Chinese | English
Welcome to the Yi Cookbook! This is your one-stop resource for everything related to Yi models, featuring tutorials, demos, and comprehensive documentation. Whether you're just starting out or looking to explore advanced use cases, you'll find valuable information here. ## News - **🔥2024-08-09**: Yi Cookbook 1.0 is released, featuring tutorials and examples in both Chinese and English. ## OpenSource | Category | Description | Notebook, Markdown | |:----------------------------|:-----------------------------|:--------------------------------------------------------------------------------------------------------------------------| | inference, swift | Perform efficient inference with the Yi model using Swift. | [Inference_using_swift.ipynb](./en/opensource/Inference/Inference_using_swift.ipynb) | | inference, transformers | Utilize Transformers to rapidly run inference with the Yi model. | [Inference_using_transformers.ipynb](./en/opensource/Inference/Inference_using_transformers.ipynb) | | inference, Imdeploy | Experience fast inference with the Yi model through Imdeploy. | [Inference_using_lmdeploy.ipynb](./en/opensource/Inference/Inference_using_lmdeploy.ipynb) | | inference, vllm | Deploy vllm for quick and efficient inference with the Yi model. | [vLLM_Inference_tutorial.ipynb](./en/opensource/Inference/vLLM_Inference_tutorial.ipynb) | | inference, skypilot | Run Yi with SkyPilot on Your Cloud. | [running_yi_with_skypilot.md](https://github.com/Haijian06/skypilot/tree/master/llm/yi) | | quantization, swift | Learn how to quantize your Yi model using the power of Swift. | [swift-yi-quantization.md](./en/opensource/quantization/swift-yi-quantization.md) | | quantization, autoawq | Explore quantization techniques for the Yi model with autoawq. | [autoawq-yi-quantization.md](./en/opensource/quantization/autoawq-yi-quantization.md) | | quantization, autogptq | Dive into quantizing the Yi model using the autogptq library. | [autogptq-yi-quantization.md](./en/opensource/quantization/autogptq-yi-quantization.md) | | fine-tuning, swift | Discover how to fine-tune your own Yi model with Swift for personalized results. | [finetune-yi-with-swift.md](./en/opensource/fine_tune/finetune-yi-with-swift.md) | | fine-tuning, LlaMA-Factory | Unlock the potential of fine-tuning your Yi model with the versatile LlaMA-Factory.| [finetune-yi-with-llamafactory.md](./en/opensource/fine_tune/finetune-yi-with-llamafactory.md) | | Local Run, ollama | Set up and run the Yi model locally using the convenient ollama framework. | [local-ollama.md](./en/opensource/local/local-ollama.md) | | Local Run, MLX-LM | Explore local execution of the Yi model within the MLX-LM environment. | [local-mlx.md](./en/opensource/local/local-mlx.md) | | Local Run, LM Studio | Get hands-on experience running the Yi model locally using the intuitive LM Studio interface. | [local-lm-studio.md](./en/opensource/local/local-lm-studio.md) | | Local Run, llama.cpp | Leverage the llama.cpp library to run the Yi model on your local machine. | [local-llama.cpp.md](./en/opensource/local/local-llama.cpp.md) | | RAG, LlamaIndex | Delve into the world of RAG by building a system based on the Yi model and the powerful LlamaIndex library.| [yi_rag_llamaindex.ipynb](./en/opensource/rag/yi_rag_llamaindex.ipynb) | | RAG, LangChain | Master the art of building robust RAG systems using LangChain as the backbone for your Yi model integration. | [yi_rag_langchain.ipynb](./en/opensource/rag/yi_rag_langchain.ipynb) | | function calling | Embark on a journey to implement function calling from the ground up. | [function_calling.ipynb](./en/opensource/function_calling/function_calling.ipynb) | | function calling, LlamaIndex | Discover the seamless integration of function calling with the Yi model and LlamaIndex. | [function_calling_llamaindex.ipynb](./en/opensource/function_calling/function_calling_llamaindex.ipynb) | ## API | Category | Description | Notebook, Markdown | |:---------------------------|:---------------------------------|:-------------------------------------------------------------------------------------------------------| | RAG, LlamaIndex | Integrate the Yi(api) model with LlamaIndex to build a powerful RAG system.| [yi_rag_llamaindex.ipynb](./en/api/rag/yi_rag_llamaindex.ipynb) | | RAG, LangChain | Explore building feature-rich RAG applications by harnessing the capabilities of LangChain with the Yi API. | [yi_rag_langchain.ipynb](./en/api/rag/yi_rag_langchain.ipynb) | | function calling, LlamaIndex | Learn how to effortlessly implement function calling using the Yi model, powered by the intuitive LlamaIndex framework. | [function_calling_llamaindex.ipynb](./en/api/function_calling/function_calling_llamaindex.ipynb) | ## Ecosystem | Category | Description | Notebook, Markdown | |:------------|:------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | fine-tuning | Unlock enhanced mathematical and logical abilities by fine-tuning the Yi-1.5-6B-Chat open-source model. | [Enhancing the mathematical and logical abilities of Yi-1.5-6B-Chat.md](./en/ecosystem/Enhancing_the_Mathematical_and_Logical_Reasoning_Abilities_of_Yi-1.5-6B-Chat.md) | | RAG | Construct an intelligent question answering system by combining the strengths of LlamaIndex and the Yi-large model. | [Building an Intelligent Question Answering System Based on LlamaIndex and Yi-large.md](./en/ecosystem/Building_an_Intelligent_Q&A_System_Based_on_LlamaIndex_and_Yi-large.md) | | demo | Witness the capabilities of large models beyond traditional tasks with a demonstration of Yi playing Street Fighter III. | [Playing Street Fighter III with Yi large model.md](./en/ecosystem/Mastering_Street_Fighter_III_with_the_Yi_Language_Model.md) | | demo | Explore the creative potential of Yi by diving into a mind map generator application. | [Building a Mind Map Generator Based on yi-large.md](./en/ecosystem/Building_a_Mind_Map_Generator_Powered_by_Yi-Large.md) | | fine-tuning | Maximize your fine-tuning efforts with a comprehensive guide to best practices for the yi-vl model. | [Best Practices for yi-vl Fine-tuning.md](./en/ecosystem/yi-vl-best-practice.md) | ## Community Contributions We warmly welcome contributions from the community! Here are some ways you can get involved: - **Report Issues**: Discover a bug or have a feature suggestion? Please submit it to [GitHub Issues](https://github.com/01-ai/Yi/issues). - **Contribute Demo**: Pull requests are highly appreciated! Please follow our [Contribution Guidelines](./CONTRIBUTING.md). ================================================ FILE: Cookbook/README_cn.md ================================================ # 👋 Yi CookbookEnglish | 中文
欢迎来到 Yi Cookbook！这是一个关于 Yi 模型的综合资源库，包含教程、演示和详细文档。无论您是初学者还是想要研究模型的高阶使用，这里都能找到有价值的信息。 ## 最新动态 - **🔥2024-08-09**: 发布 Yi Cookbook 英文版本 - **🔥2024-07-29**: 发布 Yi Cookbook 1.0 版本，包含中文教程和示例。 ## OpenSource | Category | Description | Notebook, Markdown | |:----------------------------|:---------------------------------------------|:--------------------------------------------------------------------------------------------------------| | inference, swift | 利用 Swift 快速进行 Yi 模型推理。 | [Inference_using_swift.ipynb](./cn/opensource/Inference/Inference_using_swift.ipynb) | | inference, transformers | 使用 Transformers 高效推理 Yi 模型。 | [Inference_using_transformers.ipynb](./cn/opensource/Inference/Inference_using_transformers.ipynb) | | inference, Imdeploy | 通过 Imdeploy 快速上手 Yi 模型推理。 | [Inference_using_lmdeploy.ipynb](./cn/opensource/Inference/Inference_using_lmdeploy.ipynb) | | inference, vllm | 使用 vllm 体验 Yi 模型的快速推理。 | [vLLM_Inference_tutorial.ipynb](./cn/opensource/Inference/vLLM_Inference_tutorial.ipynb) | | skypilot | 通过 SkyPilot 在你的云端高效推理 Yi 模型。| [Running_yi_with_skyPilot.md](https://github.com/Haijian06/skypilot/tree/master/llm/yi) | | quantization, swift | 使用 Swift轻松量化您的专属 Yi 模型。 | [swift-yi-quantization.md](./cn/opensource/quantization/swift-yi-quantization.md) | | quantization, autoawq | 利用 autoawq 量化您的 Yi 模型。 | [autoawq-yi-quantization.md](./cn/opensource/quantization/autoawq-yi-quantization.md) | | quantization, autogptq | 使用 autogptq 对 Yi 模型进行量化。 | [autogptq-yi-quantization.md](./cn/opensource/quantization/autogptq-yi-quantization.md) | | fine-tuning, swift | 使用 Swift 微调，打造您的个性化 Yi 模型。 | [finetune-yi-with-swift.md](./cn/opensource/fine_tune/finetune-yi-with-swift.md) | | fine-tuning, LlaMA-Factory | 使用 LlaMA-Factory 灵活微调您的 Yi 模型。 | [finetune-yi-with-llamafactory.md](./cn/opensource/fine_tune/finetune-yi-with-llamafactory.md) | | Local Run, ollama | 使用 ollama 在本地环境中运行 Yi 模型。 | [local-ollama.md](./cn/opensource/local/local-ollama.md) | | Local Run, MLX-LM | 在 MLX-LM 环境下本地运行 Yi 模型。 | [local-mlx.md](./cn/opensource/local/local-mlx.md) | | Local Run, LM Studio | 使用 LM Studio 轻松本地运行 Yi 模型。 | [local-lm-studio.md](./cn/opensource/local/local-lm-studio.md) | | Local Run, llama.cpp | 使用 llama.cpp 在本地运行 Yi 模型。 | [local-llama.cpp.md](./cn/opensource/local/local-llama.cpp.md) | | RAG, LlamaIndex | 基于 Yi 模型和 LlamaIndex 构建强大的 RAG 系统。| [yi_rag_llamaindex.ipynb](./cn/opensource/rag/yi_rag_llamaindex.ipynb) | | RAG, LangChain | 使用 LangChain 构建灵活的 RAG 系统。 | [yi_rag_langchain.ipynb](./cn/opensource/rag/yi_rag_langchain.ipynb) | | function calling | 从零开始，实现函数调用。 | [function_calling.ipynb](./cn/opensource/function_calling/function_calling.ipynb) | | function calling, LlamaIndex | 基于 LlamaIndex，轻松实现函数调用。 | [function_calling_llamaindex.ipynb](./cn/opensource/function_calling/function_calling_llamaindex.ipynb) | ## API | Category | Description | Notebook, Markdown | |:---------------------------|:---------------------------------|:-------------------------------------------------------------------------------------------------| | RAG, LlamaIndex | 使用 Yi(api) 模型与 LlamaIndex 构建 RAG 应用。 | [yi_rag_llamaindex.ipynb](./cn/api/rag/yi_rag_llamaindex.ipynb) | | RAG, angChain | 利用 LangChain，构建基于 Yi API 的 RAG 系统。| [yi_rag_langchain.ipynb](./cn/api/rag/yi_rag_langchain.ipynb) | | function calling, LlamaIndex | 使用 Yi 模型，基于 LlamaIndex 实现函数调用。 | [function_calling_llamaindex.ipynb](./cn/api/function_calling/function_calling_llamaindex.ipynb) | ## Ecosystem | Category | Description | Notebook, Markdown | |:------------|:-----------------------------------------------|:-------------------------------------------------------------------------------------------------| | fine-tuning | 强化 Yi-1.5-6B-Chat 开源模型的数学与逻辑能力。 | [强化Yi-1.5-6B-Chat的数学和逻辑能力.md](./cn/ecosystem/强化Yi-1.5-6B-Chat的数学和逻辑能力.md) | | RAG | 基于 LlamaIndex 和 Yi-large 构建智能问答系统。 | [基于LlamaIndex和Yi-large构建智能问答系统.md](./cn/ecosystem/基于LlamaIndex和Yi-large构建智能问答系统.md) | | demo | 大模型玩游戏？探索 Yi 玩转街霸三的奥秘！ | [使用Yi大模型玩转街霸三.md](./cn/ecosystem/使用Yi大模型玩转街霸三.md) | | demo | 基于 yi-large，打造高效的思维导图生成器。 | [基于yi-large构建思维导图生成器.md](./cn/ecosystem/基于yi-large构建思维导图生成器.md) | | fine-tuning | 掌握 yi-vl 微调的最佳实践，事半功倍。 | [yi-vl最佳实践.md](./cn/ecosystem/yi-vl最佳实践.md) | ## 社区贡献 我们热烈欢迎社区贡献！以下是参与方式： - **问题报告**：发现 bug 或有功能建议？请在 [GitHub Issues](https://github.com/01-ai/Yi/issues) 提交。 - **展示你的作品!**：如果你有基于Yi模型做的有趣的、实用的应用或者教学，我们非常欢迎你提交PR到我们的仓库！请遵循我们的 [贡献指南](./CONTRIBUTING_cn.md)。 ================================================ FILE: Cookbook/cn/api/function_calling/function_calling_llamaindex.ipynb ================================================ {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPi5Ho7I9ICMU0Jnn+/aVjA"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## Function Calling\n","\n","大模型使用Function Calling的主要用途是扩展模型的功能，使其能够在生成文本、做出决策或执行任务时，集成更广泛的信息和功能。通过定义和调用外部函数，大模型能够处理更复杂的任务，如进行数学运算、下订单或查询数据库等，从而提高模型的实用性和灵活性。\n"],"metadata":{"id":"tY2fLItjnn6U"}},{"cell_type":"markdown","source":["### 🌟使用LlamaIndex\n","\n","LlamaIndex 开发了简单易用的 Function Calling 使用方法，接下来我们使用yi-large进行一个示例\n","\n","首先安装依赖环境，跟着我一步一步来！\n"],"metadata":{"id":"EiQrQO8In5W2"}},{"cell_type":"code","source":["!pip install llama-index\n","!pip install llama-index-llms-huggingface\n","!pip install llama-index-llms-yi"],"metadata":{"id":"9-f7jfstpCEe"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["确保你安装完成后我们就开始吧\n","\n","加载依赖"],"metadata":{"id":"3tlgHR5spMhG"}},{"cell_type":"code","execution_count":3,"metadata":{"id":"w9F41myGnfCS","executionInfo":{"status":"ok","timestamp":1721973909886,"user_tz":-480,"elapsed":8874,"user":{"displayName":"haijian wang","userId":"16545674694152900117"}}},"outputs":[],"source":["from typing import Any\n","\n","\n","from llama_index.core.llms import (\n"," CustomLLM,\n"," CompletionResponse,\n"," CompletionResponseGen,\n"," LLMMetadata,\n",")\n","from llama_index.core.llms.callbacks import llm_completion_callback\n","from llama_index.llms.yi import Yi\n","\n","from llama_index.core.tools import FunctionTool\n","from llama_index.core.agent import ReActAgent"]},{"cell_type":"markdown","source":["配置模型，需要前往零一万物[开放平台](https://platform.lingyiwanwu.com/apikeys)申请api_key"],"metadata":{"id":"xBu4fAuapTMS"}},{"cell_type":"code","source":["llm = Yi(model=\"yi-large\", api_key=\"your_api_key\")"],"metadata":{"id":"Lq4QFDl-pLn_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["定义工具，这里我们定义 `加` `减` `乘` 的三种函数"],"metadata":{"id":"gCnRQ2vNpViR"}},{"cell_type":"code","source":["# 定义工具\n","def multiply(a: int, b: int) -> int:\n"," \"\"\"Multiple two integers and returns the result integer\"\"\"\n"," return a * b\n","\n","def plus(a: int, b: int) -> int:\n"," \"\"\"plus two integers and returns the result integer\"\"\"\n"," return a + b\n","def minus(a: int, b: int) -> int:\n"," \"\"\"Subtracts two integers and returns the result integer.\"\"\"\n"," return a - b\n","multiply_tool = FunctionTool.from_defaults(fn=multiply)\n","plus_tool = FunctionTool.from_defaults(fn=plus)\n","minus_tool = FunctionTool.from_defaults(fn=minus)"],"metadata":{"id":"g5-zAq22pXu6"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["创建Agent"],"metadata":{"id":"cvUFnsAkph-O"}},{"cell_type":"code","source":["agent = ReActAgent.from_tools([multiply_tool,plus_tool,minus_tool], llm=llm, verbose=True)"],"metadata":{"id":"Hurvg2J1pjQB"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["确保运行正确后我们就开始对话"],"metadata":{"id":"kEkZMzaXplsK"}},{"cell_type":"code","source":["while True:\n"," user_input = input(\"user>>\") # \"(1+2)*10\"\n"," agent.chat(user_input)"],"metadata":{"id":"Ki3NKbq_pqTz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["运行结果展示"],"metadata":{"id":"HjcL_f7npr-K"}},{"cell_type":"markdown","source":["``````\n","user>>(1+2)*10\n","Thought: The user's question is in English. I need to use a combination of the 'plus' and 'multiply' tools to solve the mathematical expression (1+2)*10.\n","Action: plus\n","Action Input: {'a': 1, 'b': 2}\n","Observation: 3\n","Thought: I have the result of the 'plus' operation, which is 3. Now I need to multiply this result by 10 to complete the expression (1+2)*10.\n","Action: multiply\n","Action Input: {'a': 3, 'b': 10}\n","Observation: 30\n","Thought: I have the result of the 'multiply' operation, which is 30. This is the final result of the expression (1+2)*10.\n","Thought: I can answer without using any more tools. I'll use the user's language to answer.\n","Answer: The result of the expression (1+2)*10 is 30.\n","``````"],"metadata":{"id":"oBUz2VtkpuUw"}}]} ================================================ FILE: Cookbook/cn/api/rag/yi_rag_langchain.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 使用Yi的API构建RAG(LangChain)\n", "\n", "在本教程中，我们将学习如何使用LangChain和Yi的API构建一个RAG（Retrieval-Augmented Generation）系统。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 安装必要的库\n", "\n", "首先，我们需要安装LangChain库。" ] }, { "cell_type": "code", "metadata": {}, "source": [ "!pip install langchain" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 配置环境变量\n", "\n", "接下来，我们需要配置LangSmith和Yi的API密钥。请确保您已经在[LangSmith](https://smith.langchain.com/)和零一万物[开放平台](https://platform.lingyiwanwu.com/apikeys)注册并获取了API密钥。" ] }, { "cell_type": "code", "metadata": {}, "source": [ "import getpass\n", "import os\n", "\n", "os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n", "os.environ[\"LANGCHAIN_API_KEY\"] = \"your_langsmith_api_key\"\n", "os.environ[\"YI_API_KEY\"] = \"your_yi_api_key\"" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 安装LangChain-OpenAI库\n", "\n", "我们还需要安装`langchain-openai`库。" ] }, { "cell_type": "code", "metadata": {}, "source": [ "!pip install -qU langchain-openai" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 配置LLM\n", "\n", "现在我们来配置LLM，使用Yi的大型语言模型。" ] }, { "cell_type": "code", "metadata": {}, "source": [ "from langchain_openai import ChatOpenAI\n", "\n", "llm = ChatOpenAI(\n", " base_url=\"https://api.lingyiwanwu.com/v1\",\n", " api_key=os.environ[\"YI_API_KEY\"],\n", " model=\"yi-large\",\n", ")" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 加载数据\n", "\n", "接下来我们将加载一些示例数据。这里我们使用了LangChain的WebBaseLoader来加载网页数据。" ] }, { "cell_type": "code", "metadata": {}, "source": [ "import bs4\n", "from langchain_community.document_loaders import WebBaseLoader\n", "\n", "loader = WebBaseLoader(\n", " web_paths=(\"https://lilianweng.github.io/posts/2023-06-23-agent/\",),\n", " bs_kwargs=dict(\n", " parse_only=bs4.SoupStrainer(\n", " class_=(\"post-content\", \"post-title\", \"post-header\")\n", " )\n", " ),\n", ")\n", "docs = loader.load()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 构建向量数据库\n", "\n", "我们将使用HuggingFace的嵌入模型来构建向量数据库，并使用Chroma来存储向量。" ] }, { "cell_type": "code", "metadata": {}, "source": [ "from langchain.embeddings import HuggingFaceEmbeddings\n", "from langchain_chroma import Chroma\n", "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", "\n", "# 加载向量模型\n", "embedding = HuggingFaceEmbeddings(model_name=\"BAAI/bge-base-en-v1.5\")\n", "\n", "# 切分文档\n", "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", "splits = text_splitter.split_documents(docs)\n", "\n", "# 构建向量数据库\n", "vectorstore = Chroma.from_documents(documents=splits, embedding=embedding)\n", "retriever = vectorstore.as_retriever()" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 构建RAG链\n", "\n", "最后，我们将构建RAG链，并使用LangChain的hub来获取提示。" ] }, { "cell_type": "code", "metadata": {}, "source": [ "from langchain import hub\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.runnables import RunnablePassthrough\n", "\n", "prompt = hub.pull(\"rlm/rag-prompt\")\n", "\n", "def format_docs(docs):\n", " return \"\\n\\n\".join(doc.page_content for doc in docs)\n", "\n", "rag_chain = (\n", " {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n", " | prompt\n", " | llm\n", " | StrOutputParser()\n", ")\n", "\n", "response = rag_chain.invoke(\"What is Task Decomposition?\")\n", "print(response)" ], "execution_count": null, "outputs": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.8" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: Cookbook/cn/api/rag/yi_rag_llamaindex.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "id": "SweINiRd8dLd" }, "source": [ "# 使用Yi的API构建RAG (使用LlamaIndex)\n", "\n", "`LlamaIndex`是一个灵活的框架，用于构建基于LLM的应用程序，提供数据连接器、数据结构化工具和高级检索/查询接口。\n", "\n", "\n", "在本次示例中我们使用`bge-base-zh-v1.5`进行演示，这个模型主要用于检索中文。你也可以选择`bge-base-en-v1.5`模型来检索英文文档。\n" ], "id": "SweINiRd8dLd" }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6989tVXX8dLh", "outputId": "ccdd7497-7132-4eeb-fe89-6cab8d3d9e9c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: llama-index in /usr/local/lib/python3.10/dist-packages (0.10.58)\n", "Requirement already satisfied: llama-index-agent-openai<0.3.0,>=0.1.4 in /usr/local/lib/python3.10/dist-packages (from llama-index) (0.2.9)\n", "Requirement already satisfied: llama-index-cli<0.2.0,>=0.1.2 in /usr/local/lib/python3.10/dist-packages (from llama-index) (0.1.13)\n", "Requirement already satisfied: llama-index-core==0.10.58 in /usr/local/lib/python3.10/dist-packages (from llama-index) (0.10.58)\n", "Requirement already satisfied: llama-index-embeddings-openai<0.2.0,>=0.1.5 in /usr/local/lib/python3.10/dist-packages (from llama-index) (0.1.11)\n", "Requirement already satisfied: llama-index-indices-managed-llama-cloud>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from llama-index) (0.2.7)\n", "Requirement already satisfied: llama-index-legacy<0.10.0,>=0.9.48 in /usr/local/lib/python3.10/dist-packages (from 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mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.11.0->sentence-transformers>=2.6.1->llama-index-embeddings-huggingface) (1.3.0)\n", "Downloading llama_index_embeddings_huggingface-0.2.2-py3-none-any.whl (7.2 kB)\n", "Downloading sentence_transformers-3.0.1-py3-none-any.whl (227 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.1/227.1 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hDownloading minijinja-2.0.1-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (853 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m853.2/853.2 kB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hInstalling collected packages: minijinja, sentence-transformers, llama-index-embeddings-huggingface\n", "Successfully installed llama-index-embeddings-huggingface-0.2.2 minijinja-2.0.1 sentence-transformers-3.0.1\n" ] } ], "source": [ "# 安装所需的Python包\n", "!pip install llama-index\n", "!pip install llama-index-llms-huggingface\n", "!pip install llama-index-readers-web\n", "!pip install llama-index-llms-yi\n", "!pip install llama-index-embeddings-huggingface\n" ], "id": "6989tVXX8dLh" }, { "cell_type": "markdown", "metadata": { "id": "XlxCBbmI8dLi" }, "source": [ "### 初始化Yi的API\n", "\n", "前往[零一万物开放平台](https://platform.lingyiwanwu.com/)，注册并获取免费的API额度来体验yi-large API。\n", "然后点击[这里](https://platform.lingyiwanwu.com/apikeys)，申请你的API密钥（api_key）。\n", "\n", "导入依赖包，并初始化api平台，你只需要在`your_api_key`处替换成你申请的api_key即可。\n" ], "id": "XlxCBbmI8dLi" }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 496, "referenced_widgets": [ "e2b2f20ae7184ffba003dfeea232dff4", "9bf4ca7e24384defbbe134ede45a5f55", "813a8fb61a38405e81f6ef382800a27d", "409d459c799e44c48148316ba3c2105b", "bacd0a07840d49208d3faf6849d4a695", "cd496a6f1f864d3fbf870048b745ffac", 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"text/plain": [ "modules.json: 0%| | 0.00/349 [00:00> \")\n", " response = query_engine.query(user_input)\n", " print('Yi-large>>', response)" ], "id": "9hxFwnOJ8dLk" }, { "cell_type": "markdown", "metadata": { "id": "SpqIG-_A8dLk" }, "source": [ "哈哈现在你已经完成了使用Yi的API构建RAG的基本步骤。你可以进一步探索和思考如何根据你自己的具体需求调整和优化你的RAG系统。" ], "id": "SpqIG-_A8dLk" } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.8" }, "colab": { "provenance": [], "machine_shape": "hm" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "e2b2f20ae7184ffba003dfeea232dff4": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", 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"min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ac1f084b40f540df95e7e3332a7cca6b": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: Cookbook/cn/ecosystem/yi-vl最佳实践.md ================================================ # Yi-VL 最佳实践 ## 目录 - [环境准备](#环境准备) - [推理](#推理) - [微调](#微调) - [微调后推理](#微调后推理) ## 声明 本文章作者为 swift 成员 Jintao Huang。 原文链接🔗: https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/yi-vl最佳实践.md ## 环境准备 ```shell git clone https://github.com/modelscope/swift.git cd swift pip install -e '.[llm]' ``` ## 推理 推理[yi-vl-6b-chat](https://modelscope.cn/models/01ai/Yi-VL-6B/summary): ```shell # Experimental environment: A10, 3090, V100... # 18GB GPU memory CUDA_VISIBLE_DEVICES=0 swift infer --model_type yi-vl-6b-chat ``` 输出: (支持传入本地路径或URL) ```python """ <<< 描述这张图片 Input a media path or URL <<< http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png 图片显示一只小猫坐在地板上,眼睛睁开,凝视着摄像机。小猫看起来很可爱,有灰色和白色的毛皮,以及蓝色的眼睛。它似乎正在看摄像机,可能对周围环境很好奇。 -------------------------------------------------- <<< 你是谁？ Input a media path or URL <<< 我是人工智能助手,随时准备帮助你解答问题或提供信息。 -------------------------------------------------- <<< 图中有几只羊 Input a media path or URL <<< http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/animal.png 图中有四只羊. -------------------------------------------------- <<< clear <<< 计算结果是多少 Input a media path or URL <<< http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/math.png 1452 + 45304 = 46756 -------------------------------------------------- <<< clear <<< 根据图片中的内容写首诗 Input a media path or URL <<< http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/poem.png 夜幕降临,星光闪烁, 一艘小船在河上飘荡, 船头挂着一盏明亮的灯, 照亮了周围的黑暗。 船上有两个人, 一个在船头,另一个在船尾, 他们似乎在谈话, 在星光下享受着宁静的时刻。 河岸边,树木在黑暗中站着, 在星光下投下长长的影子。 这景象是那么的宁静, 让人想起一个古老的传说。 小船,人,和星光, 构成了一个美丽的画面, 它唤起一种宁静的感觉, 在喧嚣的城市生活之外。 -------------------------------------------------- <<< clear <<< 对图片进行OCR Input a media path or URL <<< https://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/ocr.png 这是一段关于SWIFT的文字，其中包括了它的版本、功能以及一些链接。 """ ``` 示例图片如下: cat:
animal:
math:
poem:
ocr:
**单样本推理**
```python
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
from swift.llm import (
get_model_tokenizer, get_template, inference, ModelType,
get_default_template_type, inference_stream
)
from swift.utils import seed_everything
import torch
model_type = ModelType.yi_vl_6b_chat
template_type = get_default_template_type(model_type)
print(f'template_type: {template_type}')
model, tokenizer = get_model_tokenizer(model_type, torch.float16,
model_kwargs={'device_map': 'auto'})
model.generation_config.max_new_tokens = 256
template = get_template(template_type, tokenizer)
seed_everything(2) # ...
images = ['http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/road.png']
query = '距离各城市多远？'
response, history = inference(model, template, query, images=images)
print(f'query: {query}')
print(f'response: {response}')
# 流式
query = '距离最远的城市是哪？'
images = images * 2
gen = inference_stream(model, template, query, history, images=images)
print_idx = 0
print(f'query: {query}\nresponse: ', end='')
for response, history in gen:
delta = response[print_idx:]
print(delta, end='', flush=True)
print_idx = len(response)
print()
print(f'history: {history}')
"""
query: 距离各城市多远？
response: 距离甲塔14公里,距离阳江62公里,距离广州293公里,距离广州293公里。
query: 距离最远的城市是哪？
response: 最远的距离是293公里。
history: [['距离各城市多远？', '距离甲塔14公里,距离阳江62公里,距离广州293公里,距离广州293公里。'], ['距离最远的城市是哪？', '最远的距离是293公里。']]
"""
```
示例图片如下:
road:
## 微调
多模态大模型微调通常使用**自定义数据集**进行微调. 这里展示可直接运行的demo:
(默认只对LLM部分的qkv进行lora微调. 如果你想对所有linear含vision模型部分都进行微调, 可以指定`--lora_target_modules ALL`. 支持全参数微调.)
```shell
# Experimental environment: A10, 3090, V100...
# 19GB GPU memory
CUDA_VISIBLE_DEVICES=0 swift sft \
--model_type yi-vl-6b-chat \
--dataset coco-en-2-mini \
```
[自定义数据集](../LLM/自定义与拓展.md#-推荐命令行参数的形式)支持json, jsonl样式, 以下是自定义数据集的例子:
(支持多轮对话, 每轮对话须包含一张图片或不含图片, 支持传入本地路径或URL)
```jsonl
{"query": "55555", "response": "66666", "images": ["image_path"]}
{"query": "eeeee", "response": "fffff", "history": [], "images": ["image_path"]}
{"query": "EEEEE", "response": "FFFFF", "history": [["AAAAA", "BBBBB"], ["CCCCC", "DDDDD"]], "images": ["image_path", "image_path2", "image_path3"]}
```
## 微调后推理
直接推理:
```shell
CUDA_VISIBLE_DEVICES=0 swift infer \
--ckpt_dir output/yi-vl-6b-chat/vx-xxx/checkpoint-xxx \
--load_dataset_config true \
```
**merge-lora**并推理:
```shell
CUDA_VISIBLE_DEVICES=0 swift export \
--ckpt_dir output/yi-vl-6b-chat/vx-xxx/checkpoint-xxx \
--merge_lora true
CUDA_VISIBLE_DEVICES=0 swift infer \
--ckpt_dir output/yi-vl-6b-chat/vx-xxx/checkpoint-xxx-merged \
--load_dataset_config true
```
================================================
FILE: Cookbook/cn/ecosystem/使用Yi大模型玩转街霸三.md
================================================
# 使用Yi大模型玩转街霸三：从安装到应用的详细指南
## 引言
欢迎来到这篇详细的教学指南！在本文中，我们将探索如何使用Yi大模型（特别是yi-large模型）来玩转经典格斗游戏街霸三。这个有趣的项目将展示大语言模型（LLM）在游戏AI领域的潜力。无论你是AI爱好者还是游戏迷，这个教程都会带给你全新的体验。
本教程适合基础较薄弱的读者，我们会逐步详细讲解每个环节，确保你能顺利完成整个过程。
## 项目概述
在这个项目中，我们将：
1. 设置必要的环境和工具
2. 获取Yi模型的API密钥
3. 配置和运行游戏环境
4. 使用Yi模型控制街霸三的角色进行对战
.png)
## 实验环境
- 操作系统：Windows 或 Mac OS（本教程将以Mac OS为例）
- Python 3.10
- Docker Desktop
## 步骤1：环境准备
### 1.1 安装Docker Desktop
1. 访问 [Docker Desktop官网](https://www.docker.com/products/docker-desktop/)
2. 下载适合你系统的版本
3. 按照安装向导完成安装
4. 安装完成后，重启你的电脑
### 1.2 安装Conda
Conda是一个强大的包管理工具，我们将用它来创建虚拟环境。
1. 访问 [Conda官网](https://conda.io/projects/conda/en/latest/index.html)
2. 下载并安装Miniconda（推荐）或Anaconda
3. 安装完成后，打开终端，输入以下命令验证安装：
```
conda --version
```
如果显示版本号，说明安装成功。
.png)
### 1.3 注册Diambra账号
Diambra提供了我们需要的游戏环境。
1. 访问 [Diambra注册页面](https://diambra.ai/register)
2. 填写必要信息并完成注册
## 步骤2：获取Yi模型API密钥
1. 访问 [Yi大模型开放平台](https://platform.lingyiwanwu.com/)
2. 注册并登录账号
3. 在平台上创建新的API密钥
4. 保存好你的API密钥，我们后面会用到
.png)
## 步骤3：配置项目环境
### 3.1 克隆项目仓库
打开终端，执行以下命令：
```bash
git clone https://github.com/Yimi81/llm-colosseum.git
cd llm-colosseum
```
### 3.2 创建并激活虚拟环境
在项目目录下，执行：
```bash
conda create -n yi python=3.10 -y
conda activate yi
```
### 3.3 安装依赖
```bash
pip install -r requirements.txt
```
### 3.4 配置环境变量
1. 复制示例环境文件：
```bash
cp .env.example .env
```
2. 编辑.env文件：
在Mac上，你可能需要显示隐藏文件。使用快捷键 `Command + Shift + .` 切换显示/隐藏隐藏文件。
3. 打开.env文件，将`YI_API_KEY`替换为你之前获取的API密钥。
.png)
## 步骤4：启动游戏
### 4.1 找到ROM文件路径
在Mac环境中，ROM文件通常位于：
```
/Users/你的用户名/Desktop/code/llm-colosseum/.diambra/rom
```
记住这个路径，我们称之为`