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load_dataset(dataset_name, split=\"train\")" ], "metadata": { "id": "jwhnmTANEsdV", "colab": { "base_uri": "https://localhost:8080/", "height": 241, "referenced_widgets": [ "fc19637d59e941e89223a468678ca231", "a2bc577acc1542b294da0d9f2989aaaf", "a228a1b8463f4a5d80f477dcb5330ea7", "4f4d1fa751a149b98b7941fa3d57a3c1", "0b6652aede0c4c49ade13b338932d762", "79186170581442d5aa623e78ed95d2f7", "1d162f5daabc42d3b716a85baf503c91", "340bb6f0e4604356b9eb6f97128da153", "f04b1de5a81246548fc8a3e61ba271e6", "57b4c38da02b48b99db7f8dcf2783ba1", "66609d213c494adbbc1581b94b3bfb31", "09a347fb1a39479e987c0a53b412af97", "818677db34544b6f9dda67f0cd07a621", "5b38dd13e8234c4c896c719589e9074f", "1de32510b3cc4b81bd626795aa0bd311", "118b53b9e7c84dbd83fe0a2c1bff37a0", "ca06c14a2ace4780af24285a2286c6dd", "e0fa5eeb03a94d378543400663a431da", "8d43cd9a326d41bf9a60fe76cfe72fcc", "3de7ba94c801456cbe6243584e3e5541", "dd18818399b6486986b2cb6d4d155bf6", "72e157463ccf4b1681877c1eab9d23b1", "666ab7a989354e95b18ed1862b82da14", "b8d200f118d141229ca82846a6f3a621", "e69cf85f3782439cbff58ec2e55ed250", "a8b9021f3dfc46d1bc06849f736ecb2c", "3656f1cd27e24a3fa782343016c7adf6", "d2073794a52e41b5914385981f4ad468", "e20874a3b29e4c1691fb07a1a0f9653a", "5906136aa165421b895e14a3dc2d2d38", "b51538c5fdda4fff8ed0e7f06d5ea956", "63b53ce7bb5a487bb0e0c24eea55732d", "7152418562e94dc7a0debdb1186109c3", "04713e4335124ed4994677479b07a488", "00c68eac1f784ca1a2912b11bf167fa9", "65607c5ea25448c1aa6af2b1831430cf", "5c4c203f792c469f89ebdc9e21570198", "78a37f41ab8045ad99de453c03988b4d", "e83b3c03629a4f0099117f9684a40129", "bb8a343eef1f4ea5a602cea80a865b10", "927c06198c1c4149b6c871ae33eafeaa", "2e56a1254f3945eb9649f1baecdb30bd", "c20d5d6ff7e14a0cb0a5de7f80e85160", "957d0e9008ef487a964247e27414ffdc", "c97794a1377e4845a6670c927a2d7cdc", "37f5b7bdadd94623bba6bb4c91df97cb", "fee969b254d147d8b4c09d8109fed246", "3cb42507a86f48a698a2236d8b8568fa", "4cd0fd1b6b814e048560465e778e0b20", "c27f6f1ad3804d669d6207078ffa35f5", "9f648e7fb8324cf9bae90e13da6670cf", "7e6f5bf3cb0e4be6a5666835b4860a9c", "7c048b97bbe54d829d14c2f7bcc5eba2", "6f2e77b1b9ba44d986c856841cf71eeb", "0efac2b76e414c5b8b0f8fa0cf813434", "85d0fd0bfd8342b68730e532cb1841d6", "ec2f2a1d5fad45ab93c347522e204fe0", "dd6f057054e7468c8a41b9c7b40b780a", "6a6c284c0e7b4a69ba201d6cc307d459", "3e171a2bf700463bacbc070c1ff81d89", "12e65694292c4afd8afa234e1c75ac89", "4126f8b5deef4800ac4b0cabd9b81fa7", "2d65782afa61435aa80a726f776b5fb9", "f3093476fe274e20a067d2af51558174", "0d575b85c8704583999901c9a39dd123", "e65927145489439ab83edef5f27a01a2", "acb7e3579e544eb2bbc58c36531c790e", "f2a4790070ea499f9383274a461024db", "582319a681084a35994de4c4cf77b6c1", "030335dbdfca4f658631db2d948d957a", "cf58d798af0847358cb047763d1cb57b", "88418904c17b4cf5956a310c4dffa2fa", "46ebdb0292b141979f1bb9bd0c095441", "e53c6abfbf5841a88c48a7d7770631d9", "e6138544952746dd8aca097a5643c2b6", "69b5c39bd71f4223b1a55fc2001e32b1", "a2030484ddbc4f5da325fc58a689254d" ] }, "outputId": "b30abfaa-5303-4067-dbb4-a262b4d25db0" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/10.2k [00:00:12: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " assistances['prompt_assistance_text'] = assistant_prompt_values\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "['### Human: Can you write a short introduction about the relevance of the term \"monopsony\" in economics? Please use examples related to potential monopsonies in the labour market and cite relevant research.### Assistant: \"Monopsony\" refers to a market structure where there is only one buyer for a particular good or service. In economics, this term is particularly relevant in the labor market, where a monopsony employer has significant power over the wages and working conditions of their employees. The presence of a monopsony can result in lower wages and reduced employment opportunities for workers, as the employer has little incentive to increase wages or provide better working conditions.\\n\\nRecent research has identified potential monopsonies in industries such as retail and fast food, where a few large companies control a significant portion of the market (Bivens & Mishel, 2013). In these industries, workers often face low wages, limited benefits, and reduced bargaining power, leading to a situation where they are dependent on the employer for their livelihood. This dependence can result in further suppression of wages and a decline in working conditions.\\n\\nOverall, the concept of monopsony is essential to understanding the dynamics of labor markets and the impact of market power on workers. Further research is needed to understand the extent and impact of monopsonies on the economy and to develop policies to address this issue.\\n\\nReferences:\\nBivens, J., & Mishel, L. (2013). The Pay of Corporate Executives and Financial Professionals as Evidence of Rents in Top 1 Percent Incomes. Journal of Economic Perspectives, 27(3), 57-78.',\n", " \"### Human: What can be done at a regulatory level to ensure, in a monopsony, that the power over the employees isn't abused? List a few options and focus on the organs that should act.### Assistant: Here are some potential regulatory options to address monopsony power over employees:\\n\\n- Antitrust enforcement agencies like the FTC or DOJ could investigate and take action against dominant employers that are abusing their market power to suppress wages or worsen working conditions. They could force divestitures or other structural remedies.\\n- Labor standards agencies could set industry-specific minimum wage floors or other regulations around compensation and working conditions. This would reduce the ability of dominant employers to push wages/conditions below certain thresholds.\\n- Sector-specific regulators could impose requirements around how companies hire, compensate, and treat workers. For example, banking regulators could prohibit abusive practices by dominant banks with regard to their employees.\\n- Legislatures could pass laws\",\n", " '### Human: ¿CUales son las etapas del desarrollo y en qué consisten según Piaget?### Assistant: Jean Piaget fue un psicólogo suizo que propuso una teoría sobre el desarrollo cognitivo humano que consta de cuatro etapas:\\n\\nEtapa sensoriomotora (0-2 años): Durante esta etapa, el niño aprende a través de sus sentidos y movimientos. Descubre que sus acciones pueden tener un impacto en el entorno y comienza a formarse una idea básica de objetividad y continuidad.\\n\\nEtapa preoperatoria (2-7 años): En esta etapa, el niño comienza a desarrollar un pensamiento simbólico y a comprender que las cosas pueden representar a otras cosas. También comienzan a desarrollar un pensamiento lógico y a comprender conceptos como la causa y el efecto.\\n\\nEtapa de operaciones concretas (7-12 años): Durante esta etapa, el niño desarrolla un pensamiento lógico y comprende las relaciones causales. Empiezan a comprender que las cosas pueden tener múltiples perspectivas y que los conceptos pueden ser más complejos de lo que parecen a simple vista.\\n\\nEtapa de operaciones formales (12 años en adelante): En esta etapa, el individuo desarrolla un pensamiento abstracto y puede comprender conceptos complejos y abstractos. Son capaces de razonar hipotéticamente y tienen la capacidad de reflexionar sobre su propio pensamiento.\\n\\nEstas etapas no son lineales y algunos individuos pueden avanzar en una etapa más rápidamente que en otras. La teoría de Piaget sobre el desarrollo cognitivo ha sido ampliamente utilizada y es una base importante para la investigación y el entendimiento del desarrollo humano.']" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "code", "source": [ "import torch\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n", "\n", "model_name = \"microsoft/phi-2\"\n", "\n", "bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", " bnb_4bit_compute_dtype=torch.float16,\n", ")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " model_name,\n", " quantization_config=bnb_config,\n", " trust_remote_code=True\n", ")\n", "model.config.use_cache = False" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 429, "referenced_widgets": [ "77168d66f84e4a769afaa12aa1b19fc4", "70292b80ab854eac905b33435ed13b52", "555dd6c9b2224ae587c5e86cd1cccfdc", 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(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": [ "" ], "text/html": [ "Tracking run with wandb version 0.16.1" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Run data is saved locally in /content/wandb/run-20231222_062256-c9x25iqt" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Syncing run fanciful-serenity-1 to Weights & Biases (docs)
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StepTraining Loss
101.727000
201.729200
301.741600
402.015800
502.143000
601.646900
701.642300
801.666900
901.919000
1002.155700
1101.598900
1201.691200
1301.565000
1401.914600
1502.089000
1601.536500
1701.626800
1801.688800
1901.956900
2002.115300
2101.551100
2201.605400
2301.598500
2401.847300
2501.994900
2601.593200
2701.596800
2801.557700
2901.943000
3002.142900
3101.505300
3201.669700
3301.567000
3401.849800
3502.075800
3601.562700
3701.535800
3801.716700
3901.993200
4002.063200
4101.522700
4201.619200
4301.608700
4401.939600
4502.102600
4601.625000
4701.560900
4801.542000
4901.943900
5002.057600
5101.582600
5201.608400
5301.645100
5401.819300
5502.049500
5601.628800
5701.578600
5801.658500
5901.904500
6002.005300
6101.549100
6201.607700
6301.634600
6401.941400
6502.167200
6601.598700
6701.583300
6801.556400
6901.866900
7002.105600
7101.569900
7201.608900
7301.652400
7401.821000
7502.097300
7601.560700
7701.542800
7801.583600
7901.991300
8001.976200
8101.560300
8201.591000
8301.486000
8401.891200
8501.984300
8601.601200
8701.607800
8801.587000
8901.911600
9002.036300
9101.546600
9201.604000
9301.637200
9401.938700
9501.989700
9601.511600
9701.564700
9801.677800
9901.802700
10002.005900

" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "TrainOutput(global_step=1000, training_loss=1.7569399700164794, metrics={'train_runtime': 2327.0056, 'train_samples_per_second': 6.876, 'train_steps_per_second': 0.43, 'total_flos': 5.191103660531712e+16, 'train_loss': 1.7569399700164794, 'epoch': 0.89})" ] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "code", "source": [ "from transformers import (\n", " AutoModelForCausalLM,\n", " AutoTokenizer,\n", " BitsAndBytesConfig,\n", " HfArgumentParser,\n", " TrainingArguments,\n", " pipeline,\n", " logging,\n", ")" ], "metadata": { "id": "3ORw79QrE4J3" }, "execution_count": 13, "outputs": [] }, { "cell_type": "code", "source": [ "# Run text generation pipeline with our next model\n", "prompt = \"What is a large language model?\"\n", "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n", "result = pipe(f\"[INST] {prompt} [/INST]\")\n", "print(result[0]['generated_text'])" ], "metadata": { "id": "zSKUQpUiyYii", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7461e516-06cf-43ab-a872-3822302358b1" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1518: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use and modify the model generation configuration (see https://huggingface.co/docs/transformers/generation_strategies#default-text-generation-configuration )\n", " warnings.warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[INST] What is a large language model? [/INST]>\n", "\n", "[INST] What is a large language model?[/INST]>\n", "\n", "A large language model is a type of artificial intelligence model that is trained on a massive amount of text data. It is designed to be able to generate human-like text, and can be used for a variety of applications, such as generating text, summarizing text, and even generating images.\n", "\n", "Large language models are typically trained on large datasets, such as Wikipedia or other text corpora. They are then trained using a deep neural network architecture, such as a transformer, which allows them to learn the relationships between words and phrases in the text.\n", "\n", "Large language models are often used in conjunction with other AI models, such as natural language processing models, to create more advanced AI systems. They are also used in a variety of applications, such as chatbots, virtual assistants, and even in the development of\n" ] } ] }, { "cell_type": "code", "source": [ "# Run text generation pipeline with our next model\n", "prompt = \"What is a model explainability?\"\n", "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n", "result = pipe(f\"[INST] {prompt} [/INST]\")\n", "print(result[0]['generated_text'])" ], "metadata": { "id": "fEGC0eoeye5A", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f11307b4-6b0e-414f-a1e9-3e2c7d4a8b95" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[INST] What is a model explainability? [/INST]What is a model explainability?\n", "\n", "Model explainability is the ability to understand how a model makes decisions and predictions. It is important because it helps us understand how the model works and how to improve it. It also helps us understand why the model made a certain decision or prediction, which can be useful in many applications.\n", "\n", "What are some examples of model explainability?\n", "\n", "Some examples of model explainability include:\n", "\n", "- Understanding how a neural network makes predictions\n", "- Understanding how a decision tree makes decisions\n", "- Understanding how a linear regression model makes predictions\n", "- Understanding how a random forest makes predictions\n", "- Understanding how a support vector machine makes predictions\n", "- Understanding how a deep learning model makes predictions\n", "- Understanding how a reinforcement learning model makes decisions\n", "- Understanding how a reinforcement learning model makes decisions\n" ] } ] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/gdrive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YYFzlmP-U53f", "outputId": "51c3d260-1b75-4195-b3c7-7142a09cecde" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/gdrive\n" ] } ] }, { "cell_type": "code", "source": [ "ls\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Tg32BPOIWdEV", "outputId": "69ecea29-93c3-4bf7-cba3-76c274696cd8" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[0m\u001b[01;34mgdrive\u001b[0m/ \u001b[01;34mresults\u001b[0m/ \u001b[01;34msample_data\u001b[0m/ \u001b[01;34mwandb\u001b[0m/\n" ] } ] }, { "cell_type": "code", "source": [ "cd results\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sS04qDi8Wt4y", "outputId": "6224b170-22d4-4d4f-8182-90d4cf75ce54" }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "/content/results\n" ] } ] }, { "cell_type": "code", "source": [ "ls" ], "metadata": { "colab": { "base_uri": 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