Western scientists favor free AI tools like Perplexity for cited answers, Google Gemini for general tasks, Elicit & ResearchRabbit for literature discovery, Semantic Scholar for paper search, and Llama/DeepSeek models for open-source customization, with emphasis on reliable, cited information and specialized research functions beyond basic chat.
For General Research & Answers
Perplexity: Highly praised for providing direct, sourced answers with citations, acting like an AI search engine for reliable info.
Google Gemini (Free Tier): Offers powerful models and integration with Google Workspace for content creation and complex questions.
ChatGPT (Free): Still widely used as a versatile writing assistant and for general information lookups, though newer models often cost money.
For Literature & Paper Discovery
Elicit: Automates literature reviews, extracts insights, and generates structured summaries/tables from papers.
ResearchRabbit: Visualizes research networks, helps find related papers, and tracks trends with citation maps.
Semantic Scholar: Free, AI-powered search for scientific literature, offering filtering, PDF access, and semantic search.
ScienceOS (Science GPT): A specialized science chatbot that answers questions with references and builds citation networks.
For Open-Source & Customization
Llama (Meta): A popular, open-weight model family downloaded widely for community research and tailored use.
DeepSeek (DeepSeek-R1): Open-weight model with advanced reasoning, allowing researchers to download and customize the core model.
Key Considerations for Scientists
Citations: Tools like Perplexity, Elicit, and ScienceOS are favored because they cite sources, a crucial element for research validity.
Specialization: Scientists often prefer tools tailored for literature (Elicit, Semantic Scholar) over generic chatbots.
Openness: Open-weight models (Llama, DeepSeek) are valuable for deep customization.
How to download and customize the core model, DeepSeek-R1, open-weight model with advanced reasoning?
To download and customize the open-weight DeepSeek-R1 model, you can use local model runners like Ollama or frameworks like Hugging Face Transformers for more advanced fine-tuning.
Download the Model
The DeepSeek-R1 model comes in various sizes (1.5B, 7B, 8B, 14B, 32B, 70B parameters). The smaller, distilled versions are easier to run on consumer hardware.
Option 1: Using Ollama (Easiest for local deployment and running)
Ollama is a tool that simplifies running large language models locally.
Install Ollama by downloading the appropriate installer for your operating system (macOS, Linux, or Windows) from the official Ollama website.
Open your terminal or command prompt.
Pull the desired DeepSeek-R1 model version using the ollama pull command:
ollama pull deepseek-r1:1.5b
ollama pull deepseek-r1:8b (a popular choice)
ollama pull deepseek-r1:70b (requires significant GPU power)
Run the model in an interactive session with ollama run deepseek-r1:[version] (e.g., ollama run deepseek-r1:8b).
Option 2: Using Hugging Face and Python (For integration/customization)
This method provides more control and is necessary for fine-tuning.
Install prerequisites: Ensure you have Python, PyTorch, and the Hugging Face transformers library installed.
Download the model: You can load the model within a Python script using the transformers library. The library automatically handles the download and caching of the model files from the official DeepSeek-R1 repository on Hugging Face.
Load the model using the appropriate code snippets provided on the model card page, which typically involves a few lines of Python to load the pre-trained model and tokenizer.
Customize the Model
Customization typically involves fine-tuning the model on your specific dataset or using techniques like Retrieval-Augmented Generation (RAG) to provide external knowledge.
Fine-tuning: This is an advanced process that involves training the model further on a curated dataset to specialize its behavior.
You can use the Hugging Face training APIs or services like Amazon SageMaker AI for this purpose.
The DeepSeek team has released distilled models specifically for the community to fine-tune and research.
Guides for fine-tuning often involve using specialized libraries and require substantial computational resources (GPUs).
Prompt Engineering: To encourage its advanced reasoning capabilities in standard use, instruct the model to start its response with the <think>\n tag in your prompt. This triggers its methodical reasoning process.
RAG (Retrieval-Augmented Generation): Integrate the model with a vector database (like Milvus) and an application interface (like AnythingLLM or Open WebUI) to give it access to your specific documents and data. This allows the model to provide context-aware answers without modifying its core weights.
Milvus is a leading open-source, cloud-native vector database designed for high-performance similarity search on massive datasets, crucial for modern AI applications like semantic search, recommendation engines, and Generative AI. It efficiently stores, indexes, and manages embedding vectors (numerical representations of unstructured data like text, images, or audio), enabling it to find semantically similar items beyond simple keyword matching. It offers scalability, runs on various environments (laptops to cloud), and supports integrations with many embedding models, powering applications needing fast, context-aware data retrieval.
Open WebUI is a free, self-hostable, open-source web interface for interacting with AI models, offering a user-friendly chat experience similar to ChatGPT but with more control, offline functionality, and support for local models (like Ollama) or OpenAI APIs, featuring built-in RAG, web search, image generation, and user management for private, secure, and customizable AI workflows. It allows for creating custom chatbots, using prompt templates, and managing data locally, making powerful AI accessible for personal or organizational use.
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