pt-br/1_instruction_tuning/notebooks/chat_templates_example.ipynb (5,725 lines of code) (raw):

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "vZAvFVIAtFlq" }, "source": [ "# Explorando Modelos de Bate-papo com SmolLM2\n", "\n", "Este caderno demonstra como usar modelos de bate-papo com o modelo `SmolLM2`. Modelos de bate-papo ajudam a estruturar as interações entre os usuários e os modelos de IA, garantindo respostas consistentes e contextualmente apropriadas." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "K-lZu8JvtwUN", "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e" }, "outputs": [], "source": [ "# Instale os requisitos no Google Colab\n", "# !pip install transformers datasets trl huggingface_hub\n", "\n", "# Authenticate to Hugging Face\n", "from huggingface_hub import login\n", "\n", "login()\n", "\n", "# Por conveniência, você pode criar uma variável de ambiente que contém seu token do hub como HF_TOKEN" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tnHzBR7vtFlr" }, "outputs": [], "source": [ "# Importe módulos necessários\n", "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "from trl import setup_chat_format\n", "import torch" ] }, { "cell_type": "markdown", "metadata": { "id": "XTVOqbuetFlr" }, "source": [ "## Modelo de bate-papo SmolLM2\n", "\n", "Vamos explorar como usar um modelo de bate-papo com o modelo `SmolLM2`. Vamos definir uma conversa simples e aplicar o modelo de bate-papo." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 397, "referenced_widgets": [ "b922b90106414644bc0e933f28dea1bf", "e0a40f83ae2e4ab29376a1d48b53aa6e", "547eeb64ffd34e509c0b8b8ba6d657e2", "45675fb5f5c94f8cae575582f7ae41a7", "016d5e929f1240cea067372b2191d107", "a026a32dd6d646bea82c1ebb06147d89", "0479fd3fc1ba476ab46f8c0a98f89468", "cbc312cb858b48a5a0f8dbcf60b7e684", "f70401b6dba74380b19bd1ef887b3bf7", "7eb91920e4384194a008902d6c4a09c7", "b379da78cb34463aa5a72eedc3d176cd", "ae2690497e024095adb3879643cffd33", "f600aa1fe4094133888ec9a2504a60eb", "efe9a9fcebfe441b80075fbfe9c32674", "50dbf8861ca94b0ba1f4a7e2f0d8aead", "547151540399460fb9a946bbe67afbd9", "5291041c86db4933816088c047d659d8", "48724ba7ba4e4f00923445245640739f", "04ae3f7b640c42f3a8eb1977cd1a585d", "db3bd55d779947028f36a8b24a2621b6", "d17c62b889754b5d88cfced5b18ff7a7", "990f706db474450ba0997d1dbcd53cb7", "3b881514716c47308061fe85b810a6a4", "26ed0f1bae204d74a313d101d9355e90", "4ff5af1784904bc9b85515105885e2d8", "b3c42d7e25d6494993029531adc3866d", "6227b40396ea4024b3c8710c5e65601f", "7612cc9b8908471b90c9118151d6e447", "b687aca79e6e470b96254c5e309d6d63", "3fa18e3b50104af796bd0887f556224a", "4bfa3103048a47989a09a0d90ac6b9bf", "85de66e1ee3140cf85eadebe5fea1e9f", "b31de9bcf83e4070be09c7d663361232", "d64d50101891491f96ff80162dc6d26c", "d65ec0f0dc0b44e0869c6159e6e82ad6", "76febcd912404a58add3a39f80a8218d", "f4ea276bdc0d4da2a04b46e3f1ed95b5", "0942430d36de4677b4c2fa771d7bcd2a", "10a0f37020d44156a11e9750778892e0", "58fb913274b54a60a832513c09608a2f", "0bab42beb845475684e9e71dd1591e1d", "89ecd1b28ab64c90afe3b9736fd48306", "be4e145938054f13a510fe4d04a7a60d", "648c3c820b39493daf0cce5f57a55467", "01e0f8a799ad479eb95eef3e5a09bd70", "8fe2df9a14a0436c9124a856ac7419e4", "d108e029e743419989e30f64f0c82b90", "bfd11f21f197459b8f27ef364bc9b264", "76a0341ebe9f4c3face32460d7023be9", "da1a999fb5af4eae9f6a9d1086cbb4cf", "77f6c27c3c854138b4aa9789637141a1", "6ceb292f2b8544f2a9a005d16d3e8978", "41a27cf0a91246599d4d1b7dae7c7863", "745fb1db425e44e5b3a23b36ae7675d1", "bde95b39561145548fc81fb4cc94a1bf", "3cc519fd92fe4b328943ec839115b63e", "e15fc503bb73476980cedb5f06b51ced", "d8c5dc8df3be4e65b2bbba020d29150f", "c0177c4ad18740d88acfc603ce4735f8", "eb570fd159124e2cbd2df9335b3f9cd6", "5de5dab3d92f4f41838a8f302d27f0c3", "471b481a3e5b4d439ab31fdc49fc99c7", "7a0c705334694da6b750104b28db6dba", "0c336ea5c653434da49e2f0e949f83d0", "ec15d99b3a604405a2b4707931d4bf44", "e7f5d507d9564941bb7db742b4bf01c7", "aa2d32cb76ba47ebaa5ea391efbf58a7", "7b20c7c8f6be40c6815b8531ecb9c936", "e90b58981bd34d0e8f975fc1a9658c4c", "5b7b09d983844f7893bdda411f9a0076", "70f0eaed6ef14c2db8aecb592edeb1ad", "d32017fa83aa44f6b2e3443a602654be", "ff8debfb713f4b88be6b9b3bf33bfca2", "ed577dea3ac54884a637ad775b42bc68", "d43410dfcc8c4bebb8672f10ed2aeb66", "0206fb9662a349c1aa8a6d87ce01c388", "881b6196dfa0446e8c55a2420e484b6b", "d54fb2da9f1f4a89ae962b8816314f43", "77d3d81687e6417ab988b04984fc68f4", "fbce0a69847e4099a55d1e39d4118c91", "1513792bad534a0c9c381a131395c519", "69f38fecf8ad403898634cfdfadf8925", "17023310de9b4c3ebd8cc03758d59ef9", "f3e23f781bce4429954d76bfea97aff4", "530fc4c2bf1244628af7dea3e4b35cdf", "96c2aae9198441569362135ad4bcbc98", "76d306c21214412ab44e542d82e547aa", "b9e41ef9e9c54fa7b71bc333604af74e" ] }, "id": "Nrxh0oX6tFls", "outputId": "953e1527-8168-4346-9338-6e188ca31a1a" }, "outputs": [], "source": [ "# Defina dinamicamente o dispositivo\n", "device = (\n", " \"cuda\"\n", " if torch.cuda.is_available()\n", " else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n", ")\n", "\n", "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n", "model = AutoModelForCausalLM.from_pretrained(\n", " pretrained_model_name_or_path=model_name\n", ").to(device)\n", "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n", "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zkJwILrbtFls" }, "outputs": [], "source": [ "# Defina mensagens para SmolLM2\n", "messages = [\n", " {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n", " {\n", " \"role\": \"assistant\",\n", " \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n", " },\n", "]" ] }, { "cell_type": "markdown", "metadata": { "id": "Ve4dgtjstFls" }, "source": [ "# Aplique o modelo de bate-papo sem tokenização\n", "\n", "O tokenizador representa a conversa como uma string com tokens especiais para descrever o papel do usuário e do assistente.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pbAg-5x-tFls", "outputId": "5f9482db-1fcf-4c13-ccaa-ef3f6eff7f76" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Conversation with template: <|im_start|>user\n", "Hello, how are you?<|im_end|>\n", "<|im_start|>assistant\n", "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", "\n" ] } ], "source": [ "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n", "\n", "print(\"Conversation with template:\", input_text)" ] }, { "cell_type": "markdown", "metadata": { "id": "sfvdglOqtFls" }, "source": [ "# Decodifique a conversação\n", "\n", "Observe que a conversação é representada como acima, mas com uma mensagem adicional do assistente.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mXUVdPeytFls", "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Conversation decoded: <|im_start|>user\n", "Hello, how are you?<|im_end|>\n", "<|im_start|>assistant\n", "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", "<|im_start|>assistant\n", "\n" ] } ], "source": [ "input_text = tokenizer.apply_chat_template(\n", " messages, tokenize=True, add_generation_prompt=True\n", ")\n", "\n", "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))" ] }, { "cell_type": "markdown", "metadata": { "id": "UcZQpspEtFlt" }, "source": [ "# Tokenize a conversação\n", "\n", "Obviamente, o tokenizador também tokeniza a conversa e o token especial como IDs relacionados ao vocabulário do modelo." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jc2PLxAMtFlt", "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n" ] } ], "source": [ "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n", "\n", "print(\"Conversation tokenized:\", input_text)" ] }, { "cell_type": "markdown", "metadata": { "id": "m3eNp9a0tFlt" }, "source": [ "<div style='background-color: lightblue; padding: 10px; border-radius: 5px; margin-bottom: 20px; color:black'>\n", " <h2 style='margin: 0;color:blue'>Exercício: Processe um conjunto de dados para SFT</h2>\n", " <p>Pegue um conjunto de dados do hub do Hugging Face e processe-o para SFT. </p>\n", " <p><b>Níveis de Dificuldade</b></p>\n", " <p>🐒 Converta o conjunto de dados `HuggingFaceTB/smoltalk` para o formato chatml.</p>\n", " <p>πŸ• Converta o conjunto de dados `openai/gsm8k` para o formato chatml.</p>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 381 }, "id": "qbkXV2_ItFlt", "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f" }, "outputs": [ { "data": { "text/html": [ "<iframe\n", " src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n", " frameborder=\"0\"\n", " width=\"100%\"\n", " height=\"360px\"\n", "></iframe>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.core.display import display, HTML\n", "\n", "display(\n", " HTML(\n", " \"\"\"<iframe\n", " src=\"https://huggingface.co/datasets/HuggingFaceTB/smoltalk/embed/viewer/all/train?row=0\"\n", " frameborder=\"0\"\n", " width=\"100%\"\n", " height=\"360px\"\n", "></iframe>\n", "\"\"\"\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241, "referenced_widgets": [ "c2d74a42fb574b8892d0a288fd92f0a6", "056b9ef5706843b19cd62fce75743afb", "17b4d81e40564a53bb79be9fbef4918e", "951f60cddcb84dfdbbdf2058369f0541", "646484cf7a36444daebe1dfe4a0e4150", "e2f0c39ce1c046e8acb150dfbfaf5aa8", "7eb12d70d2b542a7b651c7680f590279", "ea1f9cb22abf4e7d9f6e76fc86c03387", "00c9f5ca71b84df4b26acee72c97fefb", "505f96bc0c7843bcb1498ba1c1ba5f06", "635cc2881a1e4b8788bb26c356740e04", "a6ee323c13904525a99c6f092ba96e18", "67fffe7d6f8c4963972b408529e05532", "0055b6b628934affaf88bc58a1572bb6", "aafbbb9fc5164fa3a88193bfd33d2f79", "606e39d53ed64967a60337418c71c595", "26b15fa18b1b4963a1ba76a76675e7ee", "db09ab1f79db4f3a8de77f0348eca0f7", "de04f344a8d4428e8ba1836a563d8aa1", "03c09673186046d799d6f487d6623e6b", "1cc682330b24431b8812c73041e987d0", "dafe748a452148038779f6a62a22a4ec", "addad1c100024c44a0959978153da9a8", "9bea2a23db644ad19b708d10e35d54ee", "d1174b127571420593971166fbb1966b", "add90ed3746d4293a1b71198137a892c", "8def25e6389f4e6192b517b6e80aa05e", "c9747e7a810f413ba1ea108307e3ad1d", "d0ea49d1d90f4d34bf2ae70efa96946e", "59d0997b85614384bbfebeee928340b6", "269920491c134501873e0110367bc984", "384d26051c04460e8870a3ffe9406c48", "8e8a0e89a50646c897e546c4077db79e", "ff60308921f9432683acbcd6d29fb78f", "3bc8f6339f4e4a3b961d810255c5573e", "4780ad263ec04b1a97525d985e102049", "488feef55878426bbf1c753c6d58735b", "560ba45d70ca431dadeb327d234c330a", "04d0a6f74af346f7bc696951949063c8", "2a18ce941b0f4cef8307988ef898b47f", "194e3fda3635466b998f96e3dc22746a", "e2ab3cb38b5a41f68d18ed5f0e6ae22c", "f0b271bcac6c43a9aaddac54259bb514", "0dc93d50a283472f9ca64fd0a4c6ff15", "dd1a50d4497144388a1809b78bb38f58", "6b72a856e5bd4812a5e0dd0c3bfb8455", "4e21a567d1f6461985727823b37166e1", "ec1efb7598fd496bb170673ae1b8a1df", "84f393468aa74baa903243d238b2d387", "a54ce365be104d27aaa15cf8c63b5ebe", "1791220377d141ac9b307246177d0712", "fa330d4f0fb241aebd065f6ef4a6892c", "cfa1cc6eed8a4f7791a7959308456b6b", "b50c9c4433854cf7a6b2593e946b7faa", "7557cd24ba9b4aa3955866d59db94519", "cc608dfb880c49d4bc5acf2d691b8ec6", "cb838c5bed994a9a8e6fcf5c98b76d17", "76bbe8c2beba4c0594085d32a68d2ee7", "c9836c952b07472880649b82e2347e8d", "383db57f997140d482b82b123080837a", "182abc7ec4d944d9bb2ec1281c98b4c8", "6934c6d1cbac44dbb08f3fffe3056edb", "05fa0f6eb78b4c56b219b0e57521bd2e", "012aa94e3cf24e32833c6bbca23c52f7", "76c1a1cdc9054bbe90d0d3b662cf0ed1", "e453f1672772400a851735ba64f42c8b", "d1358f6b16644cb3a2328ca639a4a77a", "c19f60d4028045399c62004027eaafd9", "8055588a1fa940239c801ef66f3ecf3b", "7468a9bc8bda44e5b44574c64fdc6803", "a13a8f8b702e44ed88c7d358a0a8b4b4", "13367fbb763747fa8de94cde40ffae32", "b1fcf477db664ccdade4096fb79de327", "9d1c06ac6b774d82adca58773f389161", "31910159cf30463b8246ec47ffd8ab5b", "72220420f9d340eabec13a01caebc92c", "55b14c03a41c495aacf8ac2d0f96ba0b" ] }, "id": "4p3atw4_tFlu", "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8" }, "outputs": [], "source": [ "from datasets import load_dataset\n", "\n", "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n", "\n", "\n", "def process_dataset(sample):\n", " # TODO: 🐒 Converta a amostra em um formato de bate-papo\n", " # Use o método do tokenizador para aplicar o modelo de bate-papo\n", " return sample\n", "\n", "\n", "ds = ds.map(process_dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 381 }, "id": "81fQeazltFlu", "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219" }, "outputs": [ { "data": { "text/html": [ "<iframe\n", " src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n", " frameborder=\"0\"\n", " width=\"100%\"\n", " height=\"360px\"\n", "></iframe>\n" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(\n", " HTML(\n", " \"\"\"<iframe\n", " src=\"https://huggingface.co/datasets/openai/gsm8k/embed/viewer/main/train\"\n", " frameborder=\"0\"\n", " width=\"100%\"\n", " height=\"360px\"\n", "></iframe>\n", "\"\"\"\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "id": "bWUSv7NMtFlu" }, "outputs": [], "source": [ "ds = load_dataset(\"openai/gsm8k\", \"main\")\n", "\n", "\n", "def process_dataset(sample):\n", " # TODO: πŸ• Converta a amostra em um formato de bate-papo\n", "\n", " # 1. Crie um formato de mensagem com o papel e o conteúdo\n", "\n", " # 2. Aplique o modelo de bate-papo nas amostras usando o método do tokenizador\n", "\n", " return sample\n", "\n", "\n", "ds = ds.map(process_dataset)" ] }, { "cell_type": "markdown", "metadata": { "id": "qlXCuRKotFlu" }, "source": [ "## Conclusão\n", "\n", "Este caderno demonstrou como aplicar modelos de bate-papo a diferentes modelos, `SmolLM2`. Ao estruturar interações com modelos de bate-papo, podemos garantir que os modelos de IA forneçam respostas consistentes e contextualmente relevantes.\n", "\n", "No exercício, você tentou converter um conjunto de dados para o formato chatml. Felizmente, o TRL fará isso por você, mas é útil entender o que está acontecendo nas entre-linhas de tudo." ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "py310", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.15" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0055b6b628934affaf88bc58a1572bb6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", 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