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Fine-tuning LLMs is quite interesting for some specific use - cases, like: - When you have a specific task or domain that differs from the general use case of the pre-trained model. - When you have a dataset that represents your specific use case or contains information not present in the original training data. - When you need to improve the model's performance on particular types of inputs or outputs. - When you want to adapt the model's style, tone, or domain-specific language. And nowadays, fine-tuning provides consistent and very good results. Thanks to Hugging Face and the open source community, we also have tools in place, allowing virtually everyone to engage in fine-tuning. Using the right methods, one can fine-tune a 7B parameter model on a single consumer GPU! We are happy to provide a fine-tuning hands-on introduction in our latest blog post - showing how to execute a fine-tuning training loop and more importantly, explaining the more important parameters of the quantization, PEFT and training instances. https://lnkd.in/dzyMFiDD

Extracting structured data from documents used to be extremely hard. With the rise of great open source models with vision capabilities this suddenly gets much easier and cheaper. Just some weeks ago, you would've needed to purchase 500k in GPUs and massively train & fine tune a full pipeline of ML models just to get some data from documents (if you had multiple docs with multiple layouts for example). Today, however, you can simply use an off-the-shelf LLM with vision capabilities - and you have the best parser you can imagine. Read more about how this works in our latest post https://lnkd.in/dJ85k_fE

What if we told you, that you can add a summary to all your texts in your PostgreSQL database with a single command? Directly from within PostgreSQL? Timescale just released a new PostgreSQL extension called "pgai". It provides some simple Postgres functions for: - embedding creation - text generation API invokation - moderation It basically allow to call the OpenAI API from within Postgres. No additional runtime environment, no build step, no external script - just Postgres. Why is this helpful? For example, simply call UPDATE ... SET label = openai_chat_complete(); and you can add labels to ALL of your rows in Postgres. Just imagine that for a second. One single Postgres function call and all your data are updated, using an LLM! Read more about: 🔧 Installation and setup of pgai 💡 Use-cases including content moderation, automatic tagging, and text summarization ⚙️ Hands-on examples and SQL queries to get you started 🚀 Automating AI tasks with triggers for seamless workflows in our latest blog: https://lnkd.in/djm8RvYG

🔍 Is This the End of Feedforward Networks? A Deep Dive into Kolmogorov-Arnold Networks (KANs)🔍 Are Multi-Layer Perceptrons (MLPs) on their way out? These fully-connected feedforward networks have been the backbone of powerful AI models, driving advancements in image recognition, speech processing, and predictive analytics. But a new player has entered the arena: Kolmogorov-Arnold Networks (KANs). KANs leverage spline functions instead of static weights, offering remarkable accuracy and interpretability, even with smaller network sizes. Could this be the next revolutionary step in AI? 🧠 Key Differences: MLPs vs. KANs MLPs: - Utilize static weights and activation functions. - Known for their versatility and power in modeling complex relationships. - Relies on the Universal Approximation Theorem. KANs:  - Use spline functions instead of static weights. - Founded on the Kolmogorov-Arnold Representation Theorem. - Offer high accuracy, efficiency, interpretability, and support continuous learning. KANs combine the strengths of MLPs and splines, achieving impressive performance with fewer parameters. For instance, a 2-layer KAN with 10 nodes can be 100 times more accurate and parameter-efficient than a 4-layer MLP with 100 nodes. 💡 Advantages of KANs: - Accuracy: High precision even with small networks. - Efficiency: Smaller size and fewer parameters without sacrificing performance. - Interpretability: Clear, understandable feature influences. - Continuous Learning: Supports incremental training without forgetting previously learned information. Despite their longer training times, KANs present a promising alternative to MLPs, with applications extending into scientific fields like physics and mathematics. The future of AI may not see MLPs disappearing overnight, but the potential of KANs cannot be ignored. Dive into our latest blog post to explore the full details and implications of this groundbreaking research. 🔗 https://lnkd.in/dEmaPAbF #AI #GenAI #DeepLearning #PondhouseAI #FutureOfAI

LLM safety is an important consideration, where the potential for misuse and harmful outputs is a real concern. Tools like Llama Guard 2 can help mitigate some of these risks by providing a set of features designed to improve the safety and reliability of LLM applications. Llama Guard 2 offers a customizable taxonomy that can be adapted to various use cases, and it has shown promising results in identifying and classifying potentially unsafe content. For developers building chatbots, AI assistants, or other LLM-based applications, incorporating safety measures like those provided by Llama Guard 2 can help ensure compliance with relevant guidelines and standards. Implementing safety tools can help protect applications from certain security threats and contribute to a more trustworthy user experience. As AI technology continues to get better and better, it will be important for developers to stay informed about best practices for maintaining a balance between innovation and security. https://lnkd.in/dxM7gQei

🚀 Introducing BitNet: The Frontier of LLM Quantization 🌐💡 In the rapidly evolving world of large language models (LLMs), the race for higher performance often comes with a hefty price tag—increased computational demand and energy consumption. But what if we could break these boundaries without sacrificing quality? Our latest blog post, "BitNet: LLM Quantization at its Extreme," explores Microsoft's groundbreaking approach to LLM efficiency. BitNet leverages extreme quantization, reducing model parameters to binary levels—yes, just 1-bit! This innovation not only slashes the computational load but also maintains competitive performance. 🔍 Highlights of the Blog: - Understanding BitNet: Dive into how BitNet uses binary weights to drastically reduce the computational demands of LLMs. - Comparative Analysis: See how BitNet stacks up against traditional post-training quantization methods, delivering robust performance with significantly lower energy use. - Future Implications: Discover the potential for deploying advanced LLMs on edge devices, boosting accessibility and efficiency. Join us as we explore how BitNet sets a new standard for LLMs, offering a sustainable solution that could revolutionize AI technology. It's time to think smaller for bigger gains! 👉 Read more about this technological leap on our blog! https://lnkd.in/dqMcpzRm #AI #MachineLearning #Quantization #TechnologyInnovation #SustainableAI

🌍💻 GenAI: A Technological Marvel or an Ecological Challenge? 🚀🔌 As we embrace the astounding capabilities of generative AI, it's crucial to ponder: Are we advancing towards a sustainable future or stepping closer to an ecological precipice? Our latest blog dives deep into the dual-edged impact of GenAI. From revolutionizing work with AI technologies like ChatGPT and groundbreaking image generators to escalating concerns over their hefty carbon footprints, we explore the intricate balance of innovation and its costs. 🔍 What's Inside? * Technological Advances: A look at how AI models have scaled, pushing the boundaries of what's possible. * Environmental Costs: Insight into the substantial energy demands of AI, heightened by the pursuit of better models. * Innovative Solutions: From specialized hardware enhancing efficiency to strategic load shifting aligning with renewable energy availability. As we navigate this complex terrain, the key lies in harnessing technology responsibly. Join us in examining how cutting-edge innovations and strategic interventions could steer us toward a sustainable trajectory. 👉 Dive into the full discussion on our blog and discover whether GenAI will lead us to a brighter future or deeper into an energy crisis. https://lnkd.in/dXHmaVrc #AI #Sustainability #TechnologyInnovation #GenAI

🔗 Chat with your Confluence - A Guide to Building AI Chatbots for Confluence Our latest blog post explores the process of creating an AI-powered chatbot that integrates with Confluence data, aiming to improve how teams access and interact with their knowledge base. We begin by discussing Airbyte, an open-source data integration platform that provides more than 300 connectors, allowing for amazing integration scenarios. The integration of PGVector, an extension for efficient vector operations, optimizes the system for executing large language model (LLM) queries. The guide also covers LangChain, a tool that connects different technologies, enabling smooth data workflows. This integration is essential for developing a chatbot powered by LLMs like GPT-4, which can understand complex queries and provide accurate, context-aware responses. We provide step-by-step instructions to help you get started, from setting up your environment to using PyAirbyte (an incredible python module for running Airbyte jobs in any python script) for data syncing and leveraging Langchain's capabilities for a dynamic AI interaction layer. This guide offers both theoretical knowledge and practical skills to help you implement your own AI chatbot, improving data retrieval and utilization within your organization. For those interested in combining AI with data management—including software developers and data enthusiasts—this guide serves as a starting point for developing advanced chatbot solutions that push the boundaries of AI in business processes. Check out the full guide to learn more! 👉 Read the full guide here: https://lnkd.in/dSWqMqzC #AI #DataManagement #Confluence #Chatbot #Airbyte #Langchain #PGVector #TechnologyIntegration #Optimization

Hypothetical Document Embeddings (HyDE) is an advanced technique designed to improve the effectiveness of Retrieval-Augmented Generation (RAG) systems. HyDE generates synthetic document embeddings from a query, which better represent the ideal answer. These embeddings guide the retrieval process towards more relevant documents, enhancing the overall quality of the generated responses. To implement HyDE, use a language model to create hypothetical documents based on the query, encode these documents into embeddings, and employ these embeddings to retrieve the most relevant information. This approach ensures that RAG systems can more accurately access and utilize external knowledge, especially in specialized or complex domains. Explore the full capabilities and setup instructions of HyDE on our latest blog for detailed guidance on integrating this technique into your RAG systems.

We at Pondhouse provide quite a powerful RAG application. Which does a lot of behind-the-scenes computing, prompt preparation and data processing. A lot of these processes require LLMs. At the same time we want to offer competitive pricing - therefore we need to optimize all these LLM calls as good as possible. Some of the tricks we used can be found in our latest post concerning cost savings when running LLMs. A lot of people pay too much for LLMs. Most people start by sending prompts to the LLM API, refine the prompt, create longer prompts - end get a huge bill at the end. There are a multitude of things one can do to tackle these costs. 1. Optimize your LLM prompt: Short on concise prompts are often not only cheaper, but also produce better answers. 2. Use task-specific, smaller models: It's not always GPT-4 and not always Llama2 72B. Sometimes a well-used Mixtral 7B does the job equally well - and requires way less resources. 3. Cache responses: If you implement just one of these tips - use this one. Semantically caching the response of LLMs can save thousands of $ - especially when you have many users. 4. Batch requests: Instead of sending individual requests to the LLM API every time you need to process some text, you can group multiple requests together and send them as a single batch. 5. Use prompt compression: LLMLingua-2 was just recently released. It's a technique and model proposed by Microsoft to reduce unnecessary words and tokens from prompt. 6. Use model quantization: If you self-host your models, most of the time you don't need the full-bit version. Quantization is a technique to use shorter floating point numbers and drastically reducing the required HW. Oftentimes with almost no real impact on quality. 7. Fine-tune your model: If you have a specific use case, fine tuning might help that the model does not need long prompts. This can result in quite significant savings. 8. Implement early stopping: Well, LLMs tend to talk and talk and talk... Implement a mechanism (and if it's just a stop button) to end LLM generation, once you have the answer. This saves on token costs. 9. Use model distillation: This is one of my favourites. Instead of training and fine-tuning models in the void, model distillation is a technique to use large, great and intelligent models like GPT-4 or Anthropic Claude 3 as "teachers" for small models. 10. Use RAG instead of sending everything to the LLM: Don't send huge walls of texts to LLMs. This is expensive. Use RAG to find the relevant paragraphs for your question and only send these. 11. Summarize your LLM conversation: Easy but powerful method: When you have a chatbot-style application, don't send the whole chat history all the time. Use things like the LangChain conversation memory, to summarize the conversation and only send the summary. What else did I miss? https://lnkd.in/d_GBqA-8