026-04-12T18:19:20.653091Z"
|
||
},
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"`loss_type=None` was set in the config but it is unrecognized. Using the default loss: `ForCausalLMLoss`.\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Epoch 1/15\n",
|
||
"Train Loss: 3.5623 | Val Loss: 3.4823 | PPL: 32.53\n",
|
||
"Time: 807.01s | ETA: 188.30m\n",
|
||
"------------------------------\n",
|
||
"Epoch 2/15\n",
|
||
"Train Loss: 3.1944 | Val Loss: 3.5107 | PPL: 33.47\n",
|
||
"Time: 804.86s | ETA: 174.62m\n",
|
||
"------------------------------\n",
|
||
"Epoch 3/15\n",
|
||
"Train Loss: 2.9477 | Val Loss: 3.5796 | PPL: 35.86\n",
|
||
"Time: 804.26s | ETA: 161.08m\n",
|
||
"------------------------------\n",
|
||
"Epoch 4/15\n",
|
||
"Train Loss: 2.7343 | Val Loss: 3.6644 | PPL: 39.03\n",
|
||
"Time: 804.41s | ETA: 147.61m\n",
|
||
"------------------------------\n",
|
||
"Epoch 5/15\n",
|
||
"Train Loss: 2.5390 | Val Loss: 3.7701 | PPL: 43.38\n",
|
||
"Time: 803.75s | ETA: 134.14m\n",
|
||
"------------------------------\n",
|
||
"Epoch 6/15\n",
|
||
"Train Loss: 2.3569 | Val Loss: 3.9075 | PPL: 49.78\n",
|
||
"Time: 805.25s | ETA: 120.74m\n",
|
||
"------------------------------\n",
|
||
"Epoch 7/15\n",
|
||
"Train Loss: 2.1847 | Val Loss: 4.0130 | PPL: 55.31\n",
|
||
"Time: 804.88s | ETA: 107.32m\n",
|
||
"------------------------------\n",
|
||
"Epoch 8/15\n",
|
||
"Train Loss: 2.0214 | Val Loss: 4.1459 | PPL: 63.18\n",
|
||
"Time: 804.38s | ETA: 93.90m\n",
|
||
"------------------------------\n",
|
||
"Epoch 9/15\n",
|
||
"Train Loss: 1.8675 | Val Loss: 4.3203 | PPL: 75.21\n",
|
||
"Time: 804.52s | ETA: 80.48m\n",
|
||
"------------------------------\n",
|
||
"Epoch 10/15\n",
|
||
"Train Loss: 1.7229 | Val Loss: 4.4458 | PPL: 85.27\n",
|
||
"Time: 804.39s | ETA: 67.06m\n",
|
||
"------------------------------\n",
|
||
"Epoch 11/15\n",
|
||
"Train Loss: 1.5866 | Val Loss: 4.6018 | PPL: 99.67\n",
|
||
"Time: 806.30s | ETA: 53.66m\n",
|
||
"------------------------------\n",
|
||
"Epoch 12/15\n",
|
||
"Train Loss: 1.4592 | Val Loss: 4.7285 | PPL: 113.12\n",
|
||
"Time: 804.76s | ETA: 40.24m\n",
|
||
"------------------------------\n",
|
||
"Epoch 13/15\n",
|
||
"Train Loss: 1.3404 | Val Loss: 4.8635 | PPL: 129.48\n",
|
||
"Time: 804.95s | ETA: 26.83m\n",
|
||
"------------------------------\n",
|
||
"Epoch 14/15\n",
|
||
"Train Loss: 1.2288 | Val Loss: 4.9791 | PPL: 145.34\n",
|
||
"Time: 804.86s | ETA: 13.41m\n",
|
||
"------------------------------\n",
|
||
"Epoch 15/15\n",
|
||
"Train Loss: 1.1252 | Val Loss: 5.1356 | PPL: 169.97\n",
|
||
"Time: 804.99s | ETA: 0.00m\n",
|
||
"------------------------------\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 7
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"source": [
|
||
"### Model loop with LORA"
|
||
],
|
||
"metadata": {
|
||
"id": "579d53bb4bfa662d"
|
||
}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"model_lora = GPT2LMHeadModel.from_pretrained(\"gpt2\")\n",
|
||
"model_lora.resize_token_embeddings(len(tokenizer))\n",
|
||
"\n",
|
||
"lora_config = LoraConfig(r=8, lora_alpha=32, target_modules=[\"c_attn\"], lora_dropout=0.1, bias=\"none\", task_type=\"CAUSAL_LM\")\n",
|
||
"model_lora = get_peft_model(model_lora, lora_config)\n",
|
||
"\n",
|
||
"training_dataset.set_format(type=\"torch\")\n",
|
||
"training_data = DataLoader(training_dataset[\"train\"], batch_size=2, shuffle=True)\n",
|
||
"testing_data = DataLoader(training_dataset[\"test\"], batch_size=2, shuffle=True)\n",
|
||
"optimiser = optim.AdamW(filter(lambda p: p.requires_grad, model_lora.parameters()), lr=5e-5)\n",
|
||
"\n",
|
||
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
|
||
"model_lora.to(device)\n",
|
||
"model_lora.print_trainable_parameters()"
|
||
],
|
||
"metadata": {
|
||
"trusted": true,
|
||
"id": "cc3011ec1a4165d5",
|
||
"outputId": "1f8e4f20-aeb2-4938-aee9-2b00907aa3eb",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 258,
|
||
"referenced_widgets": [
|
||
"029c185360a64c6794727b4f7f761fd6",
|
||
"fc25645d5afe420ea83f2af9cc7536f8",
|
||
"939fb81f762448dcb8c6c52eb3d27afe",
|
||
"fd26feeb51d14c57a08520c9ba864184",
|
||
"66968ff20f884c51aa96230c02f5496e",
|
||
"01ddbc29311f416ca97f0bb7b7277280",
|
||
"ffa3e6b8168941b9be6e35e1ad90da74",
|
||
"70c2152fffb648ada5c239c997f41397",
|
||
"fc497251b3fa4d07818fa9ac2745931a",
|
||
"0a832b055ad844a085767ebeebbbb4b2",
|
||
"ffbed32a4c3c4428b96f73675330789d"
|
||
]
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Loading weights: 0%| | 0/148 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "029c185360a64c6794727b4f7f761fd6"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"GPT2LMHeadModel LOAD REPORT from: gpt2\n",
|
||
"Key | Status | | \n",
|
||
"---------------------+------------+--+-\n",
|
||
"h.{0...11}.attn.bias | UNEXPECTED | | \n",
|
||
"\n",
|
||
"Notes:\n",
|
||
"- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
|
||
"/usr/local/lib/python3.12/dist-packages/peft/tuners/lora/layer.py:2285: UserWarning: fan_in_fan_out is set to False but the target module is `Conv1D`. Setting fan_in_fan_out to True.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"trainable params: 294,912 || all params: 124,734,720 || trainable%: 0.2364\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 8
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"num_epochs = 15\n",
|
||
"total_start = time.time()\n",
|
||
"\n",
|
||
"for epoch in range(num_epochs):\n",
|
||
" start = time.time()\n",
|
||
"\n",
|
||
" model_lora.train()\n",
|
||
" train_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",
|
||
" optimiser.zero_grad()\n",
|
||
" outputs = model_lora(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
|
||
" loss = outputs.loss\n",
|
||
" loss.backward()\n",
|
||
" optimiser.step()\n",
|
||
"\n",
|
||
" train_loss += loss.item()\n",
|
||
"\n",
|
||
" model_lora.eval()\n",
|
||
" validation_loss = 0.0\n",
|
||
"\n",
|
||
" with torch.no_grad():\n",
|
||
" for batch in testing_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",
|
||
" outputs = model_lora(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
|
||
" validation_loss += outputs.loss.item()\n",
|
||
"\n",
|
||
" avg_train_loss = train_loss / len(training_data)\n",
|
||
" avg_val_loss = validation_loss / len(testing_data)\n",
|
||
" perplexity = torch.exp(torch.tensor(avg_val_loss))\n",
|
||
"\n",
|
||
" if device.type == \"cuda\":\n",
|
||
" torch.cuda.synchronize()\n",
|
||
"\n",
|
||
" epoch_time = time.time() - start\n",
|
||
" avg_time_per_epoch = (time.time() - total_start) / (epoch + 1)\n",
|
||
" eta = avg_time_per_epoch * (num_epochs - epoch - 1)\n",
|
||
"\n",
|
||
" print(f\"Epoch {epoch+1}/{num_epochs}\")\n",
|
||
" print(f\"Train Loss: {avg_train_loss:.4f} | Val Loss: {avg_val_loss:.4f} | PPL: {perplexity:.2f}\")\n",
|
||
" print(f\"Time: {epoch_time:.2f}s | ETA: {eta/60:.2f}m\")\n",
|
||
" print(\"-\" * 30)"
|
||
],
|
||
"metadata": {
|
||
"id": "9bb57749d5521287",
|
||
"outputId": "3296833b-9c9e-4390-c543-b8aa3c1bbcbd",
|
||
"trusted": true,
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Epoch 1/15\n",
|
||
"Train Loss: 3.8060 | Val Loss: 3.5381 | PPL: 34.40\n",
|
||
"Time: 412.03s | ETA: 96.14m\n",
|
||
"------------------------------\n",
|
||
"Epoch 2/15\n",
|
||
"Train Loss: 3.6866 | Val Loss: 3.5079 | PPL: 33.38\n",
|
||
"Time: 412.21s | ETA: 89.29m\n",
|
||
"------------------------------\n",
|
||
"Epoch 3/15\n",
|
||
"Train Loss: 3.6561 | Val Loss: 3.4930 | PPL: 32.88\n",
|
||
"Time: 411.43s | ETA: 82.38m\n",
|
||
"------------------------------\n",
|
||
"Epoch 4/15\n",
|
||
"Train Loss: 3.6382 | Val Loss: 3.4855 | PPL: 32.64\n",
|
||
"Time: 411.14s | ETA: 75.48m\n",
|
||
"------------------------------\n",
|
||
"Epoch 5/15\n",
|
||
"Train Loss: 3.6259 | Val Loss: 3.4784 | PPL: 32.41\n",
|
||
"Time: 410.35s | ETA: 68.57m\n",
|
||
"------------------------------\n",
|
||
"Epoch 6/15\n",
|
||
"Train Loss: 3.6153 | Val Loss: 3.4735 | PPL: 32.25\n",
|
||
"Time: 411.17s | ETA: 61.71m\n",
|
||
"------------------------------\n",
|
||
"Epoch 7/15\n",
|
||
"Train Loss: 3.6073 | Val Loss: 3.4697 | PPL: 32.13\n",
|
||
"Time: 410.98s | ETA: 54.84m\n",
|
||
"------------------------------\n",
|
||
"Epoch 8/15\n",
|
||
"Train Loss: 3.6001 | Val Loss: 3.4660 | PPL: 32.01\n",
|
||
"Time: 409.84s | ETA: 47.97m\n",
|
||
"------------------------------\n",
|
||
"Epoch 9/15\n",
|
||
"Train Loss: 3.5934 | Val Loss: 3.4637 | PPL: 31.94\n",
|
||
"Time: 410.01s | ETA: 41.10m\n",
|
||
"------------------------------\n",
|
||
"Epoch 10/15\n",
|
||
"Train Loss: 3.5883 | Val Loss: 3.4634 | PPL: 31.92\n",
|
||
"Time: 411.50s | ETA: 34.26m\n",
|
||
"------------------------------\n",
|
||
"Epoch 11/15\n",
|
||
"Train Loss: 3.5840 | Val Loss: 3.4594 | PPL: 31.80\n",
|
||
"Time: 410.61s | ETA: 27.40m\n",
|
||
"------------------------------\n",
|
||
"Epoch 12/15\n",
|
||
"Train Loss: 3.5789 | Val Loss: 3.4600 | PPL: 31.82\n",
|
||
"Time: 410.77s | ETA: 20.55m\n",
|
||
"------------------------------\n",
|
||
"Epoch 13/15\n",
|
||
"Train Loss: 3.5754 | Val Loss: 3.4585 | PPL: 31.77\n",
|
||
"Time: 409.52s | ETA: 13.70m\n",
|
||
"------------------------------\n",
|
||
"Epoch 14/15\n",
|
||
"Train Loss: 3.5717 | Val Loss: 3.4571 | PPL: 31.73\n",
|
||
"Time: 411.73s | ETA: 6.85m\n",
|
||
"------------------------------\n",
|
||
"Epoch 15/15\n",
|
||
"Train Loss: 3.5687 | Val Loss: 3.4573 | PPL: 31.73\n",
|
||
"Time: 411.14s | ETA: 0.00m\n",
|
||
"------------------------------\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 9
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"output_dir = \"./gpt2_finetuned\"\n",
|
||
"model_full.save_pretrained(output_dir)\n",
|
||
"\n",
|
||
"tokenizer.save_pretrained(output_dir)\n",
|
||
"\n",
|
||
"print(f\"Model saved to {output_dir}\")"
|
||
],
|
||
"metadata": {
|
||
"trusted": true,
|
||
"id": "9a4b279a-3502-47f0-aff5-b25e7d008c90",
|
||
"outputId": "92bd979a-d9d8-4e7f-8938-e8bafd5b8082",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 68,
|
||
"referenced_widgets": [
|
||
"56c0e004828e40ad87e9acd0c41b91bd",
|
||
"cf07155125e7456aae02a235134cf925",
|
||
"c392608bf81748bf9d7b5b3ea277efa2",
|
||
"8763fceb841d4efc9a57a83743b598b9",
|
||
"eae3f1ed9cb044798fc3531279ae2402",
|
||
"c1cb6dcb8f32497eb61022610e42141f",
|
||
"a02be6dacebe41e39110cb2c5af8d5f0",
|
||
"d5ea266225764785bf745505a8055d55",
|
||
"6e693b9020b7464f8d6eb07d2200ee19",
|
||
"7f5842d17ee54d4d94f69a46a5bfa70a",
|
||
"b4055b28fb114e07ab533ded0a72e26f"
|
||
]
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Writing model shards: 0%| | 0/1 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "56c0e004828e40ad87e9acd0c41b91bd"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Model saved to ./gpt2_finetuned\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 10
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"shutil.make_archive('model_output_15_epochs', 'zip', './gpt2_finetuned')\n",
|
||
"\n",
|
||
"print(\"Model zipped and ready for download!\")"
|
||
],
|
||
"metadata": {
|
||
"trusted": true,
|
||
"id": "00f683d7-c8fd-47ac-804c-4fccd4c51bb7",
|
||
"outputId": "d75604ab-5129-4de1-e416-872ae7590b39",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Model zipped and ready for download!\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 11
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"output_dir = \"./gpt2_lora\"\n",
|
||
"print(f\"Merging weights and saving to {output_dir}...\")\n",
|
||
"merged_model = model_lora.merge_and_unload()\n",
|
||
"merged_model.save_pretrained(output_dir)\n",
|
||
"tokenizer.save_pretrained(output_dir)\n",
|
||
"shutil.make_archive('model_output_15_epochs_w_lora', 'zip', './gpt2_lora')\n",
|
||
"print(\"Done! You now have a standalone GPT-2 model.\")"
|
||
],
|
||
"metadata": {
|
||
"trusted": true,
|
||
"id": "75e43852-b6ce-4d7a-a266-dd96e7077e73",
|
||
"outputId": "f6fcca5a-d2ec-4db2-b9c1-3f27d8c0a358",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 87,
|
||
"referenced_widgets": [
|
||
"aa8fddd0912d4bc0b99b4a26ab89a73d",
|
||
"073bfbac220a43c580b2f6774d2c599a",
|
||
"7434c7d58b634391b01d79b5630b1d16",
|
||
"496e25d657db41669009142639b2276f",
|
||
"bc88931dfb5b4ad2af295a60e74ab57b",
|
||
"9edfe615e40f4557801067e32fc74824",
|
||
"3efdaf49ce9d4e58bb86637fc7ea0f8f",
|
||
"4ab9527cebdf4c61a69a9e33d759a667",
|
||
"8ea7bf574b6d44f188a4a00e397939dd",
|
||
"072f26b0d2664c45b5086cba65f31b8c",
|
||
"a06aabadff6c4fdcb7d1eed6f48f3e00"
|
||
]
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Merging weights and saving to ./gpt2_lora...\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"Writing model shards: 0%| | 0/1 [00:00<?, ?it/s]"
|
||
],
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"version_major": 2,
|
||
"version_minor": 0,
|
||
"model_id": "aa8fddd0912d4bc0b99b4a26ab89a73d"
|
||
}
|
||
},
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"Done! You now have a standalone GPT-2 model.\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 12
|
||
}
|
||
]
|
||
} |