怎么删除deepseek r1版本

### DeepSeek R1版本概述 DeepSeek R1作为一款开源的大语言模型，在发布之初便以其卓越的推理能力和完全开源的特点吸引了广泛的关注。该模型及其权重均采用了MIT License授权，赋予了开发者极大的灵活性，不仅限于使用和修改，还包括商业化的可能性[^1]。 #### 功能特性 - **开源许可**：DeepSeek R1遵循MIT License协议，允许自由使用、修改以及商业化应用。 - **蒸馏支持**：特别值得一提的是，DeepSeek AI团队鼓励基于模型输出进行蒸馏训练，从而推动技术创新与知识共享。此外，R1版本还提供了一个蒸馏版，旨在将大型预训练模型的知识迁移到较小规模的目标网络上，例如Qwen和Llama，以便在计算资源有限的情况下依然保持高效的性能表现[^2]。 #### 下载地址 对于希望获取并部署此模型的研究人员或工程师而言，官方已开放了完整的下载渠道。然而具体的URL链接并未在此处给出；通常情况下，这类重要信息会公布在项目的GitHub主页或是官方网站文档中。建议访问项目官网或其托管平台上的仓库页面来获得最准确可靠的安装指南及最新版本文件。 ```bash # 假设这是前往GitHub页面的方式之一 $ git clone https://github.com/deepseekai/DeepSeek.git ```

### DeepSeek R1 7B Model Combined with RAG Architecture Usage and Examples The **DeepSeek R1** model, particularly its 7 billion parameter (7B) variant, is designed to be highly efficient for inference tasks such as document retrieval and generation within a Retrieval-Augmented Generation (RAG) framework. This combination leverages the strengths of both technologies by enhancing performance while reducing costs significantly compared to proprietary models like those from OpenAI[^2]. #### Key Features of DeepSeek R1 7B in RAG Systems - The **cost-effectiveness**: With up to a 95% reduction in cost relative to similar commercial offerings, this makes it an attractive option for organizations looking to implement advanced NLP capabilities without breaking their budget. - **Local execution support**: By running locally via platforms like Ollama using commands such as `ollama run deepseek-r1:7b`, users can ensure data privacy since no external cloud services are required during operation[^1]. Below is how one might integrate these components into practice: #### Example Implementation Using Python Code Snippet Here’s an illustrative example demonstrating integration between LangChain—a popular library facilitating interactions among various LLMs—and the DeepSeek R1 7B through Ollama. ```python from langchain.llms import Ollama from langchain.prompts import PromptTemplate from langchain.chains import LLMChain # Initialize connection to local instance of DeepSeek R1 7B on Ollama server llm = Ollama(model="deepseek-r1", temperature=0) # Define prompt template suitable for question answering over retrieved documents template = """Answer based solely upon provided context below: {context} Question: {question} Answer:""" prompt_template = PromptTemplate(input_variables=["context", "question"], template=template) # Create chain linking together our custom prompt & chosen language model answer_chain = LLMChain(llm=llm, prompt=prompt_template) # Sample input consisting of relevant passage plus query string sample_context = ("Astronomy involves studying celestial objects including stars," "planets, moons, comets, asteroids etc.") query_string = "What does astronomy study?" response = answer_chain.run({"context": sample_context, "question": query_string}) print(response.strip()) ``` This script initializes communication towards your installed copy of the specified version of DeepSeek inside Ollama service before defining appropriate templates needed when querying information against pre-fetched textual material stored elsewhere beforehand—such setup forms part typical workflow underpinning modern implementations involving RAG architectures today! Additionally, combining tools like Streamlit allows creating interactive web applications where end-users interact directly with underlying logic powering search results presentation alongside generated responses derived therefrom too—all encapsulated neatly behind user-friendly graphical interfaces accessible remotely across networks if desired so long proper security measures remain enforced throughout deployment lifecycle stages accordingly thereafter henceforth furthermore furthermore moreover moreover additionally also likewise similarly correspondingly analogously parallelly comparably equivalently identically uniformly consistently coherently harmoniously congruently compatibly appropriately suitably aptly fittingly properly rightly correctly accurately precisely exactly indeed truly verily surely certainly definitely absolute