Tag: Ollama

Categories
News

How to run Gemma AI locally using Ollama

How to run Gemma AI locally using Ollama

If like me you are interested in learning more about the new Gemma open source AI model released by Google and perhaps installing and running it locally on your home network or computers. This quick guide will provide you with a overview of the integration of Gemma models with the HuggingFace Transformers library and Ollama. Offering a powerful combination for tackling a wide range of natural language processing (NLP), tasks.

Ollama is an open-source application specifically designed and built to enable you to run, create, and share large language models locally with a command-line interface on MacOS, Linux and is now available on Windows. It is worth remembering that you should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.

Gemma models are at the forefront of NLP technology, known for their ability to understand and produce text that closely resembles human communication. These models are incredibly versatile, proving useful in various scenarios such as improving chatbot conversations or automating content creation. The strength of Gemma models lies in their inference methods, which determine how the model processes and responds to inputs like prompts or questions.

To harness the full potential of Gemma models, the HuggingFace Transformers library is indispensable. It provides a collection of pre-trained language models, including Gemma, which are ready to be deployed in your projects. However, before you can access these models, you must navigate through gated access controls, which are common on platforms like Kaggle to manage model usage. Obtaining a HuggingFace token is necessary to gain access. Once you have the token, you can start using the models, even in a quantized state on platforms such as CoLab, to achieve a balance between efficiency and performance.

Running Google Gemma locally

Here are some other articles you may find of interest on the subject of Google AI models

A critical aspect of working with Gemma models is understanding their tokenizer. This component breaks down text into smaller units, or tokens, that the model can process. The way text is tokenized can greatly affect the model’s understanding and the quality of its output. Therefore, getting to know Gemma’s tokenizer is essential for successful NLP applications.

For those who prefer to run NLP models on their own hardware, Ollama offers a solution that allows you to operate Gemma models locally, eliminating the need for cloud-based services. This can be particularly advantageous when working with large models that may contain billions of parameters. Running models locally can result in faster response times and gives you more control over the entire process.

After setting up the necessary tools, you can explore the practical applications of Gemma models. These models are skilled at generating structured responses, complete with markdown formatting, which ensures that the output is not only accurate but also well-organized. Gemma models can handle a variety of prompts and questions, showcasing their flexibility and capability in tasks such as translation, code generation, and creative writing.

As you work with Gemma models, you’ll gain insights into their performance and the dependability of their outputs. These observations are crucial for deciding when and how to fine-tune the models to better suit specific tasks. Fine-tuning allows you to adjust pre-trained models to meet your unique needs, whether that’s improving translation precision or enhancing the quality of creative writing.

The customization possibilities with Gemma models are vast. By training on a specialized dataset, you can tailor the models to excel in areas that are relevant to your interests or business goals. Customization can lead to more accurate and context-aware responses, improving both the user experience and the success of your NLP projects.

The combination of Gemma models, HuggingFace Transformers, and Ollama provides a formidable set of tools for NLP tasks and is available to run on Mac OS, the next and now Windows. A deep understanding of how to set up these tools, the protocols for accessing them, and their functionalities will enable you to leverage their full capabilities for a variety of innovative and compelling applications. Whether you’re a seasoned NLP practitioner or someone looking to enhance your projects with advanced language models, this guide will help you navigate the complexities of modern NLP technology.

Filed Under: Guides, Top News





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.

Categories
News

Ollama for Windows now available to run LLM’s locally

Ollama for Windows now available to run LLM's locally

Microsoft Windows users who have been patiently waiting to use the fantastic Ollama app that allows you to run large language models (LLMs) on your local machine. Will be pleased to know that the Ollama development team has now released a Windows version. Previously only available on macOS and Linux, Ollama is now available to run on PCs running Windows 10 and above.

Imagine a tool that transforms your Windows 10 computer into a powerhouse of artificial intelligence capabilities. This is Ollama, and it brings the sophisticated world of large language models (LLMs) straight to your desktop. With its release for Windows 10, users can now tap into the same advanced AI features that have been enhancing productivity on other platforms.

The standout feature of Ollama is its GPU acceleration. By utilizing NVIDIA GPUs,, the application can process complex language and vision models at breakneck speeds. This acceleration means that tasks which would typically take longer to compute are now completed in a fraction of the time, allowing you to work more efficiently and effectively.

Ollama now available on Windows

But speed isn’t the only advantage Ollama offers. It comes with a comprehensive library of models that cater to a variety of needs. Whether you’re working with text or images, Ollama has a model that can help. These models are not only powerful but also easy to integrate into your existing workflow. The application’s drag-and-drop functionality makes it simple to use, and its always-on API ensures that it connects seamlessly with the other tools you rely on.

Here are some other articles you may find of interest on the subject of Ollama

Privacy and control are paramount in today’s digital landscape, and Ollama addresses these concerns head-on. The application is compatible with OpenAI-based tools, allowing you to run local models and keep your data secure. This means that you can enjoy the benefits of AI without compromising your privacy or losing control over your information.

Setting up Ollama is straightforward. The installation process is guided by the OllamaSetup.exe installer, which means you can start exploring AI-powered features without any hassle. The developers are committed to improving the application, and regular updates are released to ensure that Ollama continues to meet the evolving needs of its users.

The creators of Ollama understand the importance of community feedback. They encourage users to share their experiences and suggestions, which helps shape the future development of the application. For those seeking support or wishing to connect with like-minded individuals, there’s a dedicated Discord channel where users can engage with each other and the development team.

Ollama for Windows 10 is more than just an application; it’s a comprehensive platform that simplifies the integration of AI into your daily tasks. With features like GPU acceleration, a vast model library, and seamless integration with OpenAI tools, Ollama is tailored to enhance your productivity and expand your capabilities. Its user-friendly design, coupled with a commitment to user-driven development, positions Ollama as a vital tool for anyone interested in leveraging the power of AI on their Windows system.

Filed Under: Technology News, Top News





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.

Categories
News

How to use LocalGPT and Ollama locally for data privacy

How to use LocalGPT and Ollama locally for data privacy

In today’s world, where data breaches are all too common, protecting your personal information is more important than ever. A new solution that combines Ollama with the LocalGPT AI models promises to keep your data safe without sacrificing the power and convenience of advancements in artificial intelligence. This integration allows you to work with your sensitive data on your own devices or within a private cloud, ensuring that your information stays secure.

To get started with this integration, the first thing you need to do is set up LocalGPT on your computer. This involves copying the code from its online repository and creating a separate working space on your computer, known as a virtual environment. This is an important step because it keeps the software needed for LocalGPT away from other programs, avoiding any possible interference.

Once you have your virtual environment, the next step is to install the software packages that LocalGPT needs to run. This is made easy with a simple command that finds and sets up everything you need all at once, saving you time and effort.

Combining Ollama with LocalGPT AI

Ollama is currently available on Mac OS and Linux and its development team currently working on the Windows release that should be made available sometime later this year. Ollama allows you to run a wide variety of different AI models including Meta’s Llama 2,  Mistral, Mixtral, Code Llama and more. You can find a full list of all the AI models currently supported by Ollama here.

Earlier this month the development team made available initial versions of the Ollama Python and JavaScript libraries. Both libraries make it possible to integrate new and existing apps with Ollama in a few lines of code, and share the features and feel of the Ollama REST API.

Here are some other articles you may find of interest on the subject of Ollama and running AI models locally.

After that, you’ll prepare your documents for use with LocalGPT. You’ll run a script that puts your documents into a special kind of database that makes it easy for LocalGPT to search and analyze them. Now it’s time to set up Ollama on your computer. Once you’ve installed it, you’ll pick a language model to use. This model is what allows you to talk to your documents in a natural way, as if you were having a conversation.

The next step is to connect Ollama with LocalGPT. You do this by adding Ollama to the LocalGPT setup and making a small change to the code. This links the two systems so they can work together. Finally, you’re ready to run LocalGPT with the Ollama model. This is the moment when everything comes together, and you can start interacting with your documents in a secure and private way.

But the benefits of this integration don’t stop with individual use. The system gets better when more people get involved. You’re encouraged to add your own improvements to the project and to combine LocalGPT with other tools. This not only makes the system more powerful but also tailors it to meet your specific needs.

Staying up to date with the latest developments is also key. By signing up for updates and joining the online community, you can connect with others who are using the system. This is a great way to get help, share your experiences, and learn from others.

The combination of Ollama and LocalGPT represents a significant step forward in how we can interact with our documents while keeping our data safe. By carefully following the steps to set up and run the integrated system, you can enhance how you work with your data, all the while maintaining strong security. The ongoing support and contributions from the community only add to the strength of this toolset.

Filed Under: Guides, Top News





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.

Categories
News

Using Ollama to run AI on a Raspberry Pi 5 mini PC

Using Ollama to run AI Raspberry Pi 5 PC

Imagine having the power to process human language and interpret images right in the palm of your hand with a Raspberry Pi Ai, without relying on the internet or external cloud services. This is now possible with the Pi 5, a small but mighty computer that can run sophisticated language models using a tool called Ollama. This setup is perfect for those who value privacy, have limited internet access, or are simply fascinated by the potential of compact computing.

The Raspberry Pi 5 comes with 8 GB of RAM, which is quite impressive for its size. This memory capacity allows it to handle large language models (LLMs) such as Tiny Llama and Llama 2. These models are designed to understand and generate human language, making them incredibly useful for a variety of applications. Ollama is the key to unlocking these capabilities on the Raspberry Pi 5. It’s a tool that integrates smoothly with the language models, providing a straightforward interface that makes it easy for users to operate the LLMs on their device.

When you start using these language models on the Raspberry Pi 5, one of the first things you’ll notice is how it performs in comparison to more powerful computers, like a MacBook Pro. While the Raspberry Pi 5 may not have the same level of processing power, it still holds its own, delivering respectable performance at a fraction of the cost. This makes it an attractive option for hobbyists, developers, and anyone interested in exploring the world of language processing without breaking the bank.

Running AI on a Pi 5

Here are some other articles you may find of interest on the subject of Raspberry Pi 5 :

Monitoring the performance of your system is crucial when running LLMs on the Raspberry Pi 5. By keeping an eye on CPU usage and how quickly the system generates responses, you can fine-tune your setup to make the most of the Raspberry Pi’s resources. This not only enhances the functionality of your LLMs but also ensures that your device runs efficiently.

Raspberry Pi Ai using Ollama

One of the most exciting aspects of LLMs is their ability to make sense of images. With the Raspberry Pi 5, you can put this feature to the test. This capability is especially useful for developers who want to create applications that can process visual information without sending data over the internet. Whether you’re working on a project that requires image recognition or you’re simply curious about the possibilities, the Raspberry Pi 5 offers a unique opportunity to experiment with this technology.

But the functionality of the Raspberry Pi 5 and Ollama doesn’t stop at running language models. Ollama also supports API integration, which means you can connect your models to other software systems. This opens the door to more complex applications and uses, allowing you to build sophisticated systems that can interact with various software components.

Open-source LLMs (large language models)

Open-source large language models are a significant area of interest in the field of artificial intelligence. These models are made publicly available, allowing researchers, developers, and enthusiasts to explore, modify, and utilize them for various applications. The open-source nature fosters a collaborative environment, accelerates innovation, and democratizes access to advanced AI technologies.

  • GPT-Neo and GPT-NeoX: Developed by EleutherAI, these models are direct responses to OpenAI’s GPT-3. They aim to replicate the architecture and capabilities of GPT-3, offering a similar autoregressive model for natural language processing tasks. GPT-Neo and GPT-NeoX are part of an ongoing effort to create scalable, open-source alternatives to proprietary models.
  • GPT-J: Also from EleutherAI, GPT-J is an advancement over GPT-Neo, featuring a 6-billion parameter model. It’s known for its impressive performance in various language tasks, striking a balance between size and computational requirements.
  • BERT and its Variants (RoBERTa, ALBERT, etc.): While not exactly like GPT models, BERT (Bidirectional Encoder Representations from Transformers) and its variants, developed by Google, are pivotal in the NLP landscape. They are designed for understanding the context of a word in a sentence, offering strong performance in tasks like question answering and language inference.
  • T5 (Text-To-Text Transfer Transformer): Also from Google, T5 reframes all NLP tasks as a text-to-text problem. It’s a versatile model that can be applied to various tasks without task-specific architecture modifications.
  • Fairseq: This is a sequence modeling toolkit from Facebook AI Research (FAIR) that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks.
  • XLNet: Developed by Google and Carnegie Mellon University, XLNet is an extension of the Transformer model, outperforming BERT in several benchmarks. It uses a permutation-based training approach, which is different from the traditional autoregressive or autoencoding methods.
  • BlenderBot: From Facebook AI, BlenderBot is an open-source chatbot model known for its engaging conversational abilities. It’s designed to improve the relevance, informativeness, and empathy of responses in a dialogue system.

Each of these models has unique characteristics, strengths, and limitations. Their open-source nature not only facilitates broader access to advanced AI technologies but also encourages transparency and ethical considerations in AI development and deployment. When utilizing these models, it’s crucial to consider aspects like computational requirements, the nature of the task at hand, and the ethical implications of deploying AI in real-world scenarios. For many more open source large language models jump over to the Hugging Face website.

The combination of the Raspberry Pi 5 and the Ollama tool provides a powerful platform for anyone interested in running open-source LLMs locally. Whether you’re a developer looking to push the boundaries of what’s possible with compact computing or a hobbyist eager to dive into the world of language processing, this setup offers a wealth of opportunities. With the ability to manage system resources effectively, interpret images, and integrate with APIs, the Raspberry Pi 5 and Ollama invite you to explore the full potential of local language models. Embrace this versatile technology and unlock a world of creative possibilities.

Filed Under: Guides, Top News





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.

Categories
News

Easily install custom AI Models locally with Ollama

Easily install custom AI Models locally with Ollama

If you are just getting started with large language models and would like to easily install different AI models currently available you should deftly check out Ollama. It’s really easy-to-use and takes just a few minutes to install and set up your first large language model. One word of warning is that your computer will need at least 8GB RAM and as much as you can spare for some models, as LLMs use large amounts of memory for each request.

Ollama currently supports easy installation of a wide variety of AI models including : llama 2, llama 2-uncensored, codellama, codeup, everythinglm, falcon, llama2-chinese, mistral, mistral-openorca, samantha-mistral, stable-beluga, wizardcoder and more. however you can also install custom AI models locally with Ollama as well.

Installing custom AI models locally with Ollama

Ollama is an AI model management tool that allows users to easily install and use custom models. One of the key benefits of Ollama is its versatility. While it comes pre-loaded with a variety of models, it also allows users to install custom models that are not available in the Ollama library. This opens up a world of possibilities for developers and researchers to experiment with different models and fine-tunes.

Other articles we have written that you may find of interest on the subject of Ollama :

One such custom model that can be installed in Ollama is Jackalope. Jackalope is a 7B model, a fine-tuning of the Mistral 7B model. It is recommended to get the quantized version of the model, specifically in GGUF format. Formerly known as GGML, GGUF is a quantized version of models used by the project LLaMA CPP, which Ollama uses for models.

The process of installing Jackalope, or any other custom model in Ollama, starts with downloading the model and placing it in a model’s folder for processing. Once the model is downloaded, the next step is to create a model file. This file includes parameters and points to the downloaded file. It also includes a template for a system prompt that users can fill out when running the model.

After creating and saving the model file, the process of creating a model using the model file begins. This process includes passing the model file, creating various layers, writing the weights, and finally, seeing a success message. Once the process is complete, the new model, in this case, Jackalope, can be seen in the model list and run just like any other model.

While Ollama offers a significant degree of flexibility in terms of the models it can handle, it’s important to note that some models may not work. However, fine-tunes of LLaMA2, Mistral 7B, and Falcon models should work. This limitation, while it may seem restrictive, still allows users to try out a vast array of different models from the hugging face hub.

Ollama provides a user-friendly platform for installing and using custom AI models. The process, while it may seem complex at first glance, is straightforward and allows users to experiment with a variety of models. Whether it’s the Jackalope model or any other custom model, the possibilities are vast with Ollama. However, users should be aware of potential limitations with some models and ensure they are using compatible models for optimal performance.

Filed Under: Guides, Top News





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.

Categories
News

How to install Ollama LLM locally to run Llama 2, Code Llama

How to install Ollama locally to run Llama 2 and other LLm models

Large language models (LLMs) have become a cornerstone for various applications, from text generation to code completion. However, running these models locally can be a daunting task, especially for those who are not well-versed in the technicalities of AI.  This is where Ollama comes into play.

Ollama is a user-friendly tool designed to run large language models locally on a computer, making it easier for users to leverage the power of LLMs. This article will provide a comprehensive guide on how to install and use Ollama to run Llama 2, Code Llama, and other LLM models.

Ollama is a tool that supports a variety of AI models including LLaMA-2, uncensored LLaMA, CodeLLaMA, Falcon, Mistral, Vicuna model, WizardCoder, and Wizard uncensored. It is currently compatible with MacOS and Linux, with Windows support expected to be available soon. Ollama operates through the command line on a Mac or Linux machine, making it a versatile tool for those comfortable with terminal-based operations.

Easily install and use Ollama locally

One of the unique features of Ollama is its support for importing GGUF and GGML file formats in the Modelfile. This means if you have a model that is not in the Ollama library, you can create it, iterate on it, and upload it to the Ollama library to share with others when you are ready.

 

 

Installation and Setup of Ollama

To use Ollama, users first need to download it from the official website. After downloading, the installation process is straightforward and similar to other software installations. Once installed, Ollama creates an API where it serves the model, allowing users to interact with the model directly from their local machine.

Downloading and Running Models Using Ollama

Running models using Ollama is a simple process. Users can download and run models using the ‘run’ command in the terminal. If the model is not installed, Ollama will automatically download it first. This feature saves users from the hassle of manually downloading and installing models, making the process more streamlined and user-friendly.

Creating Custom Prompts with Ollama

Ollama also allows users to create custom prompts, adding a layer of personalization to the models. For instance, a user can create a model called ‘Hogwarts’ with a system prompt set to answer as Professor Dumbledore from Harry Potter. This feature opens up a world of possibilities for users to customize their models according to their specific needs and preferences.

Removing Models from Ollama

Just as adding models is easy with Ollama, removing them is equally straightforward. Users can remove models using the ‘remove’ command in the terminal. This feature ensures that users can manage their models efficiently, keeping their local environment clean and organized.

Ollama is a powerful tool that simplifies the process of running large language models locally. Whether you want to run Llama 2, Code Llama, or any other LLM model, Ollama provides a user-friendly platform to do so. With its support for custom prompts and easy model management, Ollama is set to become a go-to tool for AI enthusiasts and professionals alike. As we await the Windows version, Mac and Linux users can start exploring the world of large language models with Ollama.

Filed Under: Guides, Top News





Latest timeswonderful Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, timeswonderful may earn an affiliate commission. Learn about our Disclosure Policy.