Ollama - Access Powerful Large Language Models Locally

July 17, 2024

Ollama

Have you ever heard of artificial intelligence (AI) whiz kids generating realistic text, translating languages in a flash, or even writing basic code? 

This magic is powered by large language models (LLMs), complex algorithms trained on massive amounts of data. But what if you could use this power on your own computer, without relying on cloud services or needing a Ph.D. in AI? Enter Ollama, a game-changer in the LLM landscape.

Understanding LLMs: The Brains Behind the Brawn

LLMs are like supercharged brains, gobbling up text and code and learning to perform various tasks. They can generate different creative text formats, translate languages, write different kinds of creative content, and answer your questions in an informative way. 

However, traditionally, using LLMs required special access to cloud computing resources, making them inaccessible to most people.

Large Language Model

What is Ollama?

Ollama is an open-source toolkit specifically designed to run large language models (LLMs) locally on your own computer. In simpler terms, it acts as a bridge between powerful AI tech and user experience. 

It simplifies the process of downloading, installing, and interacting with various LLMs, making them accessible even for those without extensive AI knowledge.

Who Made Ollama Ai And What Is The Need To Create Ollama

Ollama was created by Jeffrey Morgan and Michael Chiang. Their vision was to democratize access to powerful large language models (LLMs). 

Traditionally, LLMs reside on cloud platforms, requiring significant computing power and expertise to use. Ollama addresses this by providing a user-friendly, open-source toolkit that allows anyone to run LLMs on their own machine.

The need for Ollama arose from the growing interest in LLMs and the limitations of cloud-based solutions. Cloud access can be expensive, have latency issues, and raise privacy concerns for some users. 

Additionally, cloud platforms often require users to be familiar with complex APIs and configurations. Ollama solves these problems by making LLMs more accessible, affordable, and private for a wider range of users.

Features

Ollama boasts several key features that make it a powerful tool for working with large language models (LLMs) locally:

  • Local Execution: Unlike cloud-based LLMs, Ollama allows you to run models directly on your own computer. This offers several benefits:

     

    • Offline Usage: No internet connection is required, making Ollama perfect for situations with limited or unreliable internet access.
    • Privacy: Processing happens entirely on your machine, addressing privacy concerns some users have with cloud platforms.
    • Faster Performance: Local execution can significantly reduce latency compared to cloud-based models.
  • Diverse LLM Library: Ollama provides access to a growing library of pre-trained LLMs. This includes versatile general-purpose models and specialized models tailored for specific tasks or domains. You can choose the model that best suits your needs.

     

  • Simple Setup and Management: Ollama streamlines the often complex process of downloading, installing, and configuring LLMs. The user-friendly interface makes it easy to get started, even for those without extensive AI expertise.

     

  • Customization and Fine-tuning: Ollama empowers you to fine-tune the parameters of the LLMs. This allows you to adjust settings and tailor the model’s behavior to your specific needs and preferences. Experiment with different configurations to optimize performance for your particular tasks.

     

  • Local API Integration: Ollama offers a local API, enabling developers to seamlessly integrate LLMs into their applications and workflows. This opens doors for innovative AI-powered tools and functionalities.

     

What Does Ollama Do?

Ollama functions as a local platform for running powerful large language models (LLMs) on your personal computer. It bypasses the traditional approach of relying on cloud-based systems, making LLMs more accessible and user-friendly. 

Here’s a explanation of what Ollama does, along with examples to illustrate its capabilities:

  1. Local Execution of LLMs:
  • Ollama downloads and installs pre-trained LLM models directly onto your machine. This enables you to utilize these models without an internet connection, making it ideal for:
    • Offline content creation: Imagine you’re a writer working on a novel during a long flight. Ollama allows you to leverage an LLM for tasks like generating creative text suggestions or checking grammar, even without internet access.
    • Privacy-focused tasks: If you’re working with sensitive information, Ollama provides a secure environment for using LLMs, as all processing occurs locally on your machine. This can be beneficial for legal documents, medical records, or any data where privacy is paramount.
  1. Diverse LLM Library and Management:
  • Ollama offers a curated library of pre-trained LLMs, encompassing general-purpose models and specialized ones for specific domains. You can choose the most suitable model for your needs.
    • Example 1: You need help writing marketing copy for a new product launch. Ollama allows you to use an LLM specifically trained for marketing tasks. This model can generate creative product descriptions, suggest headlines, or even craft targeted social media posts.
    • Example 2: You’re a data scientist working on a research project. Ollama provides access to specialized LLMs trained on scientific data. You can leverage these models to analyze research papers, summarize complex findings, or even generate hypotheses based on existing data sets.
  1. User-Friendly Setup and Customization:
  • Ollama simplifies the often-complex process of setting up and managing LLMs. Its user-friendly interface makes it easy to download, install, and configure models, even for beginners with limited AI experience.
  • Furthermore, Ollama allows customization by letting you fine-tune the parameters of the LLMs. You can adjust settings to tailor the model’s behavior to your specific goals.
    • Example: You’re using an LLM for writing different creative text formats, like poems, scripts, or code. Ollama allows you to fine-tune the model for each format, improving the quality and style of the generated text.
  1. Local API Integration for Developers:
  • Ollama offers a local API, enabling developers to seamlessly integrate LLMs into their existing applications and workflows. This opens doors for creating innovative tools and functionalities powered by AI.

Example: A developer could build a custom writing assistant application that leverages Ollama’s LLMs to provide real-time grammar and style suggestions as users write.

What is Ollama AI used for?

Ollama AI isn’t exactly an AI itself, but rather a software tool that allows you to interact with large language models (LLMs) on your own computer. Here’s how Ollama aids in various tasks:

  1. Creative Writing and Content Generation:
  • Writers can overcome writer’s block, brainstorm ideas, and generate different creative text formats like poems, scripts, or code. Ollama’s LLMs can help with tasks like:
    • Crafting unique product descriptions or marketing copy.
    • Generating headlines and social media posts.
    • Writing different creative text formats
  1. Code Generation and Assistance:
  • Programmers can leverage Ollama’s LLMs for:
    • Writing entire code snippets or functions.
    • Debugging existing code and suggesting fixes.
    • Generating code documentation to improve readability.
  1. Research and Education:
  • Ollama empowers researchers and students to:
    • Analyze research papers and summarize complex findings.
    • Explore natural language processing (NLP) concepts in a hands-on way.
    • Develop custom LLMs for specific educational purposes.
  1. General Productivity and Personal Use:
  • Ollama can be a helpful tool for various personal tasks, such as:
    • Summarizing lengthy documents or emails.
    • Translating languages (depending on the capabilities of the chosen LLM).
    • Generating different creative text formats for personal projects or entertainment.
  1. Building AI-powered Applications:
  • Developers can leverage Ollama’s local API to integrate LLMs into their applications, creating innovative features like:
    • Real-time grammar and style suggestions in writing assistants.
    • Chatbots with improved conversation flow and understanding.
    • AI-powered tools for specific industries or domains.

By making LLMs accessible locally, Ollama opens doors to a wide range of applications across various fields. It empowers users to experiment with AI technology and create innovative solutions without relying on cloud-based services.

What Are The Benefits Of Ollama?

Ollama offers a compelling set of benefits that make it a valuable tool for anyone interested in working with large language models (LLMs). Here’s a breakdown of its key advantages:

Cost-Effectiveness:

  • Reduced reliance on cloud services: Traditionally, using LLMs requires significant computing power often found on cloud platforms. Ollama allows you to leverage your own computer’s resources, eliminating the ongoing costs associated with cloud-based solutions.

Data Privacy and Security:

  • Local processing: Unlike cloud-based LLMs where data is processed on remote servers, Ollama keeps everything on your machine. This ensures greater control over your data and addresses privacy concerns for users handling sensitive information.

Customization and Flexibility:

  • Fine-tuning capabilities: Ollama empowers users to fine-tune the parameters of the LLMs. This allows you to tailor the model’s behavior to your specific needs and preferences. For instance, you can adjust settings to optimize performance for writing different creative formats or analyzing scientific data.
  • Diverse LLM library: Ollama offers access to a growing collection of pre-trained LLMs. This includes versatile general-purpose models and specialized ones for specific tasks or domains. You can choose the model that best suits your project requirements.

Offline Access and Reliability:

  • No internet dependency: Ollama allows you to use LLMs even without an internet connection. This is crucial for situations with limited or unreliable internet access, ensuring uninterrupted workflow.
  • Reduced latency: Local execution of LLMs on your machine can significantly improve response times compared to cloud-based models. This translates to a more efficient and responsive user experience.

Experimentation and Learning:

  • Open-source platform: Ollama’s open-source nature fosters a collaborative environment for developers and researchers. Users can access the source code, modify it to explore new functionalities, and contribute to the project’s development.
  • Hands-on experience: Ollama provides a platform for users of all experience levels to experiment with LLMs directly. This allows for a deeper understanding of LLM capabilities and limitations, promoting exploration and learning within the field of AI.

Accessibility and Ease of Use:

  • Simplified setup and management: Ollama streamlines the often complex process of downloading, installing, and configuring LLMs. The user-friendly interface makes it easy to get started, even for those without extensive AI expertise.
  • Reduced entry barrier: By making LLMs readily available and user-friendly, Ollama democratizes access to this powerful technology. This empowers individuals and smaller organizations to leverage LLMs for their projects without the need for significant resources or expertise.

Potential for Integration:

  • Local API for developers: Ollama offers a local API, enabling developers to seamlessly integrate LLMs into their existing applications and workflows. This opens doors for creating innovative tools and functionalities powered by AI.

Limitations Of Ollama

Ollama offers a powerful and accessible way to interact with large language models (LLMs) locally, it does come with some limitations to consider:

Computational Requirements:

  • Hardware limitations: Running LLMs, especially complex or larger models, requires significant computational resources like processing power and memory. Ollama may not function optimally on computers with limited hardware capabilities, leading to slow response times or even model crashes.

Limited Functionality Compared to Cloud-Based Solutions:

  • Resource constraints: Local machines typically have fewer resources compared to powerful cloud servers. This can restrict the features and functionalities available in Ollama compared to cloud-based LLM solutions. For instance, cloud platforms might offer more advanced features like model training or access to a wider range of pre-trained models.

Data Dependence:

  • Quality and quantity of data: The effectiveness of LLMs is highly reliant on the quality and quantity of data they are trained on. Ollama relies on pre-trained models, and the quality of the outputs may be limited by the data used for training. If the training data is biased or limited, the LLM may exhibit similar biases or limitations in its responses.

Ethical Considerations:

  • Transparency and accountability: As with any AI technology, ethical considerations arise regarding Ollama’s decision-making processes. Since Ollama utilizes pre-trained models, it can be challenging to understand the reasoning behind the model’s outputs. Additionally, ensuring accountability for potential biases within the LLM can be complex.

User Expertise:

  • Learning curve: While Ollama simplifies LLM usage compared to complex cloud-based solutions, there’s still a learning curve involved. Understanding how to choose the right LLM for your task, fine-tuning parameters, and interpreting the outputs might require some technical knowledge.

Limited Technical Support:

  • Open-source nature: Ollama is an open-source project, and while it has a growing community, it may not offer the same level of dedicated technical support as some commercial LLM platforms. Troubleshooting issues or receiving assistance might require more user effort or involvement in the project’s community forums.

Security Considerations:

  • Local processing: While local processing offers privacy benefits, it also means the LLM model resides on your machine. This necessitates implementing proper security measures to protect the model from unauthorized access or potential vulnerabilities.

Ollama is a valuable tool for working with LLMs locally, it’s essential to be aware of its limitations. Understanding these limitations can help you make informed decisions about whether Ollama is the right choice for your specific needs and projects.

Is Ollama Open-Source?

Yes, Ollama is indeed an open-source project! This means the source code is freely available for anyone to access, modify, and contribute to. Here are some benefits of Ollama being open-source:

  • Transparency: The open-source nature allows anyone to examine the code behind Ollama, fostering trust and understanding of how it works.
  • Collaboration: Developers and researchers can contribute to Ollama’s development by suggesting improvements, fixing bugs, or adding new features. This fosters a collaborative environment and accelerates the project’s growth.
  • Accessibility: Open-source eliminates licensing fees, making Ollama accessible to a wider range of users and organizations, even those with limited budgets.
  • Customization: Users with programming expertise can modify the Ollama code to suit their specific needs and preferences.

You can find the Ollama source code on their GitHub repository.

Is Ollama Safe To Run?

llama’s safety depends on how you use it and the context. Here’s a breakdown of the safety considerations:

Generally Safe:

  • Open-source nature: Being open-source allows scrutiny of the code, making it less likely for malicious functionality to be hidden.
  • Local processing: If you download models from reputable sources and practice good security hygiene, local processing can mitigate some risks associated with cloud-based models where data is sent to external servers.

Potential Risks:

  • Model vulnerabilities: Any software, including LLMs, can have vulnerabilities. Malicious actors could exploit these vulnerabilities to gain unauthorized access to your system or manipulate the model’s outputs.
  • Data dependence: The quality of the LLM’s outputs depends on the training data. Biases or malicious content within the training data can be reflected in the model’s responses.
  • Security considerations: Since the model runs locally, it’s your responsibility to implement proper security measures to protect it from unauthorized access.

Safety Tips:

  • Download models from trusted sources: Be cautious about downloading models from unknown sources, as they might contain malware or be trained on biased data.
  • Keep Ollama updated: Ensure you’re running the latest version of Ollama to benefit from security patches and bug fixes.
  • Practice good security hygiene: Implement standard security measures on your computer, including firewalls and antivirus software.
  • Understand the limitations: Be aware of the potential biases and limitations of LLMs to avoid misinterpreting their outputs.

Overall:

Ollama can be a safe tool to run if you use it cautiously and take steps to mitigate potential risks. However, for tasks involving highly sensitive information, a cloud-based LLM solution with robust security measures might be a more suitable choice.

Why Should I Use Ollama?

Whether or not Ollama is the right choice for you depends on your specific needs and priorities. Here’s a breakdown of the pros and cons to help you decide:

Reasons to Use Ollama:

  • Cost-effective: No need to pay for cloud services, making it a budget-friendly option for individuals and smaller organizations.
  • Privacy-focused: Local processing keeps your data on your machine, addressing privacy concerns for sensitive tasks.
  • Customization and Flexibility: Fine-tune models for specific needs and access a diverse library of pre-trained models.
  • Offline functionality: Use LLMs even without an internet connection, ideal for situations with limited or unreliable access.
  • Reduced latency: Local execution can lead to faster response times compared to cloud-based solutions.
  • Open-source and collaborative: Contribute to the project’s development, learn from the code, and benefit from community support.
  • Experimentation and learning: Hands-on experience for understanding LLMs and exploring their potential.
  • Easy to use: User-friendly interface simplifies setup and management, even for beginners.
  • Potential for integration: Local API allows developers to integrate LLMs into their applications.

Things to Consider Before Using Ollama:

  • Hardware limitations: Complex models might require powerful hardware for smooth operation.
  • Limited functionality compared to cloud: Local resources might restrict features compared to cloud-based solutions.
  • Data dependence: The quality of outputs hinges on the data used to train the LLM models.
  • Ethical considerations: Understanding the model’s decision-making process and potential biases can be challenging.
  • Learning curve: While easier than complex cloud solutions, some technical knowledge might be helpful for optimal use.
  • Limited technical support: Troubleshooting might require more user effort or involvement in the Ollama community.
  • Security considerations: Local processing necessitates implementing proper security measures to protect the model.

Use Ollama if:

  • You prioritize privacy and control over your data.
  • You work with sensitive information and require offline access.
  • You want to experiment and learn about LLMs in a hands-on way.
  • You have the necessary hardware to run the models you need.
  • You value the cost-effectiveness and open-source nature of the platform.
  • You’re interested in integrating LLMs into your own applications (for developers).

Consider alternatives like cloud-based LLMs if:

  • You need access to a wider range of advanced features or functionalities.
  • You don’t have powerful enough hardware to run complex models locally.
  • Robust security measures are crucial for your specific use case.
  • You require dedicated technical support.

Reasons to Consider Alternatives:

  • Computational limitations: Complex LLMs might require more processing power than your computer can handle, leading to slow performance.
  • Limited functionality: Ollama might offer fewer features compared to advanced cloud-based LLM solutions.
  • Data dependence: The quality of LLM outputs depends on the training data. Ollama relies on pre-trained models with potential limitations.
  • Ethical considerations: Understanding the LLM’s decision-making process and ensuring accountability for potential biases can be challenging.
  • Learning curve: While user-friendly, Ollama still requires some knowledge to choose models, fine-tune parameters, and interpret outputs.
  • Limited technical support: Troubleshooting issues might require more user effort or involvement in the Ollama community forums.
  • Security considerations: Local processing necessitates implementing proper security measures to protect the LLM model.

Ultimately, the choice depends on your priorities and project requirements. Ollama is a valuable tool for specific needs, but it’s essential to be aware of its limitations to make an informed decision.

Does Ollama Need The Internet?

No, Ollama itself does not require internet access to function. Here’s why:

  • Local Processing: Ollama allows you to run large language models (LLMs) directly on your own computer. Once the model is downloaded and installed, it operates entirely on your local machine.
  • Offline Functionality: This feature makes Ollama ideal for situations where you have limited or unreliable internet access. You can still leverage the power of LLMs for tasks like writing, code generation, or data analysis, even without being online.

However, there are a couple of scenarios where Ollama might interact with the internet:

  • Downloading Models: The initial download of the LLM models themselves might require an internet connection. Ollama offers a library of pre-trained models, and you’ll need internet access to download them to your computer.
  • Updates: Similar to other software, Ollama may periodically check for updates or bug fixes online. However, this is not mandatory for basic functionality.

Is Ollama Private?

Ollama offers a high degree of privacy compared to cloud-based LLM solutions. Here’s how:

  • Local Processing: Since all processing happens on your machine, your data and the LLM’s outputs never leave your computer. This eliminates concerns about data being sent to external servers and potentially accessed by third parties.
  • No Cloud Reliance: Unlike cloud-based LLMs, Ollama doesn’t require you to upload your data or interact with remote servers. This minimizes the risk of data breaches or unauthorized access.

Important Note:

While Ollama itself promotes privacy, it’s still your responsibility to ensure the security of the downloaded LLM models. Be cautious about downloading models from untrusted sources, as they might contain malware or be trained on biased data.

Getting Started with Ollama: It's Easier Than You Think!

ollama

Large language models (LLMs) are revolutionizing the way we interact with computers. But traditionally, using them required cloud access and technical expertise. Ollama changes the game by making LLMs accessible and user-friendly, right on your own computer!

Step 1: Download Ollama

Head over to the Ollama website and download the installer for your operating system (Mac, Windows, or Linux). The installation process is straightforward and takes just a few minutes.

Step 2: Get Your Model

Ollama offers a growing library of pre-trained LLMs. You can browse the available models based on their size and purpose. Here are some options:

  • General-purpose models: Ideal for a wide range of tasks like writing, translation, or code generation. (e.g., Llama 3)
  • Specialized models: Tailored for specific domains like science or marketing. (e.g., Gemma 2B)

Step 3: Run the Model

Once you’ve chosen your model, Ollama takes care of downloading and installing it on your machine. With the model ready, you can simply start interacting with it!

Step 4: Interact and Explore

Ollama provides a user-friendly interface called the “repple.” Here, you can type prompts and questions for the LLM to respond to. Experiment with different prompts and see how the LLM generates creative text formats, translates languages (depending on the model), or even helps you with coding tasks.

Ready to Get Started?

Ollama opens doors to a world of possibilities with LLMs. Download it today and explore the potential of this powerful technology directly on your computer!

Some Tips

  • The Ollama website offers comprehensive documentation to guide you through the setup process and various functionalities: 
  • The Ollama community is a valuable resource for troubleshooting or finding inspiration for your LLM projects. You can find them on the Ollama website or online forums.

With Ollama, unleashing the power of LLMs is just a few clicks away. So, dive in and discover the exciting world of local AI!

Ollama

Who Should Use Ollama?

Ollama can be a valuable tool for a wide range of users interested in exploring and utilizing large language models (LLMs) locally. Here’s a breakdown of who can benefit most from Ollama:

  1. Content Creators and Writers:
  • Overcome writer’s block: Generate ideas, brainstorm content, and explore different creative writing formats like poems, scripts, or code.
  • Enhance writing quality: Leverage LLMs for grammar and style suggestions, improving the overall flow and clarity of your writing.
  • Content marketing assistance: Generate product descriptions, headlines, or social media posts specifically tailored for marketing purposes.
  1. Developers and Programmers:
  • Boost coding productivity: Generate entire code snippets or functions, speeding up development processes.
  • Improve code quality: Utilize LLMs for debugging existing code and suggesting potential fixes or improvements.
  • Enhance code documentation: Automatically generate clear and concise documentation for your code, making it easier for others to understand.
  1. Researchers and Students:
  • Analyze research data: Summarize complex research papers or findings, accelerating the research process.
  • Explore NLP concepts: Gain practical experience by directly interacting with LLMs and observing their natural language processing capabilities.
  • Develop custom LLMs: Ollama’s open-source nature allows researchers to build and experiment with their own specialized LLMs for specific educational purposes.
  1. General Users and Professionals:
  • Boost productivity: Summarize lengthy documents or emails, saving time and effort.
  • Language translation: Depending on the LLM capabilities, Ollama can assist with basic language translation tasks for personal or professional use.
  • Spark creativity: Generate different creative text formats like poems or stories for entertainment or personal projects.
  1. Developers Building AI-powered Applications:
  • Seamless LLM integration: Ollama’s local API allows developers to integrate LLMs into their applications, creating innovative features like real-time grammar suggestions or chatbots with improved conversation flow.

Beyond these specific groups, anyone curious about LLMs and their potential can benefit from using Ollama. Its user-friendly interface and open-source nature make it a great platform to get started with this powerful AI technology.

Here are some additional factors to consider when deciding if Ollama is right for you:

  • Technical knowledge: While Ollama is user-friendly, some basic technical understanding might be helpful for choosing the right LLM, customizing parameters, and interpreting the outputs.
  • Computational resources: Running complex LLMs requires significant processing power. Ensure your computer meets the minimum system requirements for optimal performance.
  • Privacy concerns: Ollama prioritizes privacy by keeping everything local. However, it’s crucial to download models from trusted sources and practice good computer security hygiene.

The future of Ollama

The future of Ollama appears bright, with its focus on local large language models (LLMs) aligning with several promising trends in AI development:

  1. Democratization of AI: Ollama’s open-source nature and user-friendly interface make LLMs more accessible to individuals and smaller organizations. This can lead to wider adoption and innovation in various fields.
  2. Increased Focus on Privacy: As privacy concerns around cloud-based solutions grow, Ollama’s local processing offers a secure alternative for users handling sensitive data.
  3. Advancements in LLM Technology: The field of LLMs is constantly evolving. We can expect Ollama to integrate future advancements, offering users access to even more powerful and versatile models.
  4. Decentralized AI Infrastructure: The concept of local LLMs aligns with the growing interest in decentralized AI, where processing power and data are distributed rather than concentrated in large cloud platforms.

Potential future directions for Ollama

  • Expanding Model Capabilities: Ongoing research will likely lead to more powerful LLMs with improved performance, increased efficiency, and expanded capabilities in areas like multimodality (processing different types of data like text and images), multilingualism (understanding and generating text in multiple languages), and domain-specific knowledge (specialization in specific fields). Ollama will need to adapt to integrate these advancements.
  • Hardware Optimization: As hardware technology progresses, Ollama could potentially leverage advancements in areas like specialized AI chips or edge computing to improve the efficiency of running LLMs on local machines, even for complex models.
  • Decentralized Model Sharing: The Ollama community might explore ways to facilitate secure and efficient sharing of custom-trained LLM models among users. This could foster collaboration and accelerate innovation within the Ollama ecosystem.
  • Improved User Experiences: The Ollama user interface can continuously evolve to become even more intuitive and user-friendly for a wider range of users, with potential features like drag-and-drop functionality or pre-built workflows for specific tasks.

The future of Ollama hinges on its ability to adapt and integrate these advancements while staying true to its core principles of open-source development, local processing, and user-friendliness. If it can successfully navigate these changes, Ollama has the potential to become a cornerstone platform for democratizing access to powerful AI technology and fostering innovation in the years to come.

Final Words

Ollama presents a compelling option for anyone interested in taking advantage of the power of large language models (LLMs) locally. 

Whether you’re a content creator seeking inspiration, a developer aiming to boost productivity, or simply someone curious about AI technology, Ollama offers a user-friendly and accessible entry point.

Its focus on local processing allows users with control over their data and privacy, while the open-source nature strengthens collaboration and innovation within the Ollama community. With advancements in LLM technology and potential areas for future development like hardware optimization and decentralized model sharing, Ollama is poised to play a significant role in democratizing access to AI and shaping the future of this exciting field. 

So, why not download Ollama today and embark on your own journey of exploration with large language models?

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