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"output_type": "stream", "name": "stdout", "text": [ "Collecting datasets\n", " Downloading datasets-3.0.1-py3-none-any.whl.metadata (20 kB)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n", "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (16.1.0)\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/python3.10/dist-packages (from datasets) (2.2.2)\n", "Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n", "Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.5)\n", "Collecting xxhash (from datasets)\n", " 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"https://localhost:8080/", "height": 373 }, "id": "Az9vxNVzR1tI", "outputId": "07fdbab4-3864-450d-ae76-1ab31e401ba8" }, "outputs": [ { "output_type": "error", "ename": "FileNotFoundError", "evalue": "[Errno 2] No such file or directory: '/content/llm_dataset.csv'", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# Load the CSV file into a pandas DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m 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doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m 1024\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\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 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 619\u001b[0m \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\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 621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 622\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\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/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1619\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\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-> 1620\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\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 1621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1622\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 871\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 872\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 873\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 874\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 875\u001b[0m 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T5ForConditionalGeneration.from_pretrained(model_name)\n", "\n", "# Tokenization function for the dataset\n", "def preprocess_function(examples):\n", " inputs = [f\"question: {q}\" for q in examples['Question']]\n", " model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding=\"max_length\", return_tensors=\"pt\")\n", "\n", " # Prepare the target (answer) as well\n", " labels = tokenizer(examples['Answer'], max_length=512, truncation=True, padding=\"max_length\", return_tensors=\"pt\").input_ids\n", " model_inputs[\"labels\"] = labels\n", " return model_inputs\n", "\n", "# Preprocess the dataset\n", "tokenized_dataset = dataset.map(preprocess_function, batched=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 414, "referenced_widgets": [ "9859b486c04347e28dfec5913018b101", "86399b590f214ee9aa8d27d1b35b7da6", "3996d167f691437f9d983e51d047fde4", "f431499e63214c50955520b6633921db", "8bae692549c04489a47e1c80aef4c075", 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"e9f12a6dcc374a5893971cf01e303dda", "2a03376bbdc9418ba993d4e45ecdecae" ] }, "id": "lw3XTZ_cSH6Q", "outputId": "2bbe39c1-2eae-4023-f8d5-b92e197ae40d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/2.32k [00:00. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.21k [00:00" ], "text/html": [ "\n", "
\n", " \n", " \n", " [45/45 00:31, Epoch 3/3]\n", "
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EpochTraining LossValidation Loss
1No log9.192511
2No log6.174387
3No log5.171976

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=45, training_loss=7.266212972005208, metrics={'train_runtime': 36.7673, 'train_samples_per_second': 9.383, 'train_steps_per_second': 1.224, 'total_flos': 46692921507840.0, 'train_loss': 7.266212972005208, 'epoch': 3.0})" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "code", "source": [ "import torch\n", "\n", "def get_answer(question):\n", " # Preprocess the input question\n", " inputs = tokenizer(f\"question: {question}\", return_tensors=\"pt\", padding=\"max_length\", truncation=True, max_length=512)\n", "\n", " # Move the input tensors to the same device as the model\n", " # Assuming your model is on CUDA\n", " inputs = inputs.to(model.device) # Move input tensors to the model's device\n", "\n", " # Generate the answer\n", " outputs = model.generate(inputs.input_ids, max_length=512, num_beams=4, early_stopping=False)\n", "\n", " # Decode the generated answer\n", " answer = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", " return answer\n", "\n", "# Example usage\n", "question = \"What are the symptoms of Diverticulosis?\"\n", "answer = get_answer(question)\n", "print(f\"Question: {question}\\nAnswer: {answer}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "B3UAWAgTSYhp", "outputId": "e56c22c6-e43b-4c82-a5a4-5a6f1a6d4d66" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Question: What are the symptoms of Diverticulosis?\n", "Answer: Diverticulosis\n" ] } ] }, { "cell_type": "markdown", "source": [ "### GPT 2" ], "metadata": { "id": "gDaAa34WW-rB" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "from datasets import Dataset\n", "\n", "# Load the CSV file into a pandas DataFrame\n", "df = pd.read_csv('/content/llm_dataset.csv')\n", "\n", "# Concatenate question and answer to form a single text prompt\n", "df['text'] = df['Question'] + \" Answer: \" + df['Answer']\n", "\n", "# Convert the pandas DataFrame to Hugging Face's Dataset format\n", "dataset = Dataset.from_pandas(df)\n", "dataset = dataset.train_test_split(test_size=0.2) # Split the dataset into train and test sets\n" ], "metadata": { "id": "X4ii8FCBXT0b" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n", "\n", "# Load GPT-2 tokenizer and model\n", "model_name = \"gpt2\"\n", "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n", "\n", "# GPT-2 does not have padding tokens, so we add one\n", "tokenizer.pad_token = tokenizer.eos_token\n", "\n", "# Load the GPT-2 model with language modeling head\n", "model = GPT2LMHeadModel.from_pretrained(model_name)\n", "\n", "# Tokenization function for the dataset\n", "def preprocess_function(examples):\n", " inputs = [q for q in examples['text']]\n", " model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding=\"max_length\", return_tensors=\"pt\")\n", "\n", " # Add labels - shifted input_ids\n", " model_inputs[\"labels\"] = model_inputs[\"input_ids\"].clone()\n", "\n", " return model_inputs\n", "\n", "# Preprocess the dataset\n", "tokenized_dataset = dataset.map(preprocess_function, batched=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 464, "referenced_widgets": [ "17bd0779af754c938e8fd733e700666f", "9b35c2fe89674e78bc9b2e2a00ce37c6", "67905730a0614c019e20c1e92cdf6083", "008bb028b0cb447a87da202e469cdfa7", "655e33d8e1c3428db80d8968a8faefc6", "777b583edf67498e86abee1f64e87ec4", "20dd861b5898422283d7470298cfff99", "6edc7f271b324ee6b6b76dc76efa615e", "3d9852493bed4fa39266bc836d6be17d", "776af3eb1a724ce38f21fd922a6dcfd5", "424d3b00d60f4132aae2796c3a9868e9", "18f1b68686fb41bd98edc9cb14d502ee", "cfaf58c22ab942a2bb83cf7f0a765056", "96b7b9636ff9429bb46dc4f5a884daeb", "8a721ad0db4b4bfcbe1d96835500f586", "999a4c446296482fa42b872ed2f93301", "d11e7150befb428b86d54720551da21a", "4b64b7b375e741a881cac5c75d5ddd6b", "2aa0a6b366814c77a0f43137197f5095", "1ff7cc21199c44e4903fc13b8b0f08b4", "583e21445f14446cbde7bbac5c60436c", "3a473fcf4ee74b8e98409e0079a02ff9", "ac91fcb2df1c4c94a1642ebc71448747", "d411ab38b0ab427caf5f6f0f3a3dcd78", "95467f01637c45b383d3bf91ea9f104e", "3ea81101729b42409bed7840b1639cbd", "be446f1ef0b84dbcbdbe9908fc43d5cb", "7cc39bc116b14663a58e33806b9f17db", "d98616e7b33a47f29c9c66f2ee9b815e", "1d1f9b6f0daf44fba6ac715b4773b5dd", "dfefe33d68fc459799d3ec6961bb4499", "ce2080b13d8c4135ab862c09abb8d583", "8dd2d9c651984f64af3185f8ae4eec6c", "ce65ebdaf25b47c1a3c63d2b65239697", "35027cf827964d35bba45cd4e320c95e", "35ca0da2d4fc41b0b7b45993412b845e", "c340f234a892404b9b0b1e8ae0a4b511", "4308c47509254513a72dd507a9086bac", "cdf9ff6e994b483ba72e1fa0f5cd1cf4", "950c72b9304142e8867a9309068ea0f4", "c3b6235cc97b48d0b51a9a0ea400db3b", "f7233b9f11d54696aa09d2b7f044f719", "43a42d97852348319393ca557e451721", "024ecdde46df411fb93fe8b7d7b2987f", "09c1883c4c2e45d3a04a9bb3ac16de62", "34dcb8f228b246da98cb00a7c4605ef4", "a2bffb93faa547a1934cc89026d34954", "3dd8cf2443c84f0b8a2f9b5603004e33", "2e89e8b19a6f4f148ac4c3beeed8ccf3", "55298fdaacf1474880774a354fb79c81", "3abe9893db2740fdabbc6cd51c6877de", "f6b514d796f64ecbb9a33c952ae2c847", "90fc9779210f457f8f832ade5c7e46af", "4d90bbc47fd44b4e942b29bf5ed77a3c", "22c71fdb5f51417ea883614adc0c69ba", "44c8cbdc465b42f1b4da6a9df21eb769", "5decee8fdb244aada345aaf303e5f709", "18ee94f7f5e64ec7be500a1818ea2a56", "79089cfc59754ec4a7e8362bccc95273", "948283cbd10f44c18744d3b6754c3fbf", "91b1a0dd0b584bb9b6197eda1660206c", "9f03ecb80cfa423fa29a83d75ee83529", "2406ebf17d39474d879e140d9c18baa9", "9797e785b2c44c8a931c789d46d88eb9", "2218ee3b1d4f4b4b9702ad82b2fd7276", "7f9f570314ca4d7c9014693f7e72b73d", "21eb78e2590247518d7ddfad1d632c22", "74dbf334a02b4987a8ac40a48fead625", "aad60a25b4364558b7e36b9a717bc7a5", "8c9e7e5254aa4015a420fc2d57f3edd4", "319e5c1f89c34b0fbe47c29deeaa5a2e", "d7941771fc2c4084b6d48e8aeebb35e8", "f078217ee39e4d0a9b20a8b4e0bb029d", "c8c2293c93b94d688360b66061d18632", "30f14736b16f4091b8a2a5c17b268dd3", "ba1c00a81d0d49e9998eea4d059e1b3a", "3c5e66bb6f03485489c542370e6f488f", "8c5ce7dab5c64a87858fc01d96e00387", "0142e267433e40ccb3e86187bf024e9d", "05bcf9ffdbaa41ebaee9f318b5c88417", "3197ec0a28c64689bbe44c0a065680b0", "f5325bb8f2a2437f8ce1b69cb503c2b6", "b89a9d55cec04870a0eb3f522d0350c4", "e109c387f3f0431da7d1f836d432023b", "687c9ba3d0e549f0ab761058f56d560b", "e411aba033d94fa39694a88754fd6002", "06ffe6a60c444a11bbfabae1d7f33b8c", "1ccd43ac585b4fdaafe261dcc56bb16a", "1faaae4b07774d9099c788d69727ee18", "508744470fc14d7ab90c1098ec0b07d6", "c8d017d47d9a42da8d14b6a3674badcb", "fa63619a420d4670a0c25904f98f84b2", "00395e0f1b3d44b2a8614de905c6f063", "a9dc5ec8f21f4eb784469ec238493693", "7b8bc555d0ea4fc2b469f0a6c3aeb472", "8b19d16984924aac8a500197ab147b3d", "babf19cea05a448988d9d92bfac52d32", "ada0f790bb5c4e78b1502cd0543e8dde", "260f94003fa14d3296dbf520fbd43e30" ] }, "id": "H8Yp8IgOWPzT", "outputId": "713bb738-f141-48d0-89ac-f405b0b9a3ca" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/26.0 [00:00" ], "text/html": [ "\n", "

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EpochTraining LossValidation Loss
1No log0.273149
2No log0.252079
3No log0.243375
40.4299000.239692
50.4299000.242480
60.4299000.246378
70.1486000.252990
80.1486000.256806
90.1486000.267002
100.1486000.265200
110.0937000.280149
120.0937000.282521
130.0937000.286830
140.0640000.291562
150.0640000.293053
160.0640000.300838
170.0640000.300023
180.0485000.302560
190.0485000.302693
200.0485000.302897

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=580, training_loss=0.14133180749827418, metrics={'train_runtime': 390.3092, 'train_samples_per_second': 5.893, 'train_steps_per_second': 1.486, 'total_flos': 600971673600000.0, 'train_loss': 0.14133180749827418, 'epoch': 20.0})" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "code", "source": [ "def get_answer(question):\n", " # Preprocess the input question\n", " input_text = question + \" Answer:\"\n", " inputs = tokenizer(input_text, return_tensors=\"pt\", max_length=512, truncation=True)\n", "\n", " # Move input tensor\n", "\n", " inputs = inputs.to(model.device) # Move input tensors to the GPU if model is on the GPU\n", "\n", " # Generate the answer from the model\n", " # Change: Use tokenizer.eos_token_id instead of tokenizer.eos_token\n", " outputs = model.generate(inputs.input_ids, max_length=512, num_beams=5, early_stopping=True, pad_token_id=tokenizer.eos_token_id)\n", "\n", " # Decode the generated answer\n", " answer = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", "\n", " # Remove the question part from the answer (if included)\n", " answer = answer[len(question):].replace(\"Answer:\", \"\").strip()\n", "\n", " return answer" ], "metadata": { "id": "bgNcIKjNXK5d" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Example usage\n", "question = \"What is Diverticulosis?\"\n", "answer = get_answer(question)\n", "print(f\"Question: {question}\\nAnswer: {answer}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r5coyxVJYJ6A", "outputId": "f86b5526-97a5-46aa-a7b3-5048619d967b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Question: What is Diverticulosis?\n", "Answer: Diverticulosis is a type of diverticulosis that occurs when fluid from the diverticula diverges from the diverticulum. Diverticulosis occurs when fluid from the diverticula diverges from the diverticulum, leading to diverticulosis.\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Scratch Model" ], "metadata": { "id": "i42MGcxVlDEN" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "from torch.utils.data import Dataset, DataLoader\n", "from transformers import BertTokenizer\n", "\n", "# Load the dataset\n", "df = pd.read_csv(\"/content/llm_dataset.csv\")\n", "\n", "# Initialize the tokenizer\n", "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n", "\n", "class QADataset(Dataset):\n", " def __init__(self, dataframe, tokenizer, max_len):\n", " self.dataframe = dataframe\n", " self.tokenizer = tokenizer\n", " self.max_len = max_len\n", "\n", " def __len__(self):\n", " return len(self.dataframe)\n", "\n", " def __getitem__(self, idx):\n", " question = str(self.dataframe.iloc[idx]['Question'])\n", " answer = str(self.dataframe.iloc[idx]['Answer'])\n", "\n", " # Tokenize question and answer\n", " question_encodings = self.tokenizer(\n", " question,\n", " max_length=self.max_len,\n", " padding=\"max_length\",\n", " truncation=True,\n", " return_tensors=\"pt\"\n", " )\n", "\n", " answer_encodings = self.tokenizer(\n", " answer,\n", " max_length=self.max_len,\n", " padding=\"max_length\",\n", " truncation=True,\n", " return_tensors=\"pt\"\n", " )\n", "\n", " return {\n", " 'input_ids': question_encodings['input_ids'].squeeze(), # [max_len]\n", " 'attention_mask': question_encodings['attention_mask'].squeeze(), # [max_len]\n", " 'labels': answer_encodings['input_ids'].squeeze() # [max_len]\n", " }\n", "\n", "# Parameters\n", "max_len = 64 # Maximum sequence length\n", "batch_size = 8\n", "\n", "# Create Dataset and DataLoader\n", "train_dataset = QADataset(df, tokenizer, max_len)\n", "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ObTcZTzOkN8p", "outputId": "ff44a741-3243-4511-d465-2a20922f284e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n", " warnings.warn(\n" ] } ] }, { "cell_type": "code", "source": [ "import torch\n", "import torch.nn as nn\n", "\n", "class TransformerSeq2Seq(nn.Module):\n", " def __init__(self, vocab_size, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward, max_seq_len, dropout=0.1):\n", " super(TransformerSeq2Seq, self).__init__()\n", "\n", " # Embedding layers\n", " self.encoder_embedding = nn.Embedding(vocab_size, d_model)\n", " self.decoder_embedding = nn.Embedding(vocab_size, d_model)\n", "\n", " # Positional Encoding\n", " self.positional_encoding = nn.Parameter(torch.zeros(1, max_seq_len, d_model))\n", "\n", " # Transformer\n", " self.transformer = nn.Transformer(\n", " d_model=d_model,\n", " nhead=nhead,\n", " num_encoder_layers=num_encoder_layers,\n", " num_decoder_layers=num_decoder_layers,\n", " dim_feedforward=dim_feedforward,\n", " dropout=dropout\n", " )\n", "\n", " # Output linear layer\n", " self.fc_out = nn.Linear(d_model, vocab_size)\n", "\n", " def forward(self, src, tgt, src_mask=None, tgt_mask=None):\n", " # Embedding and positional encoding for encoder\n", " src_emb = self.encoder_embedding(src) + self.positional_encoding[:, :src.size(1), :]\n", " # Embedding and positional encoding for decoder\n", " tgt_emb = self.decoder_embedding(tgt) + self.positional_encoding[:, :tgt.size(1), :]\n", "\n", " # Transformer expects input of shape (seq_len, batch_size, d_model)\n", " src_emb = src_emb.transpose(0, 1) # [seq_len, batch_size, d_model]\n", " tgt_emb = tgt_emb.transpose(0, 1) # [seq_len, batch_size, d_model]\n", "\n", " # Pass through Transformer\n", " transformer_output = self.transformer(src_emb, tgt_emb, src_mask, tgt_mask)\n", "\n", " # Transpose back to [batch_size, seq_len, d_model]\n", " transformer_output = transformer_output.transpose(0, 1)\n", "\n", " # Output layer\n", " output = self.fc_out(transformer_output) # [batch_size, seq_len, vocab_size]\n", "\n", " return output\n", "\n", "# Model hyperparameters\n", "vocab_size = tokenizer.vocab_size\n", "d_model = 256\n", "nhead = 8\n", "num_encoder_layers = 4\n", "num_decoder_layers = 4\n", "dim_feedforward = 512\n", "max_seq_len = max_len\n", "\n", "# Initialize the model\n", "model = TransformerSeq2Seq(\n", " vocab_size=vocab_size,\n", " d_model=d_model,\n", " nhead=nhead,\n", " num_encoder_layers=num_encoder_layers,\n", " num_decoder_layers=num_decoder_layers,\n", " dim_feedforward=dim_feedforward,\n", " max_seq_len=max_seq_len\n", ")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wL3GaK1ylNYj", "outputId": "202a7ed4-dfc7-45a4-da9a-cac6b23c41c3" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/transformer.py:307: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n", " warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n" ] } ] }, { "cell_type": "code", "source": [ "model" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Y2ufr8TmlTfv", "outputId": "6caa1636-5cf3-49c6-c197-bb2d271e668e" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "TransformerSeq2Seq(\n", " (embedding): Embedding(30522, 256)\n", " (transformer): Transformer(\n", " (encoder): TransformerEncoder(\n", " (layers): ModuleList(\n", " (0-3): 4 x TransformerEncoderLayer(\n", " (self_attn): MultiheadAttention(\n", " (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n", " )\n", " (linear1): Linear(in_features=256, out_features=512, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (linear2): Linear(in_features=512, out_features=256, bias=True)\n", " (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", " (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", " (dropout1): Dropout(p=0.1, inplace=False)\n", " (dropout2): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (decoder): TransformerDecoder(\n", " (layers): ModuleList(\n", " (0-3): 4 x TransformerDecoderLayer(\n", " (self_attn): MultiheadAttention(\n", " (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n", " )\n", " (multihead_attn): MultiheadAttention(\n", " (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True)\n", " )\n", " (linear1): Linear(in_features=256, out_features=512, bias=True)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (linear2): Linear(in_features=512, out_features=256, bias=True)\n", " (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", " (norm2): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", " (norm3): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", " (dropout1): Dropout(p=0.1, inplace=False)\n", " (dropout2): Dropout(p=0.1, inplace=False)\n", " (dropout3): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " (norm): LayerNorm((256,), eps=1e-05, elementwise_affine=True)\n", " )\n", " )\n", " (fc_out): Linear(in_features=256, out_features=30522, bias=True)\n", ")" ] }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "code", "source": [ "import torch.optim as optim\n", "\n", "# Move model to device\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "model = model.to(device)\n", "\n", "# Loss function and optimizer\n", "criterion = nn.CrossEntropyLoss(ignore_index=tokenizer.pad_token_id)\n", "optimizer = optim.Adam(model.parameters(), lr=1e-4)\n", "\n", "# Number of epochs\n", "epochs = 20\n", "\n", "for epoch in range(epochs):\n", " model.train()\n", " epoch_loss = 0\n", "\n", " for batch in train_loader:\n", " # Get inputs and move to device\n", " input_ids = batch['input_ids'].to(device) # [batch_size, max_len]\n", " attention_mask = batch['attention_mask'].to(device) # [batch_size, max_len]\n", " labels = batch['labels'].to(device) # [batch_size, max_len]\n", "\n", " # Shift labels for decoder input (teacher forcing)\n", " decoder_input_ids = labels[:, :-1]\n", " labels = labels[:, 1:].contiguous()\n", "\n", " # Zero the gradients\n", " optimizer.zero_grad()\n", "\n", " # Forward pass\n", " outputs = model(input_ids, decoder_input_ids) # [batch_size, seq_len, vocab_size]\n", "\n", " # Reshape for loss computation\n", " outputs = outputs.view(-1, vocab_size) # [(batch_size * seq_len), vocab_size]\n", " labels = labels.view(-1) # [(batch_size * seq_len)]\n", "\n", " # Compute loss\n", " loss = criterion(outputs, labels)\n", " epoch_loss += loss.item()\n", "\n", " # Backward pass and optimization\n", " loss.backward()\n", " optimizer.step()\n", "\n", " avg_loss = epoch_loss / len(train_loader)\n", " print(f\"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HWjFR6lClV0D", "outputId": "e4b8fe55-cf99-4bd7-8c39-08ea4bcd841c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/20, Loss: 9.7251\n", "Epoch 2/20, Loss: 8.9657\n", "Epoch 3/20, Loss: 8.3415\n", "Epoch 4/20, Loss: 7.7263\n", "Epoch 5/20, Loss: 7.1735\n", "Epoch 6/20, Loss: 6.7080\n", "Epoch 7/20, Loss: 6.3273\n", "Epoch 8/20, Loss: 6.0067\n", "Epoch 9/20, Loss: 5.7498\n", "Epoch 10/20, Loss: 5.5193\n", "Epoch 11/20, Loss: 5.3198\n", "Epoch 12/20, Loss: 5.1383\n", "Epoch 13/20, Loss: 4.9742\n", "Epoch 14/20, Loss: 4.8258\n", "Epoch 15/20, Loss: 4.6762\n", "Epoch 16/20, Loss: 4.5503\n", "Epoch 17/20, Loss: 4.4374\n", "Epoch 18/20, Loss: 4.3159\n", "Epoch 19/20, Loss: 4.2030\n", "Epoch 20/20, Loss: 4.0945\n" ] } ] }, { "cell_type": "code", "source": [ "def generate_answer(question, max_len=64):\n", " model.eval()\n", " with torch.no_grad():\n", " # Tokenize the question\n", " tokens = tokenizer(question, return_tensors='pt', padding=True, truncation=True, max_length=max_len)\n", " input_ids = tokens['input_ids'].to(device) # [1, max_len]\n", "\n", " # Initialize decoder input with BOS token\n", " bos_token_id = tokenizer.bos_token_id if tokenizer.bos_token_id is not None else tokenizer.cls_token_id\n", " decoder_input = torch.tensor([[bos_token_id]], device=device) # [1, 1]\n", "\n", " generated_tokens = []\n", "\n", " for _ in range(max_len):\n", " # Forward pass through the model\n", " outputs = model(input_ids, decoder_input) # [1, seq_len, vocab_size]\n", "\n", " # Get the last token's logits\n", " next_token_logits = outputs[:, -1, :] # [1, vocab_size]\n", "\n", " # Get the token with the highest probability\n", " next_token_id = torch.argmax(next_token_logits, dim=-1).item()\n", "\n", " # If EOS token is generated, stop\n", " if next_token_id == tokenizer.eos_token_id:\n", " break\n", "\n", " # Append the token to the generated tokens\n", " generated_tokens.append(next_token_id)\n", "\n", " # Update the decoder input\n", " decoder_input = torch.cat([decoder_input, torch.tensor([[next_token_id]], device=device)], dim=1)\n", "\n", " # Decode the generated tokens to text\n", " answer = tokenizer.decode(generated_tokens, skip_special_tokens=True)\n", " return answer\n", "\n", "# Example usage\n", "question = \"What are the symptoms of pneumonia?\"\n", "answer = generate_answer(question)\n", "print(f\"Question: {question}\\nAnswer: {answer}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6QwvemxClYxo", "outputId": "6c8d8c56-06b3-43b9-8390-e848d834fa0c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Question: What are the symptoms of pneumonia?\n", "Answer: pneumonia is a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a a\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "7etd9vybpK2S" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Distil GPT 2" ], "metadata": { "id": "ddvOJsAiS9hn" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "from datasets import Dataset\n", "\n", "# Load dataset\n", "df = pd.read_csv('/content/llm_dataset.csv')\n", "\n", "# Convert DataFrame to Hugging Face Dataset format\n", "dataset = Dataset.from_pandas(df)" ], "metadata": { "id": "H4kac-3US_2O" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!pip install transformers --upgrade" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 740 }, "id": "q4Us1evfT4Ts", "outputId": "a6dd02b1-79f4-471a-fb60-4750c0407739" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n", "Collecting transformers\n", " Downloading transformers-4.45.2-py3-none-any.whl.metadata (44 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m49.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: tokenizers, transformers\n", " Attempting uninstall: tokenizers\n", " Found existing installation: tokenizers 0.19.1\n", " Uninstalling tokenizers-0.19.1:\n", " Successfully uninstalled tokenizers-0.19.1\n", " Attempting uninstall: transformers\n", " Found existing installation: transformers 4.44.2\n", " Uninstalling transformers-4.44.2:\n", " Successfully uninstalled transformers-4.44.2\n", "Successfully installed tokenizers-0.20.1 transformers-4.45.2\n" ] }, { "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { "pip_warning": { "packages": [ "transformers" ] }, "id": "6e9f9ec09e43428a93112653c07427ef" } }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [], "metadata": { "id": "8Nzkp3PVWHLh" } }, { "cell_type": "code", "source": [ "from transformers import AutoTokenizer\n", "\n", "# Load tokenizer\n", "tokenizer = AutoTokenizer.from_pretrained('distilgpt2')\n", "\n", "# Add a special token separator between the question and answer\n", "separator_token = tokenizer.eos_token\n", "\n", "# Add a padding token to the tokenizer, if it's missing.\n", "# We are using the 'eos_token' as the 'pad_token' for simplicity\n", "# but you could add a new pad token like '[PAD]' if you prefer.\n", "if tokenizer.pad_token is None:\n", " tokenizer.pad_token = tokenizer.eos_token\n", "\n", "def tokenize_function(examples):\n", " # Create input texts by combining question and answer\n", " input_texts = [q + separator_token + a for q, a in zip(examples['Question'], examples['Answer'])]\n", " # Tokenize the input texts with padding and truncation\n", " tokenized_output = tokenizer(\n", " input_texts,\n", " padding=\"max_length\",\n", " truncation=True,\n", " max_length=128,\n", " return_tensors='pt'\n", " )\n", "\n", " # Use the input_ids as labels (shifted by one)\n", " # This is a common practice for language modeling where the model predicts the next token\n", " tokenized_output[\"labels\"] = tokenized_output[\"input_ids\"].clone()\n", "\n", " return tokenized_output\n", "\n", "# Apply tokenization to your dataset\n", "tokenized_dataset = dataset.map(\n", " tokenize_function,\n", " batched=True,\n", " remove_columns=dataset.column_names # Remove original columns to avoid errors\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 368, "referenced_widgets": [ "64896ff8456643458d7ac64a89d6faa0", "985ed05bce8e43efaa2e3d9906314a6e", "3181c30d69364fd59a00714d83154571", "c79135f34c54468cbc4d85639e02b5d1", "d7d674b1a1e94706ac981354ed36e296", "33e474ada72342499c39d171cc3c0403", "adb57ba559ca4d3fab3ec2133cf547a4", "2dc1f8f8904344fbbf874bf252d68b2e", "04a75b2378c14deaa066b069dedcf968", "8db82bc323cc48dbbf857c1016746dfd", "7598f8ebcfe140b6b8106071549ff297", "88d1cd6452084cdf9524db580ac4fce7", "f86497db1c584360a2534459af8b2c7e", "ac27c740233a4efc81eeaa57432ae87a", "1a6fe151e7ba404ea6fa543f4273ccd5", "1cc32a095f8f44418c40fb3ba07ea193", "d714b470a0094fa29e505a92658d181a", "a9a8d7e0465d43ef9948160e8247a963", "31cb8fc1400b4519bfe05aea29395019", "3fcfe27d35104a52b92e58417b1fe398", "0fca32ab2b584cc387ed2d41418ae470", "9ad78e89165c4f4b9079be082a6908ce", "39217fcbdee247c2ab28078787e13bcb", "9ce8ae477b934bd59cc37d8431a9a283", "feff295afa354fa1bfb2d9344757f701", "1d45f348e60a404792cb8aebc043811f", "52f2ed28110a41839fe6806df2978625", "2e439ced8453476e9f2491495140cbe7", "d52a7a9a1f07401d9ca84fe01d2e4f73", "8145d070619a4ec798f74f9e00eead8f", "888a26f2e9e54dc5871a6f24ca455aad", "d87c15dfca024cb3a1bb4b89d685999e", "e26eba5b6e6f4eb2850d2ea94c93e5d3", "5cfa41b9cbd440d8a5a2499ded25f3ee", "f0fad724fa5e49c596e10cde99f82bf7", "2c4dfa32ec0d47319252fb0d113d6d84", "63c612c9e03445348d46208bacf94d8a", "165ff4406b0848bca0b4b9d9de8c3cf0", "c4e4134396514cd6a9b4813aa91c7359", 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"outputId": "1dfc3eca-22d0-4643-8bc2-14436af4e9bf" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/26.0 [00:00" ], "text/html": [ "\n", "

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" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=540, training_loss=0.6469527279889142, metrics={'train_runtime': 113.4784, 'train_samples_per_second': 38.069, 'train_steps_per_second': 4.759, 'total_flos': 141100245319680.0, 'train_loss': 0.6469527279889142, 'epoch': 30.0})" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "code", "source": [ "import torch\n", "\n", "def generate_answer(question, max_length=1024):\n", " model.eval() # Set the model to evaluation mode\n", " input_text = question + separator_token\n", " input_ids = tokenizer.encode(input_text, return_tensors='pt')\n", "\n", " # Move input_ids to the same device as the model\n", " input_ids = input_ids.to(model.device)\n", "\n", " output = model.generate(input_ids, max_length=max_length, pad_token_id=tokenizer.eos_token_id)\n", " answer = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)\n", " return answer\n", "\n", "# Example usage\n", "question = \"what are the symptoms of pneumonia?\"\n", "print(generate_answer(question))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nU1NIXATVBHH", "outputId": "6c3ba493-7c23-4ef3-d665-8a2c27d1eb41" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Symptoms of pneumonia vary depending on the severity of the illness and the underlying cause. Some people may experience milder symptoms, while others may experience a more severe form of pneumonia.\n" ] } ] }, { "cell_type": "code", "source": [ "/# Save the fine-tuned model\n", "model.save_pretrained('./fine-tuned-distilgpt2')\n", "tokenizer.save_pretrained('./fine-tuned-distilgpt2')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "GPWOHjK7JIvi", "outputId": "b2aeb21b-66d9-4049-98e6-4fbedc0a8481" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "('./fine-tuned-distilgpt2/tokenizer_config.json',\n", " './fine-tuned-distilgpt2/special_tokens_map.json',\n", " './fine-tuned-distilgpt2/vocab.json',\n", " './fine-tuned-distilgpt2/merges.txt',\n", " './fine-tuned-distilgpt2/added_tokens.json',\n", " './fine-tuned-distilgpt2/tokenizer.json')" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "# copy model to drive\n", "!cp /content/fine-tuned-distilgpt2/model.safetensors /content/drive/MyDrive/" ], "metadata": { "id": "YfOiLXZrKiJo" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "vsbC9b3mLk-A" }, "execution_count": null, "outputs": [] } ] }