Tag: large

Categories
News

Analyse large documents locally using AI securely and privately

Analyse large documents locally securely and privately using PrivateGPT and LocalGPT

If you have large business documents that you would like to analyze, quickly and efficiently without having to read every word. You can harness the power of artificial intelligence to answer questions about these documents locally on your personal laptop. Using PrivateGPT and LocalGPT you can securely and privately, quickly summarize, analyze and research large documents. By simply asking questions to extracting certain data that you might need for other uses, efficiently and effectively thanks to the power of GPT AI models.

Dealing with large volumes of digital documents is a common yet daunting task for most of us in business. But what if you could streamline this process, making it quicker, more efficient, secure and private? Using AI tools such as PrivateGPT and LocalGPT this is now possible transforming the way we interact with our documents locally making sure that no personal or private data centre third-party servers such as OpenAI, Bing, Google or others.

Using PrivateGPT and LocalGPT, you can now tap into the power of artificial intelligence right from your personal laptop. These tools allow you to summarize, analyze, and research extensive documents with ease. They are not just time-savers; they are smart, intuitive assistants ready to sift through pages of data to find exactly what you need.

  • Efficiency at Your Fingertips: Imagine having the ability to quickly scan through lengthy business reports or research papers and extract the essential information. With PrivateGPT and LocalGPT, this becomes a reality. They can summarize key points, highlight crucial data, and even provide analysis – all in a fraction of the time it would take to do manually.
  • Local and Private: One of the defining features of these tools is their focus on privacy. Since they operate locally on your device, you don’t have to worry about sensitive information being transmitted over the internet. This local functionality ensures that your data remains secure and private, giving you peace of mind.
  • User-Friendly Interaction: These tools are designed with the user in mind. They are intuitive and easy to use, making them accessible to anyone, regardless of their technical expertise. Whether you’re a seasoned tech professional or a business person with minimal tech knowledge, you’ll find these tools straightforward and practical.
  • Versatility in Application: Whether you’re looking to extract specific data for a presentation, find answers to complex questions within a document, or simply get a quick overview of a lengthy report, PrivateGPT and LocalGPT are up to the task. Their versatility makes them valuable across various industries and applications.
  • Simplified Document Handling: Gone are the days of poring over pages of text. These tools help you navigate through extensive content, making document handling a breeze. They are especially useful in scenarios where time is of the essence, and accuracy cannot be compromised.

How to analyze large documents securely & privately using AI

If you are wondering how these tools could fit into your workflow, you will be pleased to know that they are adaptable and can be tailored to meet your specific needs. Whether you are a legal professional dealing with case files, a researcher analyzing scientific papers, or a business analyst sifting through market reports, PrivateGPT and LocalGPT can be your allies in managing and understanding complex documents.

Other articles we have written that you may find of interest on the subject of running AI models locally for privacy and security :

PrivateGPT vs LocalGPT

For more information on how to use PrivateGPT and to download the open source AI model jump over to its official GitHub repository.

PrivateGPT

“PrivateGPT is a production-ready AI project that allows you to ask questions about your documents using the power of Large Language Models (LLMs), even in scenarios without an Internet connection. 100% private, no data leaves your execution environment at any point.”

  • Concept and Architecture:
    • PrivateGPT is an API that encapsulates a Retrieval-Augmented Generation (RAG) pipeline.
    • It is built using FastAPI and follows OpenAI’s API scheme.
    • The RAG pipeline is based on LlamaIndex, which provides abstractions such as LLM, BaseEmbedding, or VectorStore.
  • Key Features:
    • It offers the ability to interact with documents using GPT’s capabilities, ensuring privacy and avoiding data leaks.
    • The design allows for easy extension and adaptation of both the API and the RAG implementation.
    • Key architectural decisions include dependency injection, usage of LlamaIndex abstractions, simplicity, and providing a full implementation of the API and RAG pipeline​​​​.

LocalGPT

For more information on how to use LocalGPT and to download the open source AI model jump over to its official GitHub repository.

“LocalGPT is an open-source initiative that allows you to converse with your documents without compromising your privacy. With everything running locally, you can be assured that no data ever leaves your computer. Dive into the world of secure, local document interactions with LocalGPT.”

  • Utmost Privacy: Your data remains on your computer, ensuring 100% security.
  • Versatile Model Support: Seamlessly integrate a variety of open-source models, including HF, GPTQ, GGML, and GGUF.
  • Diverse Embeddings: Choose from a range of open-source embeddings.
  • Reuse Your LLM: Once downloaded, reuse your LLM without the need for repeated downloads.
  • Chat History: Remembers your previous conversations (in a session).
  • API: LocalGPT has an API that you can use for building RAG Applications.
  • Graphical Interface: LocalGPT comes with two GUIs, one uses the API and the other is standalone (based on streamlit).
  • GPU, CPU & MPS Support: Supports multiple platforms out of the box, Chat with your data using CUDA, CPU or MPS and more!
  • Concept and Features:
    • LocalGPT is an open-source initiative for conversing with documents on a local device using GPT models.
    • It ensures privacy as no data ever leaves the device.
    • Features include utmost privacy, versatile model support, diverse embeddings, and the ability to reuse LLMs.
    • LocalGPT includes chat history, an API for building RAG applications, two GUIs, and supports GPU, CPU, and MPS​​.
  • Technical Details:
    • LocalGPT runs the entire RAG pipeline locally using LangChain, ensuring reasonable performance without data leaving the environment.
    • ingest.py uses LangChain tools to parse documents and create embeddings locally, storing the results in a local vector database.
    • run_localGPT.py uses a local LLM to process questions and generate answers, with the ability to replace this LLM with any other LLM from HuggingFace, as long as it’s in the HF format​​.

PrivateGPT and LocalGPT both emphasize the importance of privacy and local data processing, catering to users who need to leverage the capabilities of GPT models without compromising data security. This aspect is crucial, as it ensures that sensitive data remains within the user’s own environment, with no transmission over the internet. This local processing approach is a key feature for anyone concerned about maintaining the confidentiality of their documents.

In terms of their architecture, PrivateGPT is designed for easy extension and adaptability. It incorporates techniques like dependency injection and uses specific LlamaIndex abstractions, making it a flexible tool for those looking to customize their GPT experience. On the other hand, LocalGPT offers a user-friendly approach with diverse embeddings, support for a variety of models, and a graphical user interface. This range of features broadens LocalGPT’s appeal, making it suitable for various applications and accessible to users who prioritize ease of use along with flexibility.

The technical approaches of PrivateGPT and LocalGPT also differ. PrivateGPT focuses on providing an API that wraps a Retrieval-Augmented Generation (RAG) pipeline, emphasizing simplicity and the capacity for immediate implementation modifications. Conversely, LocalGPT provides a more extensive range of features, including chat history, an API for RAG applications, and compatibility with multiple platforms. This makes LocalGPT a more comprehensive option for those with a broader spectrum of technical requirements.

Both tools are designed for users who interact with large documents and seek a secure, private environment. However, LocalGPT’s additional features, such as its user interface and model versatility, may make it more appealing to a wider range of users, especially those with varied technical needs. It offers a more complete solution for individuals seeking not just privacy and security in document processing, but also convenience and extensive functionality.

While both PrivateGPT and LocalGPT share the core concept of private, local document interaction using GPT models, they differ in their architectural approach, range of features, and technical details, catering to slightly different user needs and preferences in document handling and AI interaction.

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

ELEGOO OrangeStorm Giga large volume 3D printer

ELEGOO OrangeStorm Giga large volume 3D printer

If you are searching for a truly massive yet affordable 3D printer you might be interested in the new ELEGOO OrangeStorm Giga, offering a truly massive 800mm x 800mm x 1000 mm print volume and printing speeds of up to 300mm/s as well as multicolour printing. Launched via Kickstarter this month the project has already raised £2.5 million with still 54 days remaining thanks to over 1800 backers.

The ELEGOO OrangeStorm Giga is a high-capacity FDM 3D printer, designed to inspire creativity and innovation. It’s a testament to the significant advancements in 3D printing technology, boasting features that greatly improve its performance, efficiency, and ease of use, making it an invaluable tool for both hobbyists and professionals. With a build volume of 800mm x 800mm x 1000mm, it reduces the need to divide models into smaller parts for printing. This is especially beneficial for users who need large-scale prints, as it allows for the creation of larger, more complex models in one print run, saving time and resources.

Early bird backing offers are now available for the innovational project from roughly $1445 or £1227 (depending on current exchange rates), offering a considerable discount of approximately 40% off the retail market price, while the Kickstarter crowd funding is under way.

ELEGOO OrangeStorm Giga features

The printer also features an efficient heated bed, composed of four independent high-temperature platforms. These can be heated either all at once or individually, providing flexibility and efficiency in the printing process. This is particularly useful for printing materials that require different temperature settings, allowing for a more versatile and adaptable printing experience.

Large volume 3D printer

The OrangeStorm Giga is powered by a 64-bit 1.5G clock speed quad-core high-performance processor. This powerful processor enables a printing speed that is six times faster than standard 3D printers, significantly reducing the time it takes to print large, complex models, and thereby enhancing productivity. In addition to its impressive printing speed, the printer also offers multi-nozzle printing. This allows for the addition of three extra printheads for simultaneous printing, further enhancing the printer’s efficiency and productivity, and enabling the creation of more complex designs in less time.

3D printer size comparison

The printer’s cooling system includes a robust cooling fan with an intelligent control function that automatically stops once the printing process is complete. This not only saves energy but also extends the lifespan of the fan, making the printer more sustainable and cost-effective over time. The OrangeStorm Giga is built with high-quality craftsmanship. It features an integrated body with linear guides on the X and Y axes and an upgraded Z-axis rod. This design ensures stability and precision during the printing process, resulting in high-quality prints every time.

If the ELEGOO OrangeStorm Giga campaign successfully raises its required pledge goal and the project completion progresses smoothly, worldwide shipping is expected to take place sometime around August 2024. To learn more about the ELEGOO OrangeStorm Giga large volume 3D printer project preview the promotional video below.

ELEGOO OrangeStorm Giga

The printer also features an upgraded 300℃ high-temperature nozzle with a proximal double-gear extrusion structure and a full titanium alloy heat pipe. This design ensures consistent and precise extrusion, even at high temperatures, thereby enhancing the quality of the prints. Cable management is simplified with the printer’s caterpillar cable tracks. These tracks neatly store cables, prevent wear and tear, and ensure system stability. The printer also features a portable 7-inch HD capacitive screen that supports ELEGOO Cura model preview and language switching, making it user-friendly and versatile.

The OrangeStorm Giga also includes filament detection and power loss recovery features. These features allow for uninterrupted printing, ensuring that your print jobs are not disrupted by filament shortages or power outages, thereby enhancing the reliability of the printer. The printer also includes a user-friendly belt knob for easy and accurate control of belt tightness. This feature ensures that the printer’s belts are always at the optimal tension, ensuring precise and consistent prints. The printer’s large spool holder can accommodate filament spools up to 5kg, reducing the need for frequent filament changes and enhancing the printer’s efficiency.

ELEGOO OrangeStorm Giga design

Finally, the printer features an auto-leveling feature with a non-contact high-precision sensor for automatic data collection. This feature ensures that the printer’s bed is always perfectly level, resulting in high-quality, consistent prints. The ELEGOO OrangeStorm Giga is a high-capacity FDM 3D printer that offers a range of features designed to enhance its performance, efficiency, and user-friendliness. Whether you’re a hobbyist or a professional, this printer is sure to meet your 3D printing needs, making it a valuable addition to any workspace.

For a complete list of all available project pledges, stretch goals, extra media and technical attributes for the large volume 3D printer, jump over to the official ELEGOO OrangeStorm Giga crowd funding campaign page by clicking the link below.

Source : Kickstarter

Disclaimer: Participating in Kickstarter campaigns involves inherent risks. While many projects successfully meet their goals, others may fail to deliver due to numerous challenges. Always conduct thorough research and exercise caution when pledging your hard-earned money.

Filed Under: Design 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 Meta created Llama 2 large language model (LLM)

How Meta created Llama 2

The development and evolution of language models have been a significant area of interest in the field of artificial intelligence. One such AI model that has garnered attention is Llama 2, an updated version of the original Llama model. Meta the development team behind Llama 2 has made significant strides in improving the model’s capabilities, with a focus on open-source tooling and community feedback. This guide provides more details on how Meta created Llama 2 delves into the development, features, and potential applications of Llama 2, providing an in-depth look at the advancements in large language models. Thanks to a presentation by Angela Fan a research scientist at Meta AI Research Paris who focuses on machine translation.

Llama 2 was developed with the feedback and encouragement from the community. The team behind the model has been transparent about the development process, emphasizing the importance of open-source tools. This approach has allowed for a more collaborative and inclusive development process, fostering a sense of community around the project.

How Meta developed Llama 2

The architecture of Llama 2 is similar to the original, using a standard Transformer-based architecture. However, the new model comes in three different parameter sizes: 7 billion, 13 billion, and 70 billion parameters. The 70 billion parameter model offers the highest quality, but the 7 billion parameter model is the fastest and smallest, making it popular for practical applications. This flexibility in parameter sizes allows for a more tailored approach to different use cases.

The pre-training data set for Llama 2 uses two trillion tokens of text found on the internet, predominantly in English, compared to 1.4 trillion in Llama 1. This increase in data set size has allowed for a more comprehensive and diverse range of language patterns and structures to be incorporated into the model. The context length in Llama 2 has also been expanded to around 4,000 tokens, up from 2,000 in Llama 1, enhancing the model’s ability to handle longer and more complex conversations.

Other articles you may find of interest on the subject of  Llama 2 :

Training Llama 2

The training process for Llama 2 involves three core steps: pre-training, fine-tuning to make it a chat model, and a human feedback loop to produce different reward models for helpfulness and harmlessness. The team found that high-quality data set annotation was crucial for achieving high-quality supervised fine-tuning examples. They also used rejection sampling and proximal policy optimization techniques for reinforcement learning with human feedback. This iterative improvement process showed a linear improvement in both safety and helpfulness metrics, indicating that it’s possible to improve both aspects simultaneously.

The team behind Llama 2 also conducted both automatic and human evaluations, with around 4,000 different prompts evaluated for helpfulness and 2,000 for harmlessness. However, they acknowledged that human evaluation can be subjective, especially when there are many possible valuable responses to a prompt. They also highlighted that the distribution of prompts used for evaluation can heavily affect the quality of the evaluation, as people care about a wide variety of topics.

AI models

Llama 2 has been introduced as a competitive model that performs significantly better than open-source models like Falcon or Llama 1, and is quite competitive with models like GPT 3.5 or Palm. The team also discussed the concept of “temporal perception”, where the model is given a cut-off date for its knowledge and is then asked questions about events after that date. This feature allows the model to provide more accurate and contextually relevant responses.

Despite the advancements made with Llama 2, the team acknowledges that there are still many open questions to be resolved in the field. These include issues around the hallucination behavior of models, the need for models to be more factual and precise, and questions about scalability and the types of data used. They also discussed the use of Llama 2 as a judge in evaluating the performance of other models, and the challenges of using the model to evaluate itself.

Fine tuning

The team also mentioned that they have not released their supervised fine-tuning dataset, and that the model’s access to APIs is simulated rather than real. They noted that the model’s tool usage is not particularly robust and that more work needs to be done in this area. However, they also discussed the potential use of language models as writing assistants, suggesting that the fine-tuning strategy and data domain should be adjusted depending on the intended use of the model.

Llama 2 represents a significant step forward in the development of large language models. Its improved capabilities, coupled with the team’s commitment to open-source tooling and community feedback, make it a promising tool for a variety of applications. However, as with any technology, it is important to continue refining and improving the model, addressing the challenges and open questions that remain. The future of large language models like Llama 2 is bright, and it will be exciting to see how they continue to evolve and shape the field of artificial intelligence.

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.