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Integrating AI large language models LLMs with Knowledge Graphs

Integrating LLMs with Knowledge Graphs

In the exciting world of artificial intelligence (AI), two standout technologies are making waves: Large Language Models (LLMs) like GPT-3 and Knowledge Graphs. These tools are transforming how we handle and analyze data, leading to smarter decision-making processes. This article will take you on a journey through the practical steps of combining LLMs with Knowledge Graphs, exploring the benefits and tackling the challenges that come with this integration.

What are Knowledge Graphs?

Knowledge graphs are sophisticated databases designed to store and organize information in a way that illustrates the relationships and connections between various concepts and entities. They represent data in a network of interconnected nodes and edges, where nodes symbolize entities such as people, places, and objects, and edges denote the relationships between them.

This structure enables machines and humans alike to understand complex associations and contextual nuances within the data. Knowledge graphs are pivotal in enhancing AI capabilities, particularly in areas like semantic search, data analysis, and natural language processing, by providing a rich, contextual framework for understanding and utilizing information.

LLMs are advanced AI systems that have the ability to understand and generate human-like text. They work by predicting what word comes next in a sentence, learning from vast amounts of data. Knowledge Graphs, on the other hand, are databases that organize information about concepts and the connections between them in a way that both people and machines can understand.

When you bring LLMs and Knowledge Graphs together, they enhance each other’s capabilities. LLMs can use the structured information in Knowledge Graphs to add context to their interpretations, while Knowledge Graphs benefit from LLMs’ nuanced understanding of language. This synergy can lead to AI responses that are not only more accurate but also more relevant to the context, whether it’s for a search engine or a digital assistant.

Knowledge Graphs quick reference guide

  • Definition and Purpose:
    • Organize and represent knowledge in a structured format.
    • Facilitate understanding of relationships and connections between different concepts and entities.
  • Benefits:
    • Enhances data interoperability and integration.
    • Improves the efficiency and accuracy of data retrieval.
    • Enables more sophisticated, context-aware AI applications.
    • Supports semantic search and advanced analytics.
    • Aids in uncovering insights from complex and large datasets.
  • Applications:
    • Enhancing search engine capabilities with contextual understanding.
    • Powering recommendation systems in e-commerce and streaming services.
    • Improving natural language processing and understanding in AI systems.
    • Enabling advanced data analytics in various fields like healthcare, finance, and customer service.
  • Challenges:
    • Requires high-quality, consistent, and up-to-date data.
    • Managing and processing large volumes of data can be complex and resource-intensive.
    • Ensuring data accuracy and minimizing bias in the knowledge representation.
  • Future Potential:
    • Continues to evolve with advancements in AI and machine learning.
    • Holds immense promise for creating more intelligent, responsive, and personalized AI applications.
    • Expected to play a key role in the development of more advanced AI systems.

Consider a healthcare AI that merges the text analysis prowess of LLMs with a Knowledge Graph that maps out the relationships between diseases, symptoms, and treatments. Such an AI could provide deeper medical insights or help diagnose conditions based on the symptoms patients report. In the realm of customer service, an AI chatbot powered by an LLM can have natural conversations with customers. If this chatbot is also linked to a Knowledge Graph that contains detailed information about the company’s products or services, it can offer precise and helpful information, greatly improving the customer’s experience.

However, integrating LLMs with Knowledge Graphs is not without its hurdles. One major challenge is ensuring that the data is of high quality and consistent. Both systems need to work with data that is accurate, up-to-date, and free from bias to avoid mistakes in the AI’s output.

Data accuracy is paramount

As the amount of data grows, the integrated system must also be able to process and analyze this information both efficiently and cost-effectively. This requires sophisticated algorithms and a strong infrastructure that can manage heavy workloads. To keep data accurate and reliable, it’s crucial to have strict processes for validating and cleaning the data. Automated tools can help identify and fix errors, and regular updates are necessary to keep the Knowledge Graph current and precise.

When it comes to dealing with the scale and efficiency of the system, developers can use distributed computing. This approach allows the system to adjust its processing power based on the current needs. Using cloud-based platforms can provide the flexibility needed to scale up or down depending on demand. Additionally, optimizing the algorithms that combine LLMs with Knowledge Graphs can reduce the computational load, making the system more efficient.

The combination of LLMs and Knowledge Graphs holds immense promise for enhancing AI applications in various industries. By understanding how these technologies work together and addressing the technical challenges of data quality, scalability, and efficiency, we can create AI systems that are not only powerful but also reliable and cost-effective. As we continue to explore this integration, we can expect to see a surge of innovative AI solutions that push the boundaries of what AI can achieve.

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How to build knowledge graphs with large language models (LLMs)

How to build knowledge graphs with large language models (LLMs)

If you are interested in learning how to build knowledge graphs using artificial intelligence and specifically large language models (LLM). Johannes Jolkkonen has created a fantastic tutorial that shows you how to used Python to create an environment with the necessary data and setting up credentials for the OpenAI API and Neo4j database.

Wouldn’t it be fantastic if you could collate your vast amounts of information and interconnect it in a web of knowledge, where every piece of data is linked to another, creating a map that helps you understand complex relationships and extract meaningful insights. This is the power of a knowledge graph, and it’s within your reach by combining the strengths of graph databases and advanced language models. Let’s explore how these two technologies can work together to transform the way we handle and analyze data.

Graph databases, like Neo4j, excel in managing data that’s all about connections. They store information as entities and the links between them, making it easier to see how everything is related. To start building your knowledge graph, set up a Neo4j database. It will be the backbone of your project. You’ll use the Cypher query language to add, change, and find complex network data. Cypher is great for dealing with complicated data structures, making it a perfect match for graph databases.

How to build knowledge graphs with LLMs

Here are some other articles you may find of interest on the subject of large language models :

Building knowledge graphs

Now, let’s talk about the role of advanced language models, such as those developed by OpenAI, including the GPT series. These models have changed the game when it comes to understanding text. They can go through large amounts of unstructured text, like documents and emails, and identify the key entities and their relationships. This step is crucial for adding rich, contextual information to your knowledge graph.

When you’re ready to build your knowledge graph, you’ll need to extract entities and relationships from your data sources. This is where Python comes in handy. Use Python to connect to the OpenAI API, which gives you access to the powerful capabilities of GPT models for pulling out meaningful data. This process is essential for turning plain text into a structured format that fits into your graph database.

The foundation of a knowledge graph is the accurate identification of entities and their connections. Use natural language processing (NLP) techniques to analyze your data. This goes beyond just spotting names and terms; it’s about understanding the context in which they’re used. This understanding is key to accurately mapping out your data network.

Things to consider

When building a knowledge graph it’s important to consider:

  • Data Quality and Consistency: Ensuring accuracy and consistency in the data is crucial for the reliability of a knowledge graph.
  • Scalability: As data volume grows, the knowledge graph must efficiently scale without losing performance.
  • Integration of Diverse Data Sources: Knowledge graphs often combine data from various sources, requiring effective integration techniques.
  • Updating and Maintenance: Regular updates and maintenance are necessary to keep the knowledge graph current and relevant.
  • Privacy and Security: Handling sensitive information securely and in compliance with privacy laws is a significant consideration.

Adding a user interface

A user-friendly chat interface can make your knowledge graph even more accessible. Add a chatbot to let users ask questions in natural language, making it easier for them to find the information they need. This approach opens up your data to users with different levels of technical skill, allowing everyone to gain insights.

Working with APIs, especially the OpenAI API, is a critical part of this process. You’ll need to handle API requests smoothly and deal with rate limits to keep your data flowing without interruption. Python libraries are very helpful here, providing tools to automate these interactions and keep your data pipeline running smoothly.

Begin your data pipeline with data extraction. Write Python scripts to pull data from various sources and pass it through the GPT model to identify entities and relationships. After you’ve extracted the data, turn it into Cypher commands and run them in your Neo4j database. This enriches your knowledge graph with new information.

Benefits of knowledge graphs

  • Enhanced Data Interconnectivity: Knowledge graphs link related data points, revealing relationships and dependencies not immediately apparent in traditional databases.
  • Improved Data Retrieval and Analysis: By structuring data in a more contextual manner, knowledge graphs facilitate more sophisticated queries and analyses.
  • Better Decision Making: The interconnected nature of knowledge graphs provides a comprehensive view, aiding in more informed decision-making.
  • Facilitates AI and Machine Learning Applications: Knowledge graphs provide structured, relational data that can significantly enhance AI and machine learning models.
  • Personalization and Recommendation Systems: They are particularly effective in powering recommendation engines and personalizing user experiences by understanding user preferences and behavior patterns.
  • Semantic Search Enhancement: Knowledge graphs improve search functionalities by understanding the context and relationships between terms and concepts.
  • Data Visualization: They enable more complex and informative data visualizations, illustrating connections between data points.

API rate limits and costs

Handling API rate limits can be tricky. You’ll need strategies to work within these limits to make sure your data extraction and processing stay on track. Your Python skills will come into play as you write code that manages these restrictions effectively.

Don’t forget to consider the costs of using GPT models. Do a cost analysis to understand the financial impact of using these powerful AI tools in your data processing. This will help you make smart choices as you expand your knowledge graph project.

By bringing together graph databases and advanced language models, you’re creating a system that not only organizes and visualizes data but also makes it accessible through a conversational interface. Stay tuned for our next article, where we’ll dive into developing a user interface and improving chat interactions for your graph database. This is just the beginning of your journey into the interconnected world of knowledge graphs.

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Benefits of open source vs proprietary (LLMs)

Benefits of using an open source large language models LLM

With the growing number of large language models (LLMs) available on Huggingface, focusing on the distinctions between proprietary and open source models is critical for AI enthusiasts and businesses to understand.

Proprietary LLMs are owned by companies with usage restrictions, while open source LLMs are freely accessible for use and modification. Despite often being smaller in parameter size, open source LLMs are challenging the proprietary model with several benefits.

When you dive into the world of LLMs, you’ll quickly notice a key split: the choice between proprietary and open source models. Proprietary LLMs, like IBM’s Granite Language Model, are developed by private companies and come with certain restrictions on how they can be used. Their inner workings are often kept under wraps, known only to the company that created them. On the flip side, open source LLMs, such as the Bloom model by BigScience, are a testament to the power of community collaboration. These models are freely available for anyone to use, modify, and distribute, without the constraints of proprietary licenses.

“BLOOM is an autoregressive Large Language Model (LLM), trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans. BLOOM can also be instructed to perform text tasks it hasn’t been explicitly trained for, by casting them as text generation tasks.”

Open Source vs Proprietary LLMs

The allure of open source LLMs is undeniable, and their impact on the AI field is significant. One of the standout features of these models is their transparency. This openness builds trust and allows users to understand how the AI operates. But it’s not just about trust; this transparency has tangible benefits. It enables users to tailor models to specific tasks or to support underrepresented languages, making them more valuable in specialized markets.

Proprietary Large Language Models

Pros:

  1. Quality Control and Consistency: Proprietary models often have robust quality control, ensuring consistent performance and reliability.
  2. Support and Maintenance: These models typically come with dedicated support and regular updates from the owning company.
  3. Customization for Specific Applications: They may offer specialized features or customizations for specific industries or use-cases.
  4. Data Security and Privacy: Proprietary models can provide more controlled environments, potentially offering better data security and privacy compliance.

Cons:

  1. Cost and Accessibility: Access to these models often comes at a cost, which can be prohibitive for individual users or small organizations.
  2. Usage Restrictions: There are often strict usage restrictions, limiting the scope of how and where the model can be used.
  3. Lack of Transparency: The internal workings and training data of these models are typically not disclosed, leading to potential biases and ethical concerns.
  4. Dependency on a Single Provider: Users become dependent on the provider for updates, support, and continued access.

Open Source Large Language Models

Pros:

  1. Accessibility and Cost: Open-source models are freely accessible, making them available to a wider audience, including researchers, small businesses, and hobbyists.
  2. Transparency and Auditability: The open nature allows for examination and auditing of the code and algorithms, fostering trust and understanding.
  3. Community Development: They benefit from community contributions, leading to diverse inputs and rapid innovation.
  4. Flexibility in Usage: Users have the freedom to modify and use the models as per their requirements, encouraging experimentation and customization.

Cons:

  1. Quality and Reliability Variability: Open-source models may lack the consistent quality control of proprietary models.
  2. Limited Support: They often come with limited or no formal support structure, relying on community forums or documentation.
  3. Resource Intensity: Deploying and maintaining these models can require significant computational resources and expertise.
  4. Potential for Misuse: The lack of usage restrictions can lead to ethical concerns, as there is less control over how the model is used.

The success of open source projects hinges on the collective wisdom and innovation of contributors from around the globe. This shared intelligence drives rapid progress and adds to the strength and variety of the technology. In some cases, these community-driven efforts can even surpass the innovation of proprietary models, which often boast larger parameter sizes but may lack the same level of collaboration.

Open source LLMs are making waves across various industries, proving to be a boon for progress and efficiency. Take NASA, for instance, which uses these models to analyze vast amounts of textual data. Or consider the healthcare sector, where open source LLMs help professionals extract insights from medical literature and patient interactions. The versatility of these models makes them an invaluable asset for a wide array of organizational needs.

Among the standout open source LLMs are Llama 2 by Meta AI and Vicuna, which demonstrate that open source solutions can hold their own against proprietary models, even those with more substantial resources. However, LLMs are not without their challenges. Issues such as output errors, biases in training data, and security vulnerabilities are real concerns that need to be addressed. These challenges underscore the importance of ongoing research and development to minimize potential negative impacts and promote the responsible use of LLMs.

IBM Watsonx supports all LLMs

IBM has recognized the importance of the open source movement by backing platforms like Watsonx Studio. This platform supports the release and management of both proprietary and open source models, reflecting a broader trend in the industry towards embracing open source AI development. This shift acknowledges the value that community-driven innovation brings to the table.

The open source LLM scene is dynamic and constantly changing. As you delve into this area, you’ll see that the collaborative spirit of open source development is not just an idealistic notion but a practical approach to creating AI technologies that are more effective, transparent, and inclusive. Whether you’re a developer, a business leader, or an AI enthusiast, understanding the nuances of proprietary versus open source LLMs is crucial for tapping into the immense possibilities these tools present.

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LLaMA Factory lets you easily find tune and train LLMs

Easily fine tune and train large language models

If you are looking for ways to easily fine-tune and train large language models (LLMs) you might be interested in a new project called LLaMA Factory. that incorporates the LLaMA Board a one-stop online web user interface method for training and refining large language models. Fine-tuning large language models (LLMs) is a critical step in enhancing their effectiveness and applicability across various domains.

Initially, LLMs are trained on vast, general datasets, which gives them a broad understanding of language and knowledge. However, this generalist approach may not always align with the specific needs of certain domains or tasks. That’s where fine-tuning comes into play. One of the primary reasons for fine-tuning LLMs is to tailor them to specific applications or subject matter.

For instance, models trained on general data might not perform optimally in specialized fields such as medicine, law, or technical subjects. Fine-tuning with domain-specific data ensures the model’s responses are both accurate and relevant, greatly improving its utility in these specialized areas. Moreover, fine-tuning can significantly enhance the model’s overall performance. It refines the model’s understanding of context, sharpens its accuracy, and minimizes the generation of irrelevant or incorrect information.

Using LLaMA Factory to find tune LLMs is not only efficient and cost-effective, but it also supports a wide range of major open-source models, including Llama, Falcon, Mistol, Quin chat, GLM, and more. The LLaMA Factory features a user-friendly web user interface (Web UI), making it easily accessible to users with different levels of technical knowledge. This intuitive interface allows you to adjust the self-cognition of an instruction tune language model in just 10 minutes, using a single graphics processing unit (GPU). This swift and efficient process highlights the LLaMA Factory’s dedication to user-friendly design and functionality.

Easily fine tune LLMs using LLaMA Factory

Furthermore, the LLaMA Factory gives you the ability to set the language, checkpoints, model name, and model path. This level of customization ensures that the model is tailored to your specific needs and goals, providing a personalized experience. You also have the option to upload various files for model training, enabling a more focused and individualized approach to model development.

Other articles we have written that you may find of interest on the subject of fine tuning large language models:

LLaMA Factory

After your model has been trained and fine-tuned, the LLaMA Factory provides you with the tools to evaluate its performance. This essential step ensures that the model is operating at its best and meeting your predefined goals. Following the evaluation, you can export the model for further use or integration into other systems. This feature offers flexibility and convenience, allowing you to get the most out of your model. If you’re interested in integrating GPT AI models into your website check out our previous article.

Beyond its technical capabilities, the LLaMA Factory also plays a vital role in nurturing a vibrant AI community. It provides a private Discord channel that offers paid subscriptions for AI tools, courses, research papers, networking, and consulting opportunities. This feature not only enhances your technical skills but also allows you to connect with other AI enthusiasts and professionals. This fosters a sense of community and encourages collaboration and knowledge sharing, further enriching your experience.

Fine tuning LLMs

Another critical aspect of fine-tuning involves addressing and mitigating biases. LLMs, like any AI system, can inherit biases from their training data. By fine-tuning with carefully curated datasets, these biases can be reduced, leading to more neutral and fair responses. This process is particularly vital in ensuring that the model adheres to ethical standards and reflects a balanced perspective.

Furthermore, the world is constantly evolving, with new information and events shaping our society. LLMs trained on historical data may not always be up-to-date with these changes. Fine-tuning with recent information keeps the model relevant, informed, and capable of understanding and responding to contemporary issues. This aspect is crucial for maintaining the model’s relevance and usefulness.

Lastly, fine-tuning allows for customization based on user needs and preferences. Different applications might require tailored responses, and fine-tuning enables the model to adapt its language, tone, and content style accordingly. This customization is key in enhancing the user experience, making interactions with the model more engaging and relevant. Additionally, in sensitive areas such as privacy, security, and content moderation, fine-tuning ensures the model’s compliance with legal requirements and ethical guidelines.

In essence, fine-tuning is not just an enhancement but a necessity for LLMs, ensuring they are accurate, unbiased, up-to-date, and tailored to specific user needs and ethical standards. It’s a process that significantly extends the utility and applicability of these models in our ever-changing world.

The LLaMA Factory represents a great way to quickly and easily fine tune large language models for your own applications and uses. Its user-friendly interface, customization options, and community-building features make it an invaluable tool for both AI beginners and experts. Whether you’re looking to develop a language model for a specific project or seeking to expand your knowledge in the field of AI, the LLaMA Factory offers a comprehensive solution that caters to a wide range of needs and goals. it is available to download from its official GitHub repository where full instructions on installation and usage are available.

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MemGPT transforms LLMs into operating systems

MemGPT transforms LLMs into operating systems

The advent of large language models (LLMs) has undeniably revolutionized the field of artificial intelligence. However, these models are not without their limitations. One of the most significant challenges they face is the constraint of limited context windows. This limitation hampers their utility in tasks such as extended conversations and document analysis.

To address this issue, a novel technique known as virtual context management has been proposed. Drawing inspiration from hierarchical memory systems in traditional operating systems, this technique provides the illusion of large memory resources through the movement of data between fast and slow memory. This guide provides an introduction to MemGPT (Memory-GPT), a system that employs this technique to intelligently manage different memory tiers, effectively providing extended context within the LLM’s limited context window.

MemGPT is a system that augments a fixed-context LLM processor with a tiered memory system and a set of functions that allow it to manage its own memory. The main context is the fixed-length LLM input. MemGPT parses the LLM text outputs at each processing cycle and either yields control or executes a function call. These function calls can be used to move data between the main and external context. When the LLM generates a function call, it can request immediate return of execution to chain together functions. In the case of a yield, the LLM will not be run again until the next external event trigger, such as a user message or scheduled interrupt.

an introduction to MemGPT

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The concept of MemGPT is inspired by virtual memory in operating systems, which is used to create an unbounded LLM context. This is particularly useful in the context of perpetual chats, where limited context lengths can make the process challenging. With MemGPT, LLMs can be taught to manage their own memory, thereby overcoming the limitations of fixed context lengths.

The utility of MemGPT extends beyond perpetual chats. It has been evaluated in two domains where the limited context windows of modern LLMs severely handicap their performance: document analysis and multi-session chat. In the case of document analysis, MemGPT is able to analyze large documents that far exceed the underlying LLM’s context window. This is a significant advancement, as it allows for more comprehensive and in-depth analysis of large volumes of text.

In the realm of multi-session chat, MemGPT can create conversational agents that remember, reflect, and evolve dynamically through long-term interactions with their users. This is a significant step forward in the development of AI chatbots, as it allows for more natural and engaging conversations that can evolve over time.

MemGPT represents a significant advancement in the field of large language models. By intelligently managing different memory tiers and providing extended context within the LLM’s limited context window, it overcomes some of the key limitations of these models. Whether it’s enabling more comprehensive document analysis or facilitating more engaging and dynamic conversations in multi-session chats, the potential applications of MemGPT are vast and exciting. As we continue to push the boundaries of what is possible with large language models, systems like MemGPT will undoubtedly play a crucial role in shaping the future of this field.

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Perplexity Lab pplx-api API for open-source LLMs

Perplexity API for open-source LLMs

Perplexity Labs has recently introduced a new, fast, and efficient API for open-source Large Language Models (LLMs) known as pplx-api. This innovative tool is designed to provide quick access to various open-source LLMs, including Mistral 7B, Llama2 13B, Code Llama 34B, and Llama2 70B. The introduction of pplx-api marks a significant milestone in the field of AI, offering a one-stop-shop for open-source LLMs.

One of the key features of pplx-api is its ease of use for developers. The API is user-friendly, allowing developers to integrate these models into their projects with ease using a familiar REST API. This ease of use eliminates the need for deep knowledge of C++/CUDA or access to GPUs, making it accessible to a wider range of developers.

Perplexity Lab pplx-api

The pplx-api also boasts a fast inference system. The efficiency of the inference system is remarkable, offering up to 2.9x lower latency than Replicate and 3.1x lower latency than Anyscale. In tests, pplx-api achieved up to 2.03x faster overall latency compared to Text Generation Inference (TGI), and up to 2.62x faster initial response latency. The API is also capable of processing tokens up to 2x faster compared to TGI. This speed and efficiency make pplx-api a powerful tool for developers working with LLMs.

Benefits of the pplx-api

  • Ease of use: developers can use state-of-the-art open-source models off-the-shelf and get started within minutes with a familiar REST API.

  • Blazing fast inference:  thoughtfully designed inference system is efficient and achieves up to 2.9x lower latency than Replicate and 3.1x lower latency than Anyscale.

  • Battle tested infrastructure: pplx-api is proven to be reliable, serving production-level traffic in both Perplexity answer engine and  Labs playground.

  • One-stop shop for open-source LLMs: Perplexity Labs is dedicated to adding new open-source models as they arrive. For example, we added Llama and Mistral m

The infrastructure of pplx-api is reliable and battle-tested. It has been proven reliable in serving production-level traffic in both Perplexity’s answer engine and Labs playground. The infrastructure combines state-of-the-art software and hardware, including AWS p4d instances powered by NVIDIA A100 GPUs and NVIDIA’s TensorRT-LLM. This robust infrastructure makes pplx-api one of the fastest Llama and Mistral APIs commercially available.

API for open-source LLMs

The pplx-api is currently in public beta and is free for users with a Perplexity Pro subscription. This availability allows a wider range of users to test and provide feedback on the API, helping Perplexity Labs to continually improve and refine the tool. The API is also cost-efficient for LLM deployment and inference. It has already resulted in significant cost savings for Perplexity, reducing costs by approximately $0.62M/year for a single feature. This cost efficiency makes pplx-api a valuable tool for both casual and commercial use.

The team at Perplexity is committed to adding new open-source models as they become available, ensuring that pplx-api remains a comprehensive resource for open-source LLMs. The API is also used to power Perplexity Labs, a model playground serving various open-source models. The introduction of pplx-api by Perplexity Labs represents a significant advancement in the field of AI. Its ease of use, fast inference system, reliable infrastructure, and cost efficiency make it a powerful tool for developers working with open-source LLMs. As the API continues to evolve and improve, it is expected to become an even more valuable resource for the AI community.

In the near future, pplx-api will support:

  • Custom Perplexity LLMs and other open-source LLMs.

  • Custom Perplexity embeddings and open-source embeddings.

  • Dedicated API pricing structure with general access after public beta is phased out.

  • Perplexity RAG-LLM API with grounding for facts and citations.

How to access pplx-api

You can access the pplx-api REST API using HTTPS requests. Authenticating into pplx-api involves the following steps:

1. Generate an API key through the Perplexity Account Settings Page. The API key is a long-lived access token that can be used until it is manually refreshed or deleted.
2. Send the API key as a bearer token in the Authorization header with each pplx-api request.
3. It currently support Mistral 7B, Llama 13B, Code Llama 34B, Llama 70B, and the API is conveniently OpenAI client-compatible for easy integration with existing applications.

For more information, visit the official Perplexity Labs API documentation and Quickstart Guide.

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StreamingLLM helps improve the speed and of you LLMs

improve the speed of your large language model

If you have been noticing that your locally installed LLM is slowing down when you try to include larger prompts. You may be interested in a new solution to improve the speed and performance of large language models in the form of StreamingLLM helps improve the speed and performance of you LLMs . Extending Llama 2 and Falcon up to 4 million tokens and providing a 22 times faster inference than your standard LLM.

Check out the video created below by AI Jason who explains more about StreamingLLM and how it can be used to improve performance of locally installed AI models. Exploring these challenges and explores potential solutions, focusing on a new research project that aims to increase the data input capacity and efficiency of LLMs.

One of the primary challenges in deploying LLMs in streaming applications is the extensive memory consumption during the decoding stage. This is due to the caching of Key and Value states (KV) of previous tokens. This issue is further compounded by the fact that popular LLMs, such as Llama-2, MPT, Falcon, and Pythia, cannot generalize to longer texts than the training sequence length. This limitation is primarily due to GPU memory constraints and the computational time required by the complex Transformer architecture used in these models.

A common solution to manage large data inputs is the use of Window attention. This approach involves caching only the most recent KVs, effectively limiting the amount of data that needs to be stored. However, this method has a significant drawback: it loses context about the removed tokens. When the text length surpasses the cache size, the performance of window attention deteriorates, leading to a loss of context and a decrease in the quality of the generated content.

StreamingLLM helps improve the speed of you LLMs

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This problem led researchers to observe an interesting phenomenon known as attention sink. They found that the model pays more attention to initial tokens than later ones, even if the initial tokens are not semantically important. This phenomenon, they discovered, could be leveraged to largely recover the performance of window attention.

Based on this analysis, the researchers introduced StreamingLLM, an efficient framework that enables LLMs trained with a finite length attention window to generalize to infinite sequence length without any fine-tuning. This approach uses a combination of the first few tokens that have attention sink and a rolling cache of the latest tokens. This allows the LLM to maintain context about what has been discussed before, as well as recent conversation, effectively extending the effective context window.

The StreamingLLM approach has shown promising results, enabling LLMs to perform stable and efficient language modeling with up to 4 million tokens and more. In streaming settings, it outperforms the sliding window recomputation baseline by up to 22.2x speedup. This makes it particularly useful for applications such as long-form content generation and chatbots with long-term memory.

However, it’s important to note that StreamingLLM is not without its limitations. While it does maintain context about the beginning and end of a conversation, it still loses detailed context in the middle. This means it may not work well for summarizing large amounts of data, such as research papers.

The introduction of StreamingLLM and the concept of attention sink represent significant strides in overcoming the challenges of feeding unlimited data to LLMs. However, they are just one solution to the context limit problem. As the field of artificial intelligence continues to evolve, it’s likely that more creative concepts will emerge to further enhance the capacity and efficiency of LLMs.

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SteerLM a simple technique to customize LLMs during inference

SteerLM a simple technique to customise LLMs during inference

Large language models (LLMs) have made significant strides in artificial intelligence (AI) natural language generation. Models such as GPT-3, Megatron-Turing, Chinchilla, PaLM-2, Falcon, and Llama 2 have revolutionized the way we interact with technology. However, despite their progress, these models often struggle to provide nuanced responses that align with user preferences. This limitation has led to the exploration of new techniques to improve and customize LLMs.

Traditionally, the improvement of LLMs has been achieved through supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). While these methods have proven effective, they come with their own set of challenges. The complexity of training and the lack of user control over the output are among the most significant limitations.

In response to these challenges, the NVIDIA Research Team has developed a new technique known as SteerLM. This innovative approach simplifies the customization of LLMs and allows for dynamic steering of model outputs based on specified attributes. SteerLM is a part of NVIDIA NeMo and follows a four-step technique: training an attribute prediction model, annotating diverse datasets, performing attribute-conditioned SFT, and relying on the standard language modeling objective.

Customize large language models

One of the most notable features of SteerLM is its ability to adjust attributes at inference time. This feature enables developers to define preferences relevant to the application, thereby allowing for a high degree of customization. Users can specify desired attributes at inference time, making SteerLM adaptable to a wide range of use cases.

The potential applications of SteerLM are vast and varied. It can be used in gaming, education, enterprise, and accessibility, among other areas. The ability to customize LLMs to suit specific needs and preferences opens up a world of possibilities for developers and end-users alike.

In comparison to other advanced customization techniques, SteerLM simplifies the training process and makes state-of-the-art customization capabilities more accessible to developers. It uses standard techniques like SFT, requiring minimal changes to infrastructure and code. Moreover, it can achieve reasonable results with limited hyperparameter optimization.

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The performance of SteerLM is not just theoretical. In experiments, SteerLM 43B achieved state-of-the-art performance on the Vicuna benchmark, outperforming existing RLHF models like LLaMA 30B RLHF. This achievement is a testament to the effectiveness of SteerLM and its potential to revolutionize the field of LLMs.

The straightforward training process of SteerLM can lead to customized LLMs with accuracy on par with more complex RLHF techniques. This makes high levels of accuracy more accessible and enables easier democratization of customization among developers.

SteerLM represents a significant advancement in the field of LLMs. By simplifying the customization process and allowing for dynamic steering of model outputs, it overcomes many of the limitations of current LLMs. Its potential applications are vast, and its performance is on par with more complex techniques. As such, SteerLM is poised to play a crucial role in the future of LLMs, making them more user-friendly and adaptable to a wide range of applications.

To learn more about SteerLM and how it can be used to customise large language models during inference jump over to the official NVIDIA developer website.

Source &  Image :  NVIDIA

Filed Under: Technology News, Top News





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