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/usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->trl) (0.1.2)\n"]}],"source":["# Install required libraries\n","!pip install transformers datasets trl torch accelerate bitsandbytes wandb"]},{"cell_type":"code","source":["# Import necessary modules\n","from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, pipeline\n","from datasets import load_dataset\n","from trl import SFTConfig, SFTTrainer, setup_chat_format\n","import torch\n","import os\n","\n","# Detect device (GPU, MPS, or CPU)\n","device = (\n","    \"cuda\"\n","    if torch.cuda.is_available()\n","    else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n",")"],"metadata":{"id":"n1KQTqFojKgt","executionInfo":{"status":"ok","timestamp":1736100643357,"user_tz":-330,"elapsed":36375,"user":{"displayName":"PRITHIV SAKTHI U R","userId":"04296443067141805926"}}},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":["***Quick Check For Resources - Runtime***"],"metadata":{"id":"Ou4qDQJ2mHHJ"}},{"cell_type":"code","source":["!nvidia-smi"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8kKAUzEilylH","executionInfo":{"status":"ok","timestamp":1736100667801,"user_tz":-330,"elapsed":399,"user":{"displayName":"PRITHIV SAKTHI U R","userId":"04296443067141805926"}},"outputId":"984f1541-e657-464a-e921-02b82f0e7d69"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Sun Jan  5 18:11:07 2025       \n","+---------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n","|-----------------------------------------+----------------------+----------------------+\n","| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n","|                                         |                      |               MIG M. |\n","|=========================================+======================+======================|\n","|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n","| N/A   37C    P8               9W /  70W |      3MiB / 15360MiB |      0%      Default |\n","|                                         |                      |                  N/A |\n","+-----------------------------------------+----------------------+----------------------+\n","                                                                                         \n","+---------------------------------------------------------------------------------------+\n","| Processes:                                                                            |\n","|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n","|        ID   ID                                                             Usage      |\n","|=======================================================================================|\n","|  No running processes found                                                           |\n","+---------------------------------------------------------------------------------------+\n"]}]},{"cell_type":"code","source":["# Load the model and tokenizer\n","model_name = \"HuggingFaceTB/SmolLM2-360M\"\n","model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_name)\n","tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n","\n","# Set up chat format for the model and tokenizer\n","model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n","\n","# Test the base model with a simple prompt\n","prompt = \"Explain AGI ?\"\n","pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, device=0 if device == \"cuda\" else -1)\n","print(pipe(prompt, max_new_tokens=200))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":328,"referenced_widgets":["6090cb09a21e460287051daf0fcbc2a2","20ec5f726efc4cb1b8eb9e75286587c5","441da66baa09466ebf39005dff1e7f49","5cbea91c0a734089b0a3d6dd841e328c","0bf1118e15d644b6a0fa5b163acf20ff","bb4f4819768c443ab54742e024aed42c","3fe0ee3e635c47cab6df9df39611a21e","8a29064b5d2444f4bad374bf7ad80f1e","d88ea0704786411aa73f7ed2380364ab","b3340d5c5587406a993524163684ceae","5dc7388fc572466f91a9e2f3c79c8a70","334edb62beda45308864cc58a4d47c09","76d147d2c4d941fe8badc0f9290ab126","a42d5fa3f6994a6a879fc804cc61cf7f","9c17e7be9f5b48d4b9cc96fb127f01fb","3587291bcb7c4477844775f2445ee33b","f06b93d7714f496ab2efde59fdb4f9a1","100a11e9c8424bd097142d3cd34fc35f","775e1080b0f94fbe9227e1c7cd190ec6","7dfded8ce2f04058b527541fa71ccd22","418fcda5d0cf46d7bfca092dc0d2809f","bf91fc95a17240c4b3fa1d622378b693","e26a1e14c4ac44e98ec309d2879d8a3a","6965f07266e04741864fdeed1962aaa8","28914283fd074c34b5d0578fbbd929f7","91427f573b964baab013837409a3becb","fd5f03f00df54c1f92c7226b35dd2090","d7f7aa93ded24bb9a5920954bd3da2db","5846af2b30eb4d5f915cf2a9f68a6fb0","259a5afe64d943908b6845466d6c57d3","65b00aac92794883ba4ee431c5f3476b","882dd5aa07f644f2a6a726af1b68732f","35afb4d1908d47a7ab3c369cfdef4578","19d1ca660dde4b37865a563dc127a27b","d1bfec6300ae417795bb775b3994fe87","009ca2fba0f940948291c52188cdeb51","9bee6a5d8bb143e7a364fd3bae7a642f","a11488f9c4094dc5934b43532fa67f4f","93ef05ee47a74a579f655f0ae7cf0150","e3a9492c17954597acb3039c69c3720a","0f77fc478baf465489a662ab7aa82651","82cc4a555d724fb2b41c4a6d254554c7","867025f940634e30a1488441f57aaf6c","867723244478406292a704e87d7d249c","88a58ba66fe44f4d9dea7cd9e1c5b46b","44810b4b640349e8bb5598b9ab056d41","32b6704af78246c1b0344061a7cf147b","3e7201aff40e4bf08d7d3e06d3ef1129","f46d87ea7c014de6abb2c3bd42df9e37","81c93565c52b44f78e7d94c1492c6065","a890db4f254147ca91bf308224ff7637","b255e7d371ef443cb7d6336bce47290d","b33a651aa00a42508550783be33edfcd","ed9c137a93e04ab0a918524b1ce6a0e3","5d1f655c5a2b4f848aa2a090043cf010","b9720980e89b4966922db6bee3ee04c0","94f3fe9604dc4bec99f824a8b0338ac5","10291aa46bc94f76846851add9be5cbe","f09240e3ad8f43b99a3eee722b04d3b3","b88149436b404c52ba3c74c5edf6bf50","86456acb75ed4809b0fcebd2cfdd1a86","70833fa6e41c4802b9659192c9ca9c81","8d7c5693a81d4990876f4cd95a948603","ba3225f950174d5d9d593620964be1bf","33dafe602e29407b8016b0a1e9c8143c","f753a09ba3814ecea3854a3b0f38cdbf","d0e5a0a3a70a4c71ad8f9395fb0cd5f8","f894ad036a594569928bb33774f3fb37","c0edd78338e14e79a4b53a6aa91712ae","3a70d9fe86094c14ac6b55ebfd75db99","257e6e2d9ee24b6c8f773b8f054323a9","ca6399abfd2b40f1a6f2ce9e8f99ac46","cf247e21c46d4c44ba432dc647ad024b","5ebd357aafd14ebfa3c0c7599be3d416","e4e5339cef01473fb85356e5826be707","76df8f789e1846efb0413093de45e606","e083d95ba92440fdae9f0907f73d4171","d57a86c434c741b0bc7f3e35a8ada92b","e343c9364e1346d99fb20802bbe00e96","6ca776c6b2af417e9818017ee76a9d6b","aa53d60afce5456bba76ef44e2eacb06","d0f8ab2ce16f4884a56e06ffe4b63153","08346237e4034e948c121f07c2c79876","53322eaa3d904dfdb3dd5e9393adbcad","8180d9f944eb43f9bd1521fbb7fe376b","c3bcd2686b7e4cdb8157e8418512f231","ab9714ced21e4913975a40caaf39907e","cb86f41aaa624330a20fa36b97256fde"]},"id":"04ilYLXSjc32","executionInfo":{"status":"ok","timestamp":1736100706548,"user_tz":-330,"elapsed":33075,"user":{"displayName":"PRITHIV SAKTHI U R","userId":"04296443067141805926"}},"outputId":"78c05f27-0f04-4133-84d0-c13b8217e1b2"},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/689 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6090cb09a21e460287051daf0fcbc2a2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/724M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"334edb62beda45308864cc58a4d47c09"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e26a1e14c4ac44e98ec309d2879d8a3a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d0e5a0a3a70a4c71ad8f9395fb0cd5f8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/831 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d57a86c434c741b0bc7f3e35a8ada92b"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Device set to use cuda:0\n"]},{"output_type":"stream","name":"stdout","text":["[{'generated_text': 'Explain AGI ?\\n\\nA. AGI is a measure of the ability of a machine to perform a task.\\n\\nB. AGI is a measure of the ability of a machine to perform a task.\\n\\nC. AGI is a measure of the ability of a machine to perform a task.\\n\\nD. AGI is a measure of the ability of a machine to perform a task.\\n\\nAnswer: B\\n\\nQuestion 10. Which of the following is not a type of AGI?\\n\\nA. Machine learning\\n\\nB. Natural language processing\\n\\nC. Computer vision\\n\\nD. Speech recognition\\n\\nAnswer: B\\n\\nQuestion 11. Which of the following is not a type of AGI?\\n\\nA. Machine learning\\n\\nB. Natural language processing\\n\\nC. Computer vision\\n\\nD. Speech recognition\\n\\nAnswer: C\\n\\nQuestion 12. Which of the following is not a type of A'}]\n"]}]},{"cell_type":"code","source":["# Load the dataset\n","ds = load_dataset(\"prithivMLmods/Deepthink-Reasoning\")\n","\n","# Tokenization function with batch handling\n","def tokenize_function(examples):\n","    # Apply strip to each string in the batch\n","    prompts = [p.strip() for p in examples[\"prompt\"]]\n","    responses = [r.strip() for r in examples[\"response\"]]\n","\n","    # Apply chat template for each prompt-response pair\n","    texts = [\n","        tokenizer.apply_chat_template(\n","            [{\"role\": \"user\", \"content\": p}, {\"role\": \"assistant\", \"content\": r}],\n","            tokenize=False\n","        )\n","        for p, r in zip(prompts, responses)\n","    ]\n","\n","    # Tokenize the combined texts\n","    return tokenizer(texts, truncation=True, padding=\"max_length\", max_length=512)\n","\n","# Apply the tokenization function to the dataset\n","ds = ds.map(tokenize_function, batched=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":173,"referenced_widgets":["4e6e51f6e0c74fa69f442824227507ed","2120025bc50540689451c164230216b5","db7243984c904b0fb7db461f08b9fbbb","1ac413d7dc704167a9484452fbb94c2a","beb436c14ff04c6b9b3bf395cf35b64f","dcf5c445f3f34cbb89b00c8088535765","a9443ad66dfd4f8db81d3e455dd51dde","3a0c882da773418bbccc2176bfa7d93b","482eff3de2b64100a498ec6c078ed078","7f4dc140bd6e41548ea1c0c1f93cb393","8d3936785a234ebdb904a97290dc998b","7f3d889bf2784977b13626ec403fab5f","4e16e6f78b0c4cb6af6029625da4a88e","b27d37d100fd47a18eb2cc32973840db","af3087fda64b4afcacb8ab72a085513e","04dd714a251e462583f05e3219843713","5642dfdd01db441f85dda2f5de136364","930aebebbbfd4ea5af2fdc1213f95507","21c2baed80f64340951ecb23962daf9c","f4ef46c6a2544b3a812abdab69592a21","c990b89c26fd4cefbd37ec3307829843","e77e72597ff84d31b71d2597eaf0e5dc","807ad72fd69b4d79a9ef1e6f4c1f6d47","4cf00f1f30814976948e6c204cc64e4b","295ba0cc61f94ab8ac68b562b1c749ed","b96c382bc6ba4e2f8fdbebdf48f8a0ac","067380176b0e4107aa3b9cee5a4ec606","6d848776e76449b29b59aa8f8d41a4a2","8bffa2f5223c46c2b208f16b3d57b2f4","8a690ca779f7495a8eb95f146a95696a","a2bb74debd9c480f89ee49e9ec3fb340","4207311a154a4e8a85ded64f83ebd950","a74623005fa345a4a8ccdfd2fb0dc030","69d6f8d2c4704abd9de9126807f3c489","6a9451128a6d4ff08f44ead25ede236b","7cdce017e2b044d48b69473e0784ef1a","ea3454f10eb54bb5b385c68b0ee5782d","f3db377bb8774d33b2ca537e29b2ad51","b262c061869b43d7ad4a51b5362dfce7","f000882e8c5542dd8980a26b30f800e4","8bafaeaab23b4fbcb96905f0f53253ac","5103fab107664861a854b7069390414a","c2c5451096264ba4adc1758c3d459a1b","87dffa2d4d0848d491e93f66b0795b29"]},"id":"uJrzC756jvBu","executionInfo":{"status":"ok","timestamp":1736100711690,"user_tz":-330,"elapsed":3589,"user":{"displayName":"PRITHIV SAKTHI U R","userId":"04296443067141805926"}},"outputId":"905468b2-66b6-4146-c7c8-3c979314caa4"},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":["README.md:   0%|          | 0.00/1.78k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4e6e51f6e0c74fa69f442824227507ed"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["dataset/dataset_deepthink-lcot2.json:   0%|          | 0.00/991k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7f3d889bf2784977b13626ec403fab5f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/251 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"807ad72fd69b4d79a9ef1e6f4c1f6d47"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/251 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"69d6f8d2c4704abd9de9126807f3c489"}},"metadata":{}}]},{"cell_type":"code","source":["use_bf16 = torch.cuda.is_bf16_supported()\n","\n","# Prepare training arguments\n","training_args = TrainingArguments(\n","    per_device_train_batch_size=2,\n","    gradient_accumulation_steps=4,\n","    warmup_steps=5,\n","    max_steps=60,  # Adjust max_steps as needed\n","    learning_rate=2e-4,\n","    fp16=not use_bf16,  # Use fp16 if bf16 is not supported\n","    bf16=use_bf16,  # Use bf16 if supported\n","    logging_steps=1,\n","    optim=\"adamw_8bit\",\n","    weight_decay=0.01,\n","    lr_scheduler_type=\"linear\",\n","    seed=3407,\n","    output_dir=\"outputs\",\n","    report_to=\"wandb\",  # Use 'wandb' or 'tensorboard' if needed or pass 'none'\n",")\n","\n","# Initialize the SFTTrainer\n","trainer = SFTTrainer(\n","    model=model,\n","    processing_class=tokenizer,  # Use tokenizer as processing_class\n","    train_dataset=ds[\"train\"],  # Train dataset\n","    args=training_args,  # Pass the training arguments\n",")"],"metadata":{"id":"lcqd78Znj1Eu","executionInfo":{"status":"ok","timestamp":1736100715924,"user_tz":-330,"elapsed":563,"user":{"displayName":"PRITHIV SAKTHI U R","userId":"04296443067141805926"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["# Start training\n","trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"aXwPm-m1j-Lm","executionInfo":{"status":"ok","timestamp":1736101008005,"user_tz":-330,"elapsed":288803,"user":{"displayName":"PRITHIV SAKTHI U R","userId":"04296443067141805926"}},"outputId":"1f344867-ce93-4749-b0eb-238b36060ccd"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n","\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","        window._wandbApiKey = new Promise((resolve, reject) => {\n","            function loadScript(url) {\n","            return new Promise(function(resolve, reject) {\n","                let newScript = document.createElement(\"script\");\n","                newScript.onerror = reject;\n","                newScript.onload = resolve;\n","                document.body.appendChild(newScript);\n","                newScript.src = url;\n","            });\n","            }\n","            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n","            const iframe = document.createElement('iframe')\n","            iframe.style.cssText = \"width:0;height:0;border:none\"\n","            document.body.appendChild(iframe)\n","            const handshake = new Postmate({\n","                container: iframe,\n","                url: 'https://wandb.ai/authorize'\n","            });\n","            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n","            handshake.then(function(child) {\n","                child.on('authorize', data => {\n","                    clearTimeout(timeout)\n","                    resolve(data)\n","                });\n","            });\n","            })\n","        });\n","    "]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n","\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n","wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"]},{"name":"stdout","output_type":"stream","text":[" ··········\n"]},{"output_type":"stream","name":"stderr","text":["\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Tracking run with wandb version 0.19.1"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Run data is saved locally in <code>/content/wandb/run-20250105_181210-nshkq0s8</code>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["Syncing run <strong><a href='https://wandb.ai/prithivsakthi676-stranger-zone/huggingface/runs/nshkq0s8' target=\"_blank\">outputs</a></strong> to <a href='https://wandb.ai/prithivsakthi676-stranger-zone/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[" View project at <a href='https://wandb.ai/prithivsakthi676-stranger-zone/huggingface' target=\"_blank\">https://wandb.ai/prithivsakthi676-stranger-zone/huggingface</a>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[" View run at <a href='https://wandb.ai/prithivsakthi676-stranger-zone/huggingface/runs/nshkq0s8' target=\"_blank\">https://wandb.ai/prithivsakthi676-stranger-zone/huggingface/runs/nshkq0s8</a>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [60/60 04:29, Epoch 1/2]\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","    <tr>\n","      <td>1</td>\n","      <td>1.361400</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>1.517600</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>1.267000</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>1.295300</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>1.276700</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>1.048400</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>1.074100</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>0.900300</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>1.081800</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>0.804700</td>\n","    </tr>\n","    <tr>\n","      <td>11</td>\n","      <td>0.949700</td>\n","    </tr>\n","    <tr>\n","      <td>12</td>\n","      <td>0.766500</td>\n","    </tr>\n","    <tr>\n","      <td>13</td>\n","      <td>0.960400</td>\n","    </tr>\n","    <tr>\n","      <td>14</td>\n","      <td>0.815400</td>\n","    </tr>\n","    <tr>\n","      <td>15</td>\n","      <td>0.918900</td>\n","    </tr>\n","    <tr>\n","      <td>16</td>\n","      <td>0.804100</td>\n","    </tr>\n","    <tr>\n","      <td>17</td>\n","      <td>0.915500</td>\n","    </tr>\n","    <tr>\n","      <td>18</td>\n","      <td>0.742800</td>\n","    </tr>\n","    <tr>\n","      <td>19</td>\n","      <td>0.904100</td>\n","    </tr>\n","    <tr>\n","      <td>20</td>\n","      <td>0.722600</td>\n","    </tr>\n","    <tr>\n","      <td>21</td>\n","      <td>0.718100</td>\n","    </tr>\n","    <tr>\n","      <td>22</td>\n","      <td>0.959700</td>\n","    </tr>\n","    <tr>\n","      <td>23</td>\n","      <td>0.859400</td>\n","    </tr>\n","    <tr>\n","      <td>24</td>\n","      <td>0.777100</td>\n","    </tr>\n","    <tr>\n","      <td>25</td>\n","      <td>0.741500</td>\n","    </tr>\n","    <tr>\n","      <td>26</td>\n","      <td>0.689200</td>\n","    </tr>\n","    <tr>\n","      <td>27</td>\n","      <td>0.738800</td>\n","    </tr>\n","    <tr>\n","      <td>28</td>\n","      <td>0.822600</td>\n","    </tr>\n","    <tr>\n","      <td>29</td>\n","      <td>0.799700</td>\n","    </tr>\n","    <tr>\n","      <td>30</td>\n","      <td>0.855700</td>\n","    </tr>\n","    <tr>\n","      <td>31</td>\n","      <td>1.025700</td>\n","    </tr>\n","    <tr>\n","      <td>32</td>\n","      <td>0.848700</td>\n","    </tr>\n","    <tr>\n","      <td>33</td>\n","      <td>0.536000</td>\n","    </tr>\n","    <tr>\n","      <td>34</td>\n","      <td>0.455500</td>\n","    </tr>\n","    <tr>\n","      <td>35</td>\n","      <td>0.560600</td>\n","    </tr>\n","    <tr>\n","      <td>36</td>\n","      <td>0.457400</td>\n","    </tr>\n","    <tr>\n","      <td>37</td>\n","      <td>0.447900</td>\n","    </tr>\n","    <tr>\n","      <td>38</td>\n","      <td>0.383500</td>\n","    </tr>\n","    <tr>\n","      <td>39</td>\n","      <td>0.531000</td>\n","    </tr>\n","    <tr>\n","      <td>40</td>\n","      <td>0.502800</td>\n","    </tr>\n","    <tr>\n","      <td>41</td>\n","      <td>0.491000</td>\n","    </tr>\n","    <tr>\n","      <td>42</td>\n","      <td>0.372700</td>\n","    </tr>\n","    <tr>\n","      <td>43</td>\n","      <td>0.455700</td>\n","    </tr>\n","    <tr>\n","      <td>44</td>\n","      <td>0.508700</td>\n","    </tr>\n","    <tr>\n","      <td>45</td>\n","      <td>0.571100</td>\n","    </tr>\n","    <tr>\n","      <td>46</td>\n","      <td>0.598300</td>\n","    </tr>\n","    <tr>\n","      <td>47</td>\n","      <td>0.396800</td>\n","    </tr>\n","    <tr>\n","      <td>48</td>\n","      <td>0.481400</td>\n","    </tr>\n","    <tr>\n","      <td>49</td>\n","      <td>0.586000</td>\n","    </tr>\n","    <tr>\n","      <td>50</td>\n","      <td>0.387700</td>\n","    </tr>\n","    <tr>\n","      <td>51</td>\n","      <td>0.622800</td>\n","    </tr>\n","    <tr>\n","      <td>52</td>\n","      <td>0.484400</td>\n","    </tr>\n","    <tr>\n","      <td>53</td>\n","      <td>0.428100</td>\n","    </tr>\n","    <tr>\n","      <td>54</td>\n","      <td>0.408700</td>\n","    </tr>\n","    <tr>\n","      <td>55</td>\n","      <td>0.583800</td>\n","    </tr>\n","    <tr>\n","      <td>56</td>\n","      <td>0.545100</td>\n","    </tr>\n","    <tr>\n","      <td>57</td>\n","      <td>0.438300</td>\n","    </tr>\n","    <tr>\n","      <td>58</td>\n","      <td>0.376900</td>\n","    </tr>\n","    <tr>\n","      <td>59</td>\n","      <td>0.384500</td>\n","    </tr>\n","    <tr>\n","      <td>60</td>\n","      <td>0.474600</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=60, training_loss=0.7239148865143458, metrics={'train_runtime': 287.8826, 'train_samples_per_second': 1.667, 'train_steps_per_second': 0.208, 'total_flos': 459115683840000.0, 'train_loss': 0.7239148865143458, 'epoch': 1.8888888888888888})"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","source":["# Save model and tokenizer to a local directory\n","save_directory = \"/content/my_model\"\n","model.save_pretrained(save_directory)\n","tokenizer.save_pretrained(save_directory)\n","\n","# Zip the directory\n","import shutil\n","shutil.make_archive(save_directory, 'zip', save_directory)\n","\n","# Download the zip file\n","from google.colab import files\n","files.download(f\"{save_directory}.zip\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"Nzf-cCC_kKoo","executionInfo":{"status":"ok","timestamp":1736101309927,"user_tz":-330,"elapsed":109790,"user":{"displayName":"PRITHIV SAKTHI U R","userId":"04296443067141805926"}},"outputId":"eadb88fc-bc77-4adf-dcdb-8f85827a468c"},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n","    async function download(id, filename, size) {\n","      if (!google.colab.kernel.accessAllowed) {\n","        return;\n","      }\n","      const div = document.createElement('div');\n","      const label = document.createElement('label');\n","      label.textContent = `Downloading \"${filename}\": `;\n","      div.appendChild(label);\n","      const progress = document.createElement('progress');\n","      progress.max = size;\n","      div.appendChild(progress);\n","      document.body.appendChild(div);\n","\n","      const buffers = [];\n","      let downloaded = 0;\n","\n","      const channel = await google.colab.kernel.comms.open(id);\n","      // Send a message to notify the kernel that we're ready.\n","      channel.send({})\n","\n","      for await (const message of channel.messages) {\n","        // Send a message to notify the kernel that we're ready.\n","        channel.send({})\n","        if (message.buffers) {\n","          for (const buffer of message.buffers) {\n","            buffers.push(buffer);\n","            downloaded += buffer.byteLength;\n","            progress.value = downloaded;\n","          }\n","        }\n","      }\n","      const blob = new Blob(buffers, {type: 'application/binary'});\n","      const a = document.createElement('a');\n","      a.href = window.URL.createObjectURL(blob);\n","      a.download = filename;\n","      div.appendChild(a);\n","      a.click();\n","      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