Ollama如何构建自己的Llama3中文模型模型

2024-05-16码农

Ollama

Ollama 是一个开源的大型语言模型（LLM）服务工具，它允许用户在本地机器上运行和部署大型语言模型。Ollama 设计为一个框架，旨在简化在 Docker 容器中部署和管理大型语言模型的过程，使得这一过程变得简单快捷。用户可以通过简单的命令行操作，快速在本地运行如 Llama 3 这样的开源大型语言模型。

应用模型

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注意：推荐下载 GGUF文件格式的模型，可以快速简洁的导入 Ollama 中

0X01 下载模型文件

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下载地址： https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat-GGUF-8bit/tree/main

下载 Llama3-8B-Chinese-Chat-q8_0-v2_1.gguf 模型文件

0X02 编写模型文件

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Modelfile 文件内容如下：

# FROM 指定 GGUF 文件的路径 FROM D:/AI/Download/Llama3-8B-Chinese-Chat-q8_0-v2_1.gguf

0X03 创建 Ollama 模型

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使用 ollama create 命令可以根据 Modelfile 创建一个新的模型

ollama create tinywan-Llama3-8B-Chinese -f ./Modelfile transferring model data using existing layer sha256:ea6e8d5cda0fc798898b67f6e728eb2d02877a2599aa3c8290aaa6f681c2bb9e creating new layer sha256:e6e86a160950a92b7b32fcd6fcbc830e38634be6d616ec57452561bfe2a243fb writing manifest success

这个命令会读取 Modelfile 中的配置，并创建一个名为 tinywan-Llama3-8B-Chinese 的新模型。

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通过命令 ollama list 查看模型列表

> ollama list NAME ID SIZE MODIFIED tinywan-Llama3-8B-Chinese:latest adcb30feaee5 16 GB About a minute ago llama3:8b a6990ed6be41 4.7 GB 2 weeks ago

0X04 运行 Ollama 模型

使用 ollama run 命令来验证 tinywan-Llama3-8B-Chinese 新模型

ollama run tinywan-Llama3-8B-Chinese:latest

模型测试

>>> 写一首诗吧。"我说。他眨了眨眼，微笑着回答道：「好啊，我试试看。」然后，他闭上眼睛，开始吟唱：在星空之下，月光洒银辉。风轻轻地舞，树叶低语。山川河流静默，夜晚笼罩大地。但就在这宁静中，我感受到生命的脉动，它如同诗歌般跳跃，穿梭于每一颗心中。

REST API

除了命令行界面， OLlama 还提供了 REST API ，使得您可以通过HTTP请求与模型交互。这对于在Web应用程序中集成ollama尤其有用。

请求

要生成模型的响应，您可以发送一个POST请求到 /api/generate

curl -X POST http://localhost:11434/api/generate -d '{ "model": "llama3:8b", "prompt": "中文回答。你是什么大模型？", "stream": false }'

响应

返回一个JSON对象流：

{ "model": "llama3:8b", "created_at": "2024-05-12T09:00:01.9513668Z", "response": "I'm LLaMA, a large language model developed by Meta AI that can understand and respond to human input in a conversational manner. I've been trained on a massive dataset of text from the internet and can generate human-like responses to a wide range of topics and questions. My training data includes but is not limited to:\n\n* Web pages: articles, blogs, forums\n* Books: fiction and non-fiction\n* Research papers: academic journals, research articles\n* User-generated content: social media, comments, reviews\n\nI'm able to generate responses that are contextualized to the conversation I'm having with you. This means I can recall previous statements or questions in our conversation and respond accordingly.\n\nMy capabilities include:\n\n* Answering questions on a wide range of topics, from science and history to entertainment and culture\n* Generating text summaries of long pieces of content\n* Translating text from one language to another (in this case, Chinese to English)\n* Responding to natural language input in a conversational manner\n\nI'm constantly learning and improving my responses based on the conversations I have with users like you. So feel free to ask me anything, and I'll do my best to provide helpful and accurate information!", "done": true, "done_reason": "stop", "context": [ 128006, ... 128009 ], "total_duration": 37185731000, "load_duration": 10876300, "prompt_eval_count": 13, "prompt_eval_duration": 1058480000, "eval_count": 248, "eval_duration": 36115711000 }