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+ }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7AfirDAnEpMu", + "outputId": "b9045a82-c832-46ba-84e3-07020bff65af" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m133.9/133.9 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.7/265.7 kB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta 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bitsandbytes einops wandb" + ] + }, + { + "cell_type": "code", + "source": [ + "import datasets\n", + "from datasets import load_dataset\n", + "dataset_name = \"OpenAssistant/oasst1\"\n", + "dataset = 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", + 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"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", + 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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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" + ] + }, + "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": "https://localhost:8080/" + }, + "id": "dsRED0ImXgYP", + "outputId": "9dbecaa7-da5f-4011-d0bc-21a91b3cae75" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[0m\u001b[01;34mcheckpoint-10\u001b[0m/ \u001b[01;34mcheckpoint-280\u001b[0m/ \u001b[01;34mcheckpoint-470\u001b[0m/ \u001b[01;34mcheckpoint-660\u001b[0m/ \u001b[01;34mcheckpoint-850\u001b[0m/\n", + "\u001b[01;34mcheckpoint-100\u001b[0m/ \u001b[01;34mcheckpoint-290\u001b[0m/ \u001b[01;34mcheckpoint-480\u001b[0m/ \u001b[01;34mcheckpoint-670\u001b[0m/ \u001b[01;34mcheckpoint-860\u001b[0m/\n", + "\u001b[01;34mcheckpoint-1000\u001b[0m/ \u001b[01;34mcheckpoint-30\u001b[0m/ \u001b[01;34mcheckpoint-490\u001b[0m/ \u001b[01;34mcheckpoint-680\u001b[0m/ 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