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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"474da3c972314bdb92480d33e7041915"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/133 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c3f56c5097bc47e582dd0f0680427893"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.16M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"58f8b512b781448aa6642648fb3a413e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model:   0%|          | 0.00/4.69M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b84b763563f948b1aa279aced775f8b2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/33.4M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e11daa5e334d412894f9572b7c3b4574"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["added_tokens.json:   0%|          | 0.00/35.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"92f121e5fbd2426ab0849da126f27cff"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/662 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e3b51e569734403083a4e82844b42b43"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Device set to use cpu\n"]}],"source":["!pip install -U transformers\n","\n","from huggingface_hub import login\n","\n","login(new_session=False)"]},{"cell_type":"code","source":["# Import the pipeline class from the transformers library, that makes it easy to use pre-trained models for inference.\n","from transformers import pipeline\n","\n","# A text-generation pipeline - downloads and loads the google's model (\"google/gemma-3-270m\") and its associated tokenizer.\n","# The pipeline handles all the pre-processing (tokenization) and post-processing (decoding).\n","pipe = pipeline(\"text-generation\", model=\"google/gemma-3-270m\")\n","\n","# Defining \"prompt\", for the model.\n","prompt = \"Dog is barking at him since \"\n","\n","# Using the pipeline to generate new text based on the prompt.\n","# 'max_new_tokens' = the maximum number of new tokens (words/subwords) to generate.\n","generated_text = pipe(prompt, max_new_tokens=100)\n","\n","# The pipeline returns a list of dictionaries. The first dictionary contains the generated text.\n","# Access the generated text and replace double newlines with a single space for cleaner output.\n","print(generated_text[0]['generated_text'].replace('\\n\\n',' '))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"g-73fz529qWS","executionInfo":{"status":"ok","timestamp":1757513595451,"user_tz":-330,"elapsed":84,"user":{"displayName":"Vishnu Kishore","userId":"04285339965191879978"}},"outputId":"bf52df72-e7f4-4be4-c77e-f8d3f3fe51a2"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Dog is barking at him since 4.5 years and he is barking at him because he is hungry. <h2>1. What is the dog barking at?</h2> The dog is barking at him because he is hungry. <h2>2. Why is the dog barking at?</h2> He is barking because he is hungry. <h2>3. How does the dog bark at?</h2> The dog is barking because he is hungry. <h2>4. What does the dog say to the dog?</h2> The dog says\n"]}]},{"cell_type":"code","source":["from transformers import AutoTokenizer, AutoModelForCausalLM\n","\n","model_id = \"google/gemma-3-270m\"\n","\n","# Load the tokenizer and model\n","tokenizer = AutoTokenizer.from_pretrained(model_id)\n","model = AutoModelForCausalLM.from_pretrained(model_id)\n","\n","prompt = \"Dog is barking at him since \"\n","\n","# Manually tokenize the prompt\n","# This converts the text into numerical IDs that the model can process.\n","input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n","\n","# Generate text using the model\n","outputs = model.generate(input_ids, max_new_tokens=100)\n","\n","# Decode the generated numerical IDs back to human-readable text\n","generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n","\n","print(generated_text)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ow7bgca6-Uq4","executionInfo":{"status":"ok","timestamp":1757513632748,"user_tz":-330,"elapsed":19264,"user":{"displayName":"Vishnu Kishore","userId":"04285339965191879978"}},"outputId":"d6c32d28-1436-4643-9c91-3c3c7f2a258f"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Dog is barking at him since 5 months, that's a fact\n","\"I'm sorry. I told him this a couple weeks ago because I was trying to find a really cool puppy! I don't know who the one is...\"\n","The puppy looks like a puppy\n","This is from the Puppy: I'm Sorry - I am sorry for the pain that I've had from being so stupid. I'm sorry. I'm sorry.\n","Hi Dog, can I have your help? I\n"]}]},{"cell_type":"code","source":["from huggingface_hub import HfApi\n","from google.colab import userdata\n","\n","api = HfApi(token=userdata.get(\"HF_TOKEN\"))\n","api.upload_folder(\n","    folder_path=\"sample_data\",\n","    repo_id=\"VishnuKishore/vishnu-projects\",\n","    repo_type=\"model\",\n",")\n","\n","# !ls\n","\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":270,"referenced_widgets":["a450973f793d49a4bb66e571b928fa5a","7d1f7bc0f2d440d18e7e51f2a938a30d","c7d546c8e5eb41cfb7fb881c652a9d48","bdc96ebd85c840e794f1e3b51ff62a86","ff8b88b49d0c4869aa86f76a78236ecc","3a0906d620ba4861a6ecf61032fe2d39","cbae51b5caed4824a7b392aae444041d","05ff4c3a1b924711b08d98b87d41a31a","41a1e535ec96490a8ec8957b85737f29","e087f18007ed48f69bb3a5cc80ecb77c","4ff6284ad3e74051aa30e18cbb54745e","1e94d330b4324a82abd98e801a535c59","d7eead65339c4677956d3be943c37e6e","1ce6bc4c21a4492998b18bff7e891d56","ff5ccd7a4bb1433f8d67d8057a174056","36ec87c86c6d4a20bceb08d7ae6e680c","9e4465cb5f7448c588dfeba39dc89f1e","da11c577fc5047cc9e1c47c2960b1cfc","9dd64ae82bc849d9b2e27583a78b9e82","d25bc122d1924c9fb8c80fdd05d670dc","591c40b2edfb4fc59135ca029d227c95","cddddd79ee964a7d8c8ba13627fd8f36","095ffa3a3ae642ff9ccc162a656b7f90","782761f0c62046caa3ceaf55d7392845","3c384583c58042e2a7847107553a2cbf","8e37a3c00ae54d5282e79af64e64cc6c","3aa09c294b6e4d05a82f1eda9a440d8e","a67aff72caa342c589032059656858d7","22d9e359ff104ef196adb6d828dffa7d","9cc98b0f08714525ab05d229df0ca216","d3c946e586934f0c8059d9063f1639ae","fa366af6989f46c6bfc44e50eee40eef","b29a8a3b9f8b4da1a1ab452884d0fb81","1680743d94e840608b9392b715fcf21f","3137645c42b64f3393ac1672c85fba45","1d12b78a17484050a819b53d9e2ff85d","4faacdbb4d7d4fb19976dbfcf913f0aa","70e3c88232cd41ad9b4c03664f850675","e67bc40ae0014564ab055d077185ab41","bce470353d9b401b84bfc4fb04f95a57","925e1e4e4f2a4137be0aa874927b0005","95359b335f7c4236aa52f5dd92cca440","478aa2d651d3417a8ab6ae8771253f16","b7aa8c08e90049fe850d9280b4526080"]},"id":"t6h98ND4AExp","executionInfo":{"status":"ok","timestamp":1757514489883,"user_tz":-330,"elapsed":3263,"user":{"displayName":"Vishnu Kishore","userId":"04285339965191879978"}},"outputId":"f04a0c9a-15f8-47fc-d4d0-ef315fd58d22"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/hf_api.py:9706: UserWarning: Warnings while validating metadata in README.md:\n","- empty or missing yaml metadata in repo card\n","  warnings.warn(f\"Warnings while validating metadata in README.md:\\n{message}\")\n"]},{"output_type":"display_data","data":{"text/plain":["Processing Files (0 / 0)                : |          |  0.00B /  0.00B            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a450973f793d49a4bb66e571b928fa5a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["New Data Upload                         : |          |  0.00B /  0.00B            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1e94d330b4324a82abd98e801a535c59"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  /content/sample_data/mnist_test.csv   : 100%|##########| 18.3MB / 18.3MB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"095ffa3a3ae642ff9ccc162a656b7f90"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  ...t/sample_data/mnist_train_small.csv:  23%|##2       | 8.34MB / 36.5MB            "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1680743d94e840608b9392b715fcf21f"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["CommitInfo(commit_url='https://huggingface.co/VishnuKishore/vishnu-projects/commit/c290e6b87f5b7ee37dff86e44095571dec649c32', commit_message='Upload folder using huggingface_hub', commit_description='', oid='c290e6b87f5b7ee37dff86e44095571dec649c32', pr_url=None, repo_url=RepoUrl('https://huggingface.co/VishnuKishore/vishnu-projects', endpoint='https://huggingface.co', repo_type='model', repo_id='VishnuKishore/vishnu-projects'), pr_revision=None, pr_num=None)"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["# This is a command-line example to copy your notebook into the sample_data folder\n","# Replace 'your_notebook_name.ipynb' with the actual file name\n","from google.colab import drive\n","\n","# Mount Google Drive to access your files\n","drive.mount('/content/drive')\n","\n","# Copy the notebook from its location in Google Drive to the local Colab folder\n","# You may need to adjust the path if your notebook is in a different folder\n","!cp '/content/drive/MyDrive/Inference_Project_First.ipynb' '/content/sample_data/'"],"metadata":{"id":"vmLU0xRUD0UP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"FAwutP-AEOHZ"},"execution_count":null,"outputs":[]}]}