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Usando o modelo Mistral-7B para responder instruções
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Usando_LLM_Mistral_7B_para_gerar_texto.ipynb
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},
"cells": [
{
"cell_type": "markdown",
"source": [
"## Verificando a máquina\n",
"\n",
"O exemplo de uso do Mistral com modelo quantizado com GPTQ fala de instalar dependências para PyTorch 2.1 + CUDA 12.x.\n",
"\n",
"Veja: [Install the necessary packages](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ#install-the-necessary-packages)."
],
"metadata": {
"id": "ISnXXfbePUwF"
}
},
{
"cell_type": "markdown",
"source": [
"Verificando a versão do CUDA:\n",
"\n",
"`CUDA Version: 12.2`"
],
"metadata": {
"id": "WVTJJEji_LIA"
}
},
{
"cell_type": "code",
"source": [
"!nvidia-smi"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_vt3awwzNoMC",
"outputId": "4e1c5ff2-cde8-4cdb-d052-1540dc619950"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sat Apr 13 01:12:56 2024 \n",
"+---------------------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 535.104.05 Driver Version: 535.104.05 CUDA Version: 12.2 |\n",
"|-----------------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|=========================================+======================+======================|\n",
"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 40C P8 10W / 70W | 0MiB / 15360MiB | 0% Default |\n",
"| | | N/A |\n",
"+-----------------------------------------+----------------------+----------------------+\n",
" \n",
"+---------------------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=======================================================================================|\n",
"| No running processes found |\n",
"+---------------------------------------------------------------------------------------+\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Verificando se a versão do PytTorch é 2.x."
],
"metadata": {
"id": "9I9MBfo5_90A"
}
},
{
"cell_type": "code",
"source": [
"import torch\n",
"torch.__version__"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "S7xZIrS668-i",
"outputId": "4e381d7b-fb3b-4fb3-ccc4-807cad416729"
},
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'2.2.1+cu121'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 2
}
]
},
{
"cell_type": "markdown",
"source": [
"## Instalações\n",
"\n",
"Veja: [Install the necessary packages](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ#install-the-necessary-packages)."
],
"metadata": {
"id": "0K-YZvrR__6w"
}
},
{
"cell_type": "code",
"source": [
"!pip3 install --upgrade transformers optimum\n",
"!pip3 install --upgrade auto-gptq"
],
"metadata": {
"id": "itAmpTUTN6HZ",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f5385af7-8ced-43de-9eb6-93043d2282cb"
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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" Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
"Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.11->optimum)\n",
" Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
"Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.11->optimum)\n",
" Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
"Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.11->optimum)\n",
" Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
"Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.11->optimum)\n",
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" Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
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"Installing collected packages: xxhash, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, humanfriendly, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, coloredlogs, nvidia-cusolver-cu12, transformers, datasets, optimum\n",
" Attempting uninstall: transformers\n",
" Found existing installation: transformers 4.38.2\n",
" Uninstalling transformers-4.38.2:\n",
" Successfully uninstalled transformers-4.38.2\n",
"Successfully installed coloredlogs-15.0.1 datasets-2.18.0 dill-0.3.8 humanfriendly-10.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 optimum-1.18.1 transformers-4.39.3 xxhash-3.4.1\n",
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"Installing collected packages: rouge, gekko, accelerate, peft, auto-gptq\n",
"Successfully installed accelerate-0.29.2 auto-gptq-0.7.1 gekko-1.1.1 peft-0.10.0 rouge-1.0.1\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Carrega o modelo"
],
"metadata": {
"id": "1R3mALfNcjHl"
}
},
{
"cell_type": "code",
"source": [
"import torch\n",
"\n",
"# Dispositivo disponível: CPU ou GPU\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
],
"metadata": {
"id": "_rlaTBhf9rbO"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "NR_8SBCVMxgN",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 400,
"referenced_widgets": [
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},
"outputId": "a8c763b8-6894-46ce-983f-a03aedcd1786"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
"The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
"To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
"You will be able to reuse this secret in all of your notebooks.\n",
"Please note that authentication is recommended but still optional to access public models or datasets.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/1.08k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "26d2f205693a40f3a6865c244c5a5a42"
}
},
"metadata": {}
},
{
"output_type": "display_data",
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"model.safetensors: 0%| | 0.00/4.57G [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "556adfad8e884435a8a6671df40704d8"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py:4225: FutureWarning: `_is_quantized_training_enabled` is going to be deprecated in transformers 4.39.0. Please use `model.hf_quantizer.is_trainable` instead\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
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],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "326e7880544f4d2cbbcaf6c59030a7ef"
}
},
"metadata": {}
},
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"version_major": 2,
"version_minor": 0,
"model_id": "479360ed9a394173a0f6a4a1a59c3abd"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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}
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}
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"version_minor": 0,
"model_id": "cc70280fd6f84e2799b1d5504cacca7a"
}
},
"metadata": {}
}
],
"source": [
"from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
"\n",
"model_name = \"TheBloke/Mistral-7B-Instruct-v0.2-GPTQ\"\n",
"\n",
"# Usando revision=\"gptq-4bit-32g-actorder_True\" para melhor acurácia\n",
"model = AutoModelForCausalLM.from_pretrained(model_name,\n",
" device_map=\"auto\",\n",
" trust_remote_code=False,\n",
" revision=\"gptq-4bit-32g-actorder_True\")\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)\n",
"\n",
"# Como vamos apenas gerar texto e não treinar, vamos configurar\n",
"# pad_token = eos_token para evitar warnings\n",
"if tokenizer.pad_token is None:\n",
" tokenizer.pad_token_id=tokenizer.eos_token_id"
]
},
{
"cell_type": "markdown",
"source": [
"## Define a pipeline\n",
"\n",
"Usaremos o modelo por meio da pipeline da biblioteca transformers."
],
"metadata": {
"id": "IrzBZpD98ALD"
}
},
{
"cell_type": "code",
"source": [
"# Este é o template do prompt usado no treinamento do Mistral, portanto o\n",
"# recomendado é usá-lo para tentar obter melhores resultados\n",
"prompt_template='''<s>[INST] {prompt} [/INST]\n",
"'''\n",
"\n",
"pipe = pipeline(\n",
" \"text-generation\",\n",
" model=model,\n",
" tokenizer=tokenizer,\n",
" max_new_tokens=512,\n",
" do_sample=True,\n",
" temperature=0.7,\n",
" top_p=0.95,\n",
" top_k=40,\n",
" repetition_penalty=1.1\n",
")\n",
"\n",
"def answer(prompt):\n",
" prompt = prompt_template.format(prompt=prompt)\n",
" output_text = pipe(prompt)[0]['generated_text']\n",
" output_text = output_text[len(prompt):].strip()\n",
" return output_text"
],
"metadata": {
"id": "3OMmeu2vBv8i"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Usando o modelo"
],
"metadata": {
"id": "OGGmUH-8E68_"
}
},
{
"cell_type": "code",
"source": [
"prompt = \"Considerando o contexto de reconhecimento de entidades, escreva uma breve definição da entidade: pessoa\"\n",
"\n",
"answer(prompt)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
},
"outputId": "91fee515-6379-4e81-b384-7403fdeabc65",
"id": "OupS0wlgHgon"
},
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'A entidade \"pessoa\" representa um ser vivo possuindo consciência e individualidade. Elas podem ser classificadas como físicas (individuos humanos) ou lógicas (personas jurídicas, tais quais empresas ou governos). Pessoas físicas são geralmente definidas pelas características biológicas, enquanto pessoas lógicas são definidas pelas suas funções, atribuições ou propriedades. Em termos de processamento de linguagem natural, identificar a entidade \"pessoa\" envolve a localização e extração de informações relevantes sobre indivíduos ou grupos humanos mencionados no texto.'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"source": [
"prompt = \"Considering the context of entity recognition, write a short definition of the entity: person\"\n",
"\n",
"answer(prompt)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
},
"id": "ZeCmS_PpBgg9",
"outputId": "c81cc339-a059-4d2f-c3b8-7cb0762f629e"
},
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'In the context of entity recognition, a person is an identified individual with distinct name and other relevant attributes such as title, organization, or role. Entities of type \"person\" can include famous figures, political leaders, artists, athletes, or common people mentioned in text. Entity recognition systems aim to extract, identify, and classify these entities accurately within unstructured data, enabling improved information retrieval, analysis, and overall better understanding of the textual content.'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 6
}
]
}
]
}
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