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erbengpt.ipynb
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"os.environ['LC_ALL'] = 'en_US.UTF-8'\n",
"os.environ['LANG'] = 'en_US.UTF-8'\n",
"\n",
"# Now you can run your pip install command\n",
"!pip install numpy\n",
"!pip install transformers[torch]\n",
"!pip install accelerate -U\n",
"!pip install datasets\n",
"3!pip install wandb"
],
"metadata": {
"id": "Ugpaz2bxBXOg"
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},
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"import torch"
],
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"id": "w1QoBLGuKZ_g"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
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],
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"id": "9dtVNU0F6hK9"
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"cell_type": "code",
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"from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
"\n",
"model_name = \"gpt2\" # You can also try \"gpt2-medium\", \"gpt2-large\", or \"gpt2-xl\"\n",
"model = GPT2LMHeadModel.from_pretrained(model_name)\n",
"tokenizer = GPT2Tokenizer.from_pretrained(model_name)"
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"source": [
"import requests\n",
"\n",
"url_kytice = \"https://gist.githubusercontent.com/aleksejalex/3d6efa777dbb9759bb02b0d2933438cc/raw/fbe305f8f3729352ebbaf802fcd49f38b4292a83/kytice.txt\"\n",
"output_file = 'kytice.txt'\n",
"\n",
"response = requests.get(url_kytice)\n",
"\n",
"if response.status_code == 200:\n",
" with open(output_file, 'wb') as f:\n",
" f.write(response.content)\n",
" print(f\"File downloaded successfully and saved as {output_file}\")\n",
"else:\n",
" print(f\"Failed to download file. Status code: {response.status_code}\")"
],
"metadata": {
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"base_uri": "https://localhost:8080/"
},
"id": "GFoEqYFczb_L",
"outputId": "291761a2-9046-429c-9042-d79e60e61664"
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"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"File downloaded successfully and saved as kytice.txt\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Read your data from the .txt file\n",
"with open(\"kytice.txt\", \"r\", encoding=\"utf-8\") as file:\n",
" poems = file.read()\n",
"\n",
"# Tokenize the data\n",
"tokenized_poems = tokenizer(poems, return_tensors=\"pt\", max_length=512, truncation=True)"
],
"metadata": {
"id": "R7UQUuFDzW2B"
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"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
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},
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"execution_count": null,
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"output_type": "stream",
"name": "stdout",
"text": [
"Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
"Token is valid (permission: write).\n",
"Your token has been saved to /root/.cache/huggingface/token\n",
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{
"cell_type": "code",
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"from transformers import TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments\n",
"\n",
"dataset = TextDataset(\n",
" tokenizer=tokenizer,\n",
" file_path=\"kytice.txt\",\n",
" block_size=256 # Adjust this based on the average length of your poems\n",
")\n",
"\n",
"data_collator = DataCollatorForLanguageModeling(\n",
" tokenizer=tokenizer,\n",
" mlm=False\n",
")\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./ErbenGPT\",\n",
" overwrite_output_dir=False, # bylo tu True...\n",
" num_train_epochs=28, # 20 or 28 - nice\n",
" per_device_train_batch_size=15,\n",
" save_steps=10_000,\n",
" save_total_limit=2,\n",
" #output_dir=\"my-awesome-model\",\n",
" #push_to_hub=True\n",
")\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" data_collator=data_collator,\n",
" train_dataset=dataset\n",
")\n",
"\n",
"# Log hyperparameters to wandb\n",
"wandb.config.update(training_args.__dict__)\n",
"wandb.config.update({\"model_name\": \"erben_gpt2\"}) # Add any additional hyperparameters\n",
"wandb.config.update({\"num_train_epochs\": training_args.num_train_epochs})\n",
"wandb.config.update({\"per_device_train_batch_size\": training_args.per_device_train_batch_size})\n",
"wandb.config.update({\"tba\": None})\n",
"\n",
"trainer.train()\n",
"\n",
"torch.save(model.state_dict(), 'ErbenGPT_model.pth')\n",
"#artifact = wandb.Artifact('model', type='model')\n",
"#artifact.add_file('ErbenGPT_model.pth')\n",
"#run.log_artifact(artifact)\n",
"\n",
"#train_losses = trainer.state.log_history[\"loss\"]\n",
"#plt.plot(train_losses, label='Training Loss')\n",
"#plt.xlabel('Epoch or Step')\n",
"#plt.ylabel('Loss')\n",
"#plt.title('Training Loss Over Time')\n",
"#plt.legend()\n",
"#plt.show()\n",
"\n",
"wandb.finish()"
],
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" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
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" .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
" </style>\n",
"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁</td></tr><tr><td>train/global_step</td><td>▁</td></tr><tr><td>train/total_flos</td><td>▁</td></tr><tr><td>train/train_loss</td><td>▁</td></tr><tr><td>train/train_runtime</td><td>▁</td></tr><tr><td>train/train_samples_per_second</td><td>▁</td></tr><tr><td>train/train_steps_per_second</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>28.0</td></tr><tr><td>train/global_step</td><td>364</td></tr><tr><td>train/total_flos</td><td>662114009088000.0</td></tr><tr><td>train/train_loss</td><td>3.0831</td></tr><tr><td>train/train_runtime</td><td>348.8076</td></tr><tr><td>train/train_samples_per_second</td><td>14.53</td></tr><tr><td>train/train_steps_per_second</td><td>1.044</td></tr></table><br/></div></div>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
" View run <strong style=\"color:#cdcd00\">fancy-pine-8</strong> at: <a href='https://wandb.ai/sampleteam/erbenGPT/runs/q2atmyu2' target=\"_blank\">https://wandb.ai/sampleteam/erbenGPT/runs/q2atmyu2</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"Find logs at: <code>./wandb/run-20231205_143218-q2atmyu2/logs</code>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"trainer.state.log_history"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DbzphQ3JOYv_",
"outputId": "83dec880-1c7b-408e-a3b0-a7a5e0eee7b1"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'train_runtime': 348.8076,\n",
" 'train_samples_per_second': 14.53,\n",
" 'train_steps_per_second': 1.044,\n",
" 'total_flos': 662114009088000.0,\n",
" 'train_loss': 3.083096682370364,\n",
" 'epoch': 28.0,\n",
" 'step': 364}]"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"# Save the fine-tuned model\n",
"model.save_pretrained(\"./ErbenGPT\")"
],
"metadata": {
"id": "7znRWG-0z-gz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# PUSHING TOKENIZER TO HUGGINGFACE"
],
"metadata": {
"id": "24LXRrbCpF2l"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# PUSHING MODEL TO HUGGINGFACE"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 164,
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"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Upload 3 LFS files: 0%| | 0/3 [00:00<?, ?it/s]"
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"data": {
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"'https://huggingface.co/aleksejalex/ErbenGPT/tree/main/'"
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"type": "string"
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"metadata": {},
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]
},
{
"cell_type": "code",
"source": [
"# Step 6: Generate Poetry\n",
"prompt = \"Dej sem dítě\"\n",
"\n",
"# Tokenize prompt and move to the same device as the model\n",
"input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(model.device)\n",
"\n",
"# Generate poetry\n",
"generated_poem = model.generate(\n",
" input_ids,\n",
" max_length=150, # Adjust as needed\n",
" temperature=0.75, # Experiment with different temperature values\n",
" num_return_sequences=3,\n",
" no_repeat_ngram_size=0,\n",
" top_k=25,\n",
" top_p=0.95,\n",
" pad_token_id=tokenizer.eos_token_id,\n",
" do_sample=True\n",
")\n",
"\n",
"generated_text = tokenizer.decode(generated_poem[0], skip_special_tokens=True)\n",
"print(generated_text)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PvX_egZf1YKP",
"outputId": "c3c0413c-f117-43e2-8887-2b522877e453"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dej sem dítě —\n",
"sám tak to příže měsí:\n",
"a tak to měsí, kde mi —\n",
"však jemu ještě příže,\n",
"však jednou děšné věží,\n",
"a však jedna, však jedna!“\n",
"\n",
"Tak, to má, to má má;\n",
"tu jí příže větě, to kde kde,\n",
"a však jedna\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"generated_text = tokenizer.decode(generated_poem[1], skip_special_tokens=True)\n",
"print(generated_text)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "EBlVC1ETYsXB",
"outputId": "dc4b5667-5dcf-42cb-884e-4b4fc66ab4ff"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dej sem dítě — však sám,\n",
"však již pachole vodní.\n",
"\n",
"„Odej se čas na viděláv:\n",
"všej, jsem dítě — však sám,\n",
"však již pachole vodní.“ —\n",
"\n",
"„Záhořím již na svatém pahouti?\n",
"\n",
"Nešejte již kostela v kostela?“\n",
"\n",
"„Ach, kde jsem,\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"generated_text = tokenizer.decode(generated_poem[2], skip_special_tokens=True)\n",
"print(generated_text)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hF3_ueYVUVwR",
"outputId": "c268179c-dd7c-44f1-8c84-c7216c002e2f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dej sem dítě nejte vrč,\n",
"vzdlovavrá již a sotva jeho kost:\n",
"„Nedá v růží, žírá na neslý,\n",
"v ždycka vůru křeča v zemi klín,\n",
"v dřítko vůru jsem klín,\n",
"a zemi lého znamení\n",
"a dne vůru jsem znamení!“\n",
"\n",
"A když a zemi se klín,\n",
"zd\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"generated_text = tokenizer.decode(generated_poem[0], skip_special_tokens=True)\n",
"print(generated_text)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "W1Uz3hacUVbY",
"outputId": "d366dedd-c339-4fdf-cd72-197fcdca1192"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Dej sem dítě —\n",
"sám tak to příže měsí:\n",
"a tak to měsí, kde mi —\n",
"však jemu ještě příže,\n",
"však jednou děšné věží,\n",
"a však jedna, však jedna!“\n",
"\n",
"Tak, to má, to má má;\n",
"tu jí příže větě, to kde kde,\n",
"a však jedna\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "UZqi4c4qUVDv"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"first, raw result:\n",
"\n",
"prompt = `Your prompt goes here.`\n",
"\n",
"answer =\n",
"```\n",
"Ach, mé, ach!\n",
"Má, kde, milá!“\n",
"„Aj, toho, již, ještě,\n",
"jaké má mď! —\n",
"Jak, čám, řím! ší, zaře!\n",
"Kdo, dČtí! kdo! dát! Čte! jak! její — ※\n",
"a kdyť, nevĈ,? „Kdychůj! zlato, pojľ!?\n",
"```"
],
"metadata": {
"id": "Ungfy1oT1ha0"
}
},
{
"cell_type": "markdown",
"source": [
"second result (batch increased, 28 epochs):\n",
"\n",
"prompt = `Stála`\n",
"\n",
"answer =\n",
"```\n",
"Stála,\n",
"že mi se vrátil a na žel nízkaly\n",
"a zas do hodiny mějí.\n",
"\n",
"A hledal a vĚč, ale klečil, —\n",
"„Ach mé dítĽch oči!“\n",
"Klekáním dáli tváři?\n",
"Vġt�íť mi šál jiů všeň:\n",
"tvám to bohabátí, buch bęeš,\n",
"buch! buhabatuješ!\n",
"```"
],
"metadata": {
"id": "CYSoUaeV3rfQ"
}
},
{
"cell_type": "markdown",
"source": [
"third result (batch increased, 28 epochs):\n",
"\n",
"prompt = `Dej sem dítě`\n",
"\n",
"answer =\n",
"```\n",
"Dej sem dítě,\n",
"až i vyskům jiť poutník:\n",
"„Haha, mama, co vidí!“\n",
"vyskaře, jako vĚčný, —\n",
"hle, hle! Hleď!\n",
"\n",
"Vydání, vydá, drahé, aň, královském,\n",
"pœíchámĝí mġlo, pōímálo! \n",
"\n",
"\n",
"IV\n",
"I\n",
"A kdy\n",
"```"
],
"metadata": {
"id": "6nGqXSb734TV"
}
},
{
"cell_type": "markdown",
"source": [
"third result (batch increased, 28 epochs):\n",
"\n",
"prompt = `Dej sem dítě`\n",
"\n",
"params =\n",
"```\n",
"generated_poem = model.generate(\n",
" input_ids,\n",
" max_length=150, # Adjust as needed\n",
" temperature=0.75, # Experiment with different temperature values\n",
" num_return_sequences=1,\n",
" no_repeat_ngram_size=0,\n",
" top_k=50,\n",
" top_p=0.95,\n",
" pad_token_id=tokenizer.eos_token_id,\n",
" do_sample=True\n",
")\n",
"```\n",
"\n",
"answer =\n",
"```\n",
"Dej sem dítě říkou!\n",
"Vchodám, vchodá, v křítíká,\n",
"hraděčí sotva zvonu zvonu,\n",
"hrobuje černého poutník.\n",
"\n",
"A dítě slyš, a sotva již:\n",
"„Neba mám, nezpátíká,\n",
"jako v něstném sotva již!“\n",
"\n",
"Tu někřikých rok se tíkou\n",
"```"
],
"metadata": {
"id": "Modn7jhQ9dkf"
}
},
{
"cell_type": "markdown",
"source": [
"```\n",
"Dej sem dítě!“ —\n",
"\n",
"„Sviťte a po ten čas, že svěš a krati,\n",
"a na tak ruka za svěš a krati\n",
"a přišli zas na svávaje:\n",
"„Jedno po ten čas, že svatba,\n",
"a na krati zas na zemi báti\n",
"a ní muka zase zpět!“\n",
"\n",
"„Zaseť se, jaký bože, všeho se krati\n",
"```"
],
"metadata": {
"id": "pJeFF62AAVDO"
}
},
{
"cell_type": "markdown",
"source": [
"```\n",
"Dej sem dítě —\n",
"sám tak to příže měsí:\n",
"a tak to měsí, kde mi —\n",
"však jemu ještě příže,\n",
"však jednou děšné věží,\n",
"a však jedna, však jedna!“\n",
"\n",
"Tak, to má, to má má;\n",
"tu jí příže větě, to kde kde,\n",
"a však jedna\n",
"```"
],
"metadata": {
"id": "KShawiO6miVx"
}
}
]
}
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