Usando vLLM com o modelo Qwen

intro-vllm.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# <h1 align=\"center\"><font color=\"red\">Introdução ao uso do vLLM</font></h1>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<font color=\"pink\">Senior Data Scientist.: Dr. Eddy Giusepe Chirinos Isidro</font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Link de estudo:\n",
+    "\n",
+    "* [vllm-project](https://github.com/vllm-project/vllm?tab=readme-ov-file)\n",
+    "\n",
+    "* [vllm: quickstart](https://docs.vllm.ai/en/latest/getting_started/quickstart.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<font color=\"orange\">`vLLM` é uma biblioteca rápida e fácil de usar para inferência e serviço de `LLM`.</font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://pypi-camo.freetls.fastly.net/78b171d927e29d3adc6067494d26adffc78c8532/68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f766c6c6d2d70726f6a6563742f766c6c6d2f6d61696e2f646f63732f736f757263652f6173736574732f6c6f676f732f766c6c6d2d6c6f676f2d746578742d6c696768742e706e67)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<font color=\"orange\">Você deve executar o seguinte comando no terminal (deixa ele rodando como você faz no `ollama`):</font>\n",
+    "\n",
+    "```bash\n",
+    "vllm serve Qwen/Qwen2.5-1.5B-Instruct \n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openai import OpenAI\n",
+    "\n",
+    "# Modifique o OpenAI's API key e API base para usar o servidor API do vLLM:\n",
+    "openai_api_key = \"EMPTY\"\n",
+    "openai_api_base = \"http://localhost:8000/v1\"\n",
+    "\n",
+    "client = OpenAI(\n",
+    "    api_key=openai_api_key,\n",
+    "    base_url=openai_api_base,\n",
+    ")\n",
+    "completion = client.completions.create(model=\"Qwen/Qwen2.5-1.5B-Instruct\",\n",
+    "                                      prompt=\"San Francisco é uma\")\n",
+    "\n",
+    "print(\"Completion result:\", completion.choices[0].text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from openai import OpenAI\n",
+    "\n",
+    "# Modifique o OpenAI's API key e API base para usar o servidor API do vLLM:\n",
+    "openai_api_key = \"EMPTY\"\n",
+    "openai_api_base = \"http://localhost:8000/v1\"\n",
+    "\n",
+    "client = OpenAI(\n",
+    "    api_key=openai_api_key,\n",
+    "    base_url=openai_api_base,\n",
+    ")\n",
+    "\n",
+    "chat_response = client.chat.completions.create(model=\"Qwen/Qwen2.5-1.5B-Instruct\",\n",
+    "                                               messages=[{\"role\": \"system\", \"content\": \"Você é um assistente útil.\"},\n",
+    "                                                         {\"role\": \"user\", \"content\": \"Conta para mim uma piada.\"},\n",
+    "                                                        ]\n",
+    "                                              )\n",
+    "\n",
+    "print(\"Chat response:\", chat_response.choices[0].message.content)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}