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(2024.9.11)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n","Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.6.1)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n"]}],"source":["!pip install transformers\n"]},{"cell_type":"code","source":["# Install required libraries\n","!pip install transformers datasets\n","\n","# Import necessary libraries\n","from transformers import GPTNeoForCausalLM, GPT2Tokenizer, Trainer, TrainingArguments\n","from datasets import Dataset\n","import os\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RLAkIUWZ6IWG","executionInfo":{"status":"ok","timestamp":1727280224623,"user_tz":-330,"elapsed":21766,"user":{"displayName":"Harsh 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in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n","Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n"]}]},{"cell_type":"code","source":["from transformers import GPTNeoForCausalLM, GPT2Tokenizer, Trainer, TrainingArguments\n","model = GPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M')\n","tokenizer = GPT2Tokenizer.from_pretrained('EleutherAI/gpt-neo-125M')\n"],"metadata":{"id":"lPl9LnJ2vNhk"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n"],"metadata":{"id":"J9hsP9MYcEfW"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pfrom google.colab import files\n","uploaded = files.upload()\n"],"metadata":{"id":"Dau_ggXowNuT","colab":{"base_uri":"https://localhost:8080/","height":322},"executionInfo":{"status":"ok","timestamp":1727279285823,"user_tz":-330,"elapsed":25449,"user":{"displayName":"Harsh Agnihotri","userId":"17822398258247663302"}},"outputId":"0ffaccb4-7aaf-4403-91fa-c96fdb172ee2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","     <input type=\"file\" id=\"files-e3050ed1-caf3-4bcb-b5ee-cefa927044df\" name=\"files[]\" multiple disabled\n","        style=\"border:none\" />\n","     <output id=\"result-e3050ed1-caf3-4bcb-b5ee-cefa927044df\">\n","      Upload widget is only available when the cell has been executed in the\n","      current browser session. Please rerun this cell to enable.\n","      </output>\n","      <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","//      http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n","  const element = document.createElement('span');\n","  element.textContent = text;\n","  for (const key of Object.keys(styleAttributes)) {\n","    element.style[key] = styleAttributes[key];\n","  }\n","  return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n","  const steps = uploadFilesStep(inputId, outputId);\n","  const outputElement = document.getElementById(outputId);\n","  // Cache steps on the outputElement to make it available for the next call\n","  // to uploadFilesContinue from Python.\n","  outputElement.steps = steps;\n","\n","  return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n","  const outputElement = document.getElementById(outputId);\n","  const steps = outputElement.steps;\n","\n","  const next = steps.next(outputElement.lastPromiseValue);\n","  return Promise.resolve(next.value.promise).then((value) => {\n","    // Cache the last promise value to make it available to the next\n","    // step of the generator.\n","    outputElement.lastPromiseValue = value;\n","    return next.value.response;\n","  });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n","  const inputElement = document.getElementById(inputId);\n","  inputElement.disabled = false;\n","\n","  const outputElement = document.getElementById(outputId);\n","  outputElement.innerHTML = '';\n","\n","  const pickedPromise = new Promise((resolve) => {\n","    inputElement.addEventListener('change', (e) => {\n","      resolve(e.target.files);\n","    });\n","  });\n","\n","  const cancel = document.createElement('button');\n","  inputElement.parentElement.appendChild(cancel);\n","  cancel.textContent = 'Cancel upload';\n","  const cancelPromise = new Promise((resolve) => {\n","    cancel.onclick = () => {\n","      resolve(null);\n","    };\n","  });\n","\n","  // Wait for the user to pick the files.\n","  const files = yield {\n","    promise: Promise.race([pickedPromise, cancelPromise]),\n","    response: {\n","      action: 'starting',\n","    }\n","  };\n","\n","  cancel.remove();\n","\n","  // Disable the input element since further picks are not allowed.\n","  inputElement.disabled = true;\n","\n","  if (!files) {\n","    return {\n","      response: {\n","        action: 'complete',\n","      }\n","    };\n","  }\n","\n","  for (const file of files) {\n","    const li = document.createElement('li');\n","    li.append(span(file.name, {fontWeight: 'bold'}));\n","    li.append(span(\n","        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n","        `last modified: ${\n","            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n","                                    'n/a'} - `));\n","    const percent = span('0% done');\n","    li.appendChild(percent);\n","\n","    outputElement.appendChild(li);\n","\n","    const fileDataPromise = new Promise((resolve) => {\n","      const reader = new FileReader();\n","      reader.onload = (e) => {\n","        resolve(e.target.result);\n","      };\n","      reader.readAsArrayBuffer(file);\n","    });\n","    // Wait for the data to be ready.\n","    let fileData = yield {\n","      promise: fileDataPromise,\n","      response: {\n","        action: 'continue',\n","      }\n","    };\n","\n","    // Use a chunked sending to avoid message size limits. See b/62115660.\n","    let position = 0;\n","    do {\n","      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n","      const chunk = new Uint8Array(fileData, position, length);\n","      position += length;\n","\n","      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n","      yield {\n","        response: {\n","          action: 'append',\n","          file: file.name,\n","          data: base64,\n","        },\n","      };\n","\n","      let percentDone = fileData.byteLength === 0 ?\n","          100 :\n","          Math.round((position / fileData.byteLength) * 100);\n","      percent.textContent = `${percentDone}% done`;\n","\n","    } while (position < fileData.byteLength);\n","  }\n","\n","  // All done.\n","  yield {\n","    response: {\n","      action: 'complete',\n","    }\n","  };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n","  _uploadFiles,\n","  _uploadFilesContinue,\n","};\n","})(self);\n","</script> "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saving Bhuvanesh Mehta SOP Germany.pdf to Bhuvanesh Mehta SOP Germany (1).pdf\n","Saving CHAITANYA BENDRE SOP NEW.docx to CHAITANYA BENDRE SOP NEW (1).docx\n","Saving Cover Letter.docx to Cover Letter (1).docx\n","Saving Letter of Motivation molecular medicine.docx to Letter of Motivation molecular medicine (2).docx\n","Saving MOHAMMED UDAIPURWALA SOP NEW.docx to MOHAMMED UDAIPURWALA SOP NEW (1).docx\n","Saving NEU sop.docx to NEU sop (1).docx\n","Saving RAKSHANDA BAVISKAR SOP NEW.docx to RAKSHANDA BAVISKAR SOP NEW (1).docx\n","Saving SIDDHESH GHULE SOP NEW.docx to SIDDHESH GHULE SOP NEW (1).docx\n"]}]},{"cell_type":"code","source":["from datasets import Dataset\n","from transformers import GPT2Tokenizer\n"],"metadata":{"id":"VbDS0kiU8HF8"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from google.colab import files\n","\n","# Example SOP texts as a dictionary (replace this with your actual dataset)\n","from datasets import Dataset\n","from transformers import GPT2Tokenizer\n","\n","sop_texts = [\n","    \"Now I want to pursue my further studies in Master of Science Molecular Medicine ...\",\n","    \"My interest in the field of information and technology led me to do my masters in this field... \",\n","    \"Computing has transformed our world more than anything else in the world and getting to know ... \",\n","    \"Everybody, since their early childhood, develops habits which are born either out of  ...\",\n","    \"Every era has a greater good and you can make a good living while serving a greater good  ...\",\n","    # Add more SOPs here\n","]\n","\n","# Create the dataset\n","data = {\"text\": sop_texts}\n","dataset = Dataset.from_dict(data)\n","\n","# Initialize the tokenizer for GPT-Neo\n","tokenizer = GPT2Tokenizer.from_pretrained(\"EleutherAI/gpt-neo-125M\")  # You can change this to the specific GPT-Neo model you're using\n","\n","# GPT-Neo requires a pad token, which is not set by default\n","tokenizer.pad_token = tokenizer.eos_token\n","\n","# Tokenize the dataset\n","def tokenizer_function(examples):\n","    return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True, max_length=512)\n","\n","tokenized_dataset = dataset.map(tokenizer_function, batched=True)\n","\n","print(tokenized_dataset)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":122,"referenced_widgets":["eff76625039745388d091243c7a126e1","5002e8c6e48a4dd6a6fa0f581eb377c1","943e1838f1bb4afb96e6f40da79f19d6","0f9088baac764fcbb0beccfe298fc944","33492c16118447be9accc10095b69bba","af306c1991e543c59917daa2ed74664e","68ace57256a440089dc9b773d69e2242","dd7a054a46994fb0b356da7c832ed77f","5d715ba9867d47c2aabe72d9e76849ce","f2b4e64062fa47ac9e97a6c7d6ea474f","305b27ce1e40434486995da5b4bc73bc"]},"id":"Q-GyRYAz2wm3","executionInfo":{"status":"ok","timestamp":1727422908921,"user_tz":-330,"elapsed":5577,"user":{"displayName":"Harsh Agnihotri","userId":"17822398258247663302"}},"outputId":"cde8dace-0cd4-465b-8a76-bc02e5657555"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/5 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eff76625039745388d091243c7a126e1"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset({\n","    features: ['text', 'input_ids', 'attention_mask'],\n","    num_rows: 5\n","})\n"]}]},{"cell_type":"code","source":["# Install required libraries\n","!pip install transformers datasets torch\n","\n","# Import necessary libraries\n","import torch\n","from transformers import GPTNeoForCausalLM, GPT2Tokenizer, Trainer, TrainingArguments\n","from datasets import Dataset\n","\n","# Example SOP texts (add more examples here)\n","sop_texts = [\n","    \"Now I want to pursue my further studies in Master of Science Molecular Medicine ...\",\n","    \"My interest in the field of information and technology led me to do my masters in this field...\",\n","    \"Computing has transformed our world more than anything else in the world and getting to know...\",\n","    # Add more SOPs here\n","]\n","\n","# Create the dataset\n","dataset = Dataset.from_dict({\"text\": sop_texts})\n","\n","# Initialize the model and tokenizer\n","model_name = \"EleutherAI/gpt-neo-125M\"  # You can change this to a larger model if you have enough GPU memory\n","model = GPTNeoForCausalLM.from_pretrained(\"EleutherAI/gpt-neo-125M\")\n","tokenizer = GPT2Tokenizer.from_pretrained(\"EleutherAI/gpt-neo-125M\")\n","\n","# Set pad token\n","tokenizer.pad_token = tokenizer.eos_token\n","model.config.pad_token_id = tokenizer.pad_token_id\n","\n","# Tokenize the dataset\n","def tokenize_function(examples):\n","    return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True, max_length=512, return_attention_mask=True)\n","\n","tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=[\"text\"])\n","\n","# Ensure the dataset has the correct format for training\n","tokenized_dataset = tokenized_dataset.map(\n","    lambda examples: {\"labels\": examples[\"input_ids\"].copy()},\n","    batched=True\n",")\n","\n","# Set the tensor type\n","tokenized_dataset.set_format(\"torch\")\n","\n","# Define training arguments\n","training_args = TrainingArguments(\n","    output_dir=\"./results\",\n","    num_train_epochs=3,\n","    per_device_train_batch_size=1,  # Increase this if you have more GPU memory\n","    fp16=torch.cuda.is_available(),\n","    warmup_steps=500,\n","    weight_decay=0.01,\n","    logging_dir=\"./logs\",\n","    logging_steps=10,\n",")\n","\n","# Initialize Trainer\n","trainer = Trainer(\n","    model=model,\n","    args=training_args,\n","    train_dataset=tokenized_dataset,\n","    tokenizer=tokenizer\n",")\n","\n","# Train the model\n","train_result = trainer.train()\n","print(f\"Training loss: {train_result.training_loss}\")\n","\n","# Save the model\n","model.save_pretrained(\"./sop_generator_model\")\n","tokenizer.save_pretrained(\"./sop_generator_model\")\n","\n","# Function to generate SOP\n","def generate_sop(prompt, max_length=300):\n","    try:\n","        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","        model.to(device)\n","        input_ids = tokenizer.encode(prompt, return_tensors=\"pt\").to(device)\n","\n","        with torch.no_grad():\n","            output = model.generate(\n","                input_ids,\n","                max_length=max_length,\n","                num_return_sequences=1,\n","                no_repeat_ngram_size=2,\n","                do_sample=True,\n","                top_k=50,\n","                top_p=0.95,\n","                temperature=0.7\n","            )\n","\n","        return tokenizer.decode(output[0], skip_special_tokens=True)\n","    except Exception as e:\n","        print(f\"An error occurred during SOP generation: {str(e)}\")\n","        return None\n","\n","# Example usage\n","prompt = \"Generate a Statement of Purpose for a student applying for a Master's in Computer Science. The student has a background in:\"\n","background = \"software development with 2 years of experience in web technologies. They are interested in machine learning and AI.\"\n","full_prompt = prompt + \" \" + background\n","generated_sop = generate_sop(full_prompt)\n","print(generated_sop)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["b8a387c7c1a745e687b4321f90c77683","566eaf3960aa4f10b8018997d04a7bdb","f4e0971c9f2242a198f8c7dc9320290f","60aee750482e4b5a894f070688c35d4f","2f8d41481aba466693dff84a145ce865","55deca3afa424303bbdf72d3dde75339","7807889977f647c9bdf07344a0d81225","ea2e17b10fc84b80bfe63b037ebd80ce","584d690efb7c4da6aa9309de99a86ddb","ae63ee1ad7b443e59fc73ad5f1b9d9fb","8da090f817ab44dc81f1e3ea1093f545","38e68428b13849ef9f8aefd0adbfb19d","5a7ddb872d124129a71ea5bfb9f2d1f7","e843cbe11e004297a6e8e383fcc2eed4","8ffb7d5332744bbfba41e3c09147d9da","685808957b8c4eb292ae348dc0555bf7","da068fba4bfa4ba4a885f771279668f5","1d83ae41765b46159db148222a25db9d","4c6c732989d84339810b2420839e6fc9","af9cbbf12bd04197b205f3a5ed0f5435","1a54ccc84ca544de958338e9191f832a","6c5a56a1beb54080bef16ac2f195cea8","a0941093b9954508ae0a6bba7ceb5394","9e3376f2f41947519a545d572610ee08","1341792b780841eeb10530a1e0d5bef8","39a9341f220e48bbb98e384e2fb84399","18515409422246d49ccbe5e611c4750a","5d6797c2092f4ff7879906d4b11e11f6","e1f8d8e0566949d787aee9ab54e397d7","5ad8d0d4fac74202912c1359e2a7b5ff","f0730578150c414c9debb0c56c3e59ad","9e68c5077a3b4e1abb7d3844990193b3","32c142d7382f42a6b654001e8b4e7ebd","7b405642b29d4e468eac53b722165b26","51ab95e0764f43678d5c14767dce48d0","040f53d1875945688690f302d3cdfc82","fb47c57689b74ac6a5754331ce2a1718","f1d8a8ef005047eb9997b81a468e4a68","95a7988257734886adfe04a032e66c76","01305792bb634e62b03518a039406600","1f89874685f9451ab1e07f2ecadcb6eb","e823552bc0494089a03acbf45f1e5c12","8b6f3ab4aedb4c2d8f9821e829cd6076","53a03bf048b7483ba5bb910a81921101","17258866c8b64de1b1ed7fd89550aa0b","c5cfcbaca5b34bd1b7db38b2e0f25f42","ddb57b661953430492e180d090a7a524","3eda74437e10428786e164ac9dd76a13","14962ac3645b493b9b1f8acdf65058c6","42f0a3ca1a9a4ae0ae7bc07b6f2e8cbf","5fdf3425adc844839a663fcbb1c875c2","be89db9bfa97413e9197a4bd630f3f5a","0b0f164a6cf5404898033e1c9af1a05f","8355bdde474f491db1762c8494005df8","cd67f93582b84db58d8e2b35cd7a896e","a4a5a424f4d740bb894752957c1e73f7","4965d6c91fe040e0ac7b649cc31cd924","1f587e516f5c481a88d192d949abdef3","6ccdd660b5394889a0c04e6118074fd6","dc32836f4e42485ca19c73b68e52f40c","5f2917a063ea4af29ba8bb895c57b1ba","249d9849b72d4813b8ff662bd83b8865","ba5e858befd14b69b7507c19d42867ba","5eceba5dde7247fe84359b4dae39e6b6","b4d6aef6c3854bd6b4448876cebb50dd","4abde6d2046e4ec09c2cd58a0dcfaac2","8f493428b69f40e0a726d3694a523473","bd1f82adfba44b08abd66c1d51582531","58744e98740041d397e5a265e22a9514","d62c7640f2454a64926f45a20679403e","9928ae8fe3db44229f8d0e5585a40a43","064dafeb6af54f92a4b53eacadf1dc7f","09fd26960dae4253b56f6d9daa2d3757","66d24b4cba5b423890448bc0a0455c60","31340b8a7a634c3d895382f76dc628ac","2e99670707e84908b8cfcb194e37460e","9c3f696f060e42fc86b24e6f2211935a","608be71c15274b3a8f8208b5672544ef","6be56bdc9de24503917eacf9cfff5582","a159ab227f1b46cdb16db4138b385921","2b1fae83c92d4f1e989b5b5022c486d9","133c9a3ad28f44ff8d0647905a4c8ead","269942f61b364338992efe96da9caa00","766481d759d34306afbdfc4dfcbb1cfd","9adfce820dd44ccdbfa3991cd98a0f52","14c7399b394b44d5b7340fcf7729db65","839ce717d16740c1b990c67843b17a84","175376f2d6c6410fad5e41308d307c05","822b916adb924e16850264f29491da3e","3542f81293954eef86bd026f5211da1d","5890a7c66d2a44328f54d2fa8a658e57","61175978d06c4bb2a2b1cde38c5c40cc","de1dbac1a3a343c3be718ba0de90d922","7ae4ce351a764f8a9ea74234afb660ca","44a74712c409447e9156792d5f0bd1fd","ed03263a27cf4189bba4d9ab266ef964","8474be32093c46c4aebd79835fcd89a9","ddd4ac740c41478e83f89e3fb9f9eefe","969b72406cc64599b0a2b833d2b98871","419ac58b1a354c5a86a75ad298a242d5","f9431935081e4892b3d8b952644a66d2","504679134b8f4afa99166f5c6212776b","56defff6e06f4bcf9d51dc53f4edf899","1f223bf9b61947ed96dc44a088252a3a","e6c769ccff1c4154b6d7a84107284a93","b946ede1cf754f1f87bb30fc583bef5e","98f7a2e5b43f43ecba71918f9ce4d501","8582841915bd4c6dbc2da2c9bad4abab","96c5da5b52324d8ca52e99ec2230d1fb","37209ece9086465186043b06d3842f37"]},"id":"YOvgI2q3g3dC","executionInfo":{"status":"ok","timestamp":1727437349979,"user_tz":-330,"elapsed":52345,"user":{"displayName":"Harsh Agnihotri","userId":"17822398258247663302"}},"outputId":"5c981088-7dcd-473c-e653-e9d557f18183"},"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","Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (3.0.1)\n","Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.4.1+cu121)\n","Requirement 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 (from transformers) (6.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n","Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n","Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n","Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n","Requirement already satisfied: pandas in 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torch) (3.1.4)\n","Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.0)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n","Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.11.1)\n","Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n","Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n"]},{"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":["config.json:   0%|          | 0.00/1.01k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b8a387c7c1a745e687b4321f90c77683"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/526M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"38e68428b13849ef9f8aefd0adbfb19d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/119 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a0941093b9954508ae0a6bba7ceb5394"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/727 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7b405642b29d4e468eac53b722165b26"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"17258866c8b64de1b1ed7fd89550aa0b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a4a5a424f4d740bb894752957c1e73f7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/357 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8f493428b69f40e0a726d3694a523473"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|     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Please use `torch.amp.GradScaler('cuda', args...)` instead.\n","  self.scaler = torch.cuda.amp.GradScaler(**kwargs)\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='9' max='9' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [9/9 00:10, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Training loss: 8.682064480251736\n"]},{"output_type":"stream","name":"stderr","text":["The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n","Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n","The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"]},{"output_type":"stream","name":"stdout","text":["Generate a Statement of Purpose for a student applying for a Master's in Computer Science. The student has a background in: software development with 2 years of experience in web technologies. They are interested in machine learning and AI.\n","\n","I have a strong interest in learning to build a computer, and have been working with a number of schools and colleges to create a system for this. I am looking for help with this project. Any feedback would be greatly appreciated. Thanks!\n","My name is John L. Jevan, I have an interest to learn more about the basics of programming, but I don't have the time to write anything that's useful to me. If I could learn something new, would you please provide me with some advice?\n","Thanks\n","John\n","P.S. This is a project I've been wanting to try out. It's basically a set of programs that I'm writing for my own personal use. Here's the link:\n","http://www.bac.cs.uw.edu/~jevans/software/programming/\n","Please leave me a comment on the project and I'll be sure to post it in the future.\n"]}]},{"cell_type":"code","source":["def generate_sop(prompt, max_length=300):\n","    try:\n","        device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n","        model.to(device)\n","        input_ids = tokenizer.encode(prompt, return_tensors=\"pt\").to(device)\n","\n","        with torch.no_grad():\n","            output = model.generate(\n","                input_ids,\n","                max_length=max_length,\n","                num_return_sequences=1,\n","                no_repeat_ngram_size=2,\n","                top_k=50,\n","                top_p=0.95,\n","                temperature=0.7\n","            )\n","\n","        return tokenizer.decode(output[0], skip_special_tokens=True)\n","    except Exception as e:\n","        print(f\"An error occurred during SOP generation: {str(e)}\")\n","        return None\n"],"metadata":{"id":"TYY7ehLclUMZ"},"execution_count":null,"outputs":[]}]}