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Una modalidad que se ha vuelto muy popular es la modalidad de chat donde las instrucciones estan estructuradas en una secuencia de texto del tipo:\n", "\n", "- \\\n", "- \\\n", "- \\\n", "- \\\n", "- ..\n", "\n", "En este ejemplo exploraremos un modelo de generación de texto que ha sido entrenado con conjunto de datos en este formato dandole la habilidad de seguir conversaciones." ], "metadata": { "id": "g4lLoquNL-pk" } }, { "cell_type": "markdown", "source": [ "## Introdución\n", "\n", "Los grandes modelos de lenguaje son capaces de resolver problemas de clasificación al utilizar determinadas estructuras del idioma. Algunos modelos de lenguaje están especificamente entrenados para seguir instrucciones, los cuales los hace muy útiles a la hora de implementar zero-shot or few-shot learning.\n", "\n", "En este ejemplo, veremos como utilizar un modelo de aprendizaje automático entrenado de esta forma para resolver problemas de clasificación." ], "metadata": { "id": "7rNqhA9ZMbfu" } }, { "cell_type": "markdown", "source": [ "### Para ejecutar este notebook\n", "\n", "Para ejecutar este notebook, instale las siguientes librerias:" ], "metadata": { "id": "iMn_2ecENFcP" } }, { "cell_type": "code", "source": [ "!wget https://raw.githubusercontent.com/santiagxf/M72109/master/NLP/Datasets/mascorpus/tweets_marketing.csv \\\n", " --quiet --no-clobber --directory-prefix ./Datasets/mascorpus/\n", "\n", "!pip -q install transformers[torch] accelerate datasets evaluate flash-attention" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Fb1pmFc6JuCO", "outputId": "6c199bf1-47f5-4ddf-8bc8-b9dad62f9834" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/302.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/302.6 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.6/302.6 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ] }, { "cell_type": "markdown", "source": [ "### Verificando el hardware disponible" ], "metadata": { "id": "-u7dKENbNQDn" } }, { "cell_type": "code", "source": [ "import torch\n", "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n", "\n", "print(\"Este notebook se está ejecutando en\", device)" ], "metadata": { "id": "TRTV_uN7NPkV", "outputId": "86537d35-a732-481e-f2d2-425dc2081ed6", "colab": { "base_uri": "https://localhost:8080/" } }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Este notebook se está ejecutando en cuda\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Trabajando con un SLM entrenado para chat\n", "\n", "En este ejemplo, utilizaremos el modelo `dolly` en su version de 2.8 millones de parámetros. Para poder utilizar este modelo en Google Colab, necesitamos realizar algunas optimizaciones, entre las cuales, bajar la precisión numérica a 16 bits." ], "metadata": { "id": "S26jMIKGNY-0" } }, { "cell_type": "code", "source": [ "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " \"microsoft/Phi-3-mini-128k-instruct\",\n", " device_map=\"cuda\",\n", " torch_dtype=torch.bfloat16,\n", " trust_remote_code=True,\n", ")\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/Phi-3-mini-128k-instruct\")\n", "\n", "pipe = pipeline(\n", " \"text-generation\",\n", " model=model,\n", " tokenizer=tokenizer,\n", ")" ], "metadata": { "id": "ur8azOoSJzId", "colab": { "base_uri": "https://localhost:8080/", "height": 281, "referenced_widgets": [ "dbfd7dff5eae4e07bbd77f572c764a9d", "8696d2591f7d4431bdb40952a33531e6", "846095b548e14c9ab970c4cb52f20f53", "e6c365381f364eb993b9889fb73ded4b", "95e2a9b6fdac494c83b832e328c7bb5f", "975a15d24e6d464f9c23b274de705f64", "1d90e2c63c324903a655e7193dc5d5ec", "a63d55de301d4ae1b106700b0b9a1b68", "c9a10bcb6b7f4cc6bbc7dea1dd880866", "f6d1a5940075485baff00151bb990e60", "7eb4b07a725c4891b23c794cd3d735c4", "58fa0b25ca0f4e4d8c996d66ddc277e2", "7130296a580549cd9522d8056b017d48", "7fafef687e56483ba4ade28671511cd7", "c7b3827abfb74823af812cbe85536afa", "ddb0d7786f0e41b886b29494ffdc7a4c", "607e82886c5841748864578ee70cec8b", "e506d018804f43f6a353ef8e684d3224", "f4f50b8327b64ac38eb18a76b693952a", "70cd22380ea948a785610f788157d154", "a0cffd3035be48839e1cc979868ada2a", "cd3f4da853a04898a945d6a53c97fac7", "e29db64d66eb45c8848d7317e46dfe1f", "be6bb6372f3640f8877b89566450eec6", "f2e47fd4cc9b48168f2f1ada1b061671", "993e78afe11f4dbe84b4d9e48bea0cfa", "50c9adec8fda4958a73f36b5750d144f", "b14b1d356ebc4a3a9724c7ac3306f51e", "569c552fb9b84d4bb7748ccd37ea33b7", "daa351f76cf94a63abe5367e519adff3", "158dd326c35f4a158964525e53b88181", "4c99f91e49224cbfb27a8035d765cf02", "711b6e0c6ef1412dbe45796ecf31f69c", "6d6ba587f2874f49908d368cf9b7c271", "709405263fd04f64bf2d5129338e47c0", "bc128aa383bf4bb190c431b42edc4a2f", "fbc488f051e8422ea425dcc28b3ac492", "cc1dc95f73bd44119538660165b96c90", "67a7426f96c54c699844af3a036fb03c", "862a0f849a4f4ebc9b83f69ac59addc3", "0ed1244d0d2a4d3ab19af1bde9749a4b", "f75ecf776bbd41b69bd8cffc4e0b8227", "9762276b862d4b0fa70b1a437dd231bd", "3640acc1d8cd47cb80d133382f80640d", "fa263bb72e584aa080a2a6160838eb94", "e6276ff04ed745b484abc7d4b339c572", "b9588962085e496db276c1c198a0b549", "72b09eaf26ec4ed78d7d453533a14ce3", "86a745f385f440b782360cb471059375", "19a0fa1f145944f4a59d539c62671e37", "58cf93312b494e0b9529364c9eff30db", "62bf336eaa39482590c79369f3e6314a", "99c44219cd2f4c638ce7eab8c02c092e", "6d6246323d0d4e5489c8ea3dc95dc18f", "e69573af199b4cc2bf2849112afe07cb", "1766eaa528a141d89797f34bbdd1ac1d", "d959522c38d24d069cd9a29b099b1df2", "cf89acad9b224395b60f912f1385bb52", "1ba34b6a0f7543be99e1242f9ac24616", "70c8cd1b011242518a8ddab253f0de35", "45e0eb418c7747c88be737a0a27d3a1a", "c9a1abf6a5864c8da9ea3efe89744365", "06f9e30de74c435380a2fd885f3ed467", "60b7d40bd860441d820abe41c79383b8", "edab651e12e44a8eafd103c4b9a1b3d7", "9ffdce755da343718b6895544fa842b0" ] }, "outputId": "bd6ea367-d96f-4dbe-e809-cbee59466962" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:transformers_modules.microsoft.Phi-3-mini-128k-instruct.8a362e755d2faf8cec2bf98850ce2216023d178a.modeling_phi3:`flash-attention` package not found, consider installing for better performance: No module named 'flash_attn'.\n", "WARNING:transformers_modules.microsoft.Phi-3-mini-128k-instruct.8a362e755d2faf8cec2bf98850ce2216023d178a.modeling_phi3:Current `flash-attenton` does not support `window_size`. Either upgrade or use `attn_implementation='eager'`.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00