Kili Technology

🤩 Let’s discover the pioneering, or should we say a ‘fine’ dataset, that is democratizing LLM training! 🚀 With its impressive 15 trillion tokens of meticulously cleaned and deduplicated data, Hugging Face's FineWeb dataset is setting a new benchmark 📊✨ Here’s why FineWeb stands out: 🔹 Massive Scale: Over 15 trillion tokens sourced from CommonCrawl, ensuring extensive and diverse data coverage. 🔹 Rigorous Cleaning: Advanced filtering and deduplication processes using the datatrove library ensure top-tier data quality. 🔹 Benchmark Performance: FineWeb outperforms other high-quality datasets like C4, Dolma-v1.6, The Pile, SlimPajama, and RedPajama in key benchmark tasks. 🔥 High-quality datasets like FineWeb are essential for training LLMs to understand and generate human-like text. FineWeb's careful curation process ensures that models trained on it are more accurate, reliable, and better at advanced language understanding. 🌟 👀 Read more about the FineWeb Dataset by Hugging Face in our newest blog article 👇 #FineWebDataset #AI #LLM

🔥 Organizations aim to harness the full potential of Generative AI face however they face significant challenges such as developing sophisticated benchmarks, discovering the optimal data mix for model building and fine-tuning, and more. 🚀 Let’s see how Kili Technology can address these challenges 👇 🔹 Leveraging GenAI for Faster Dataset Creation: - Integration with Labelling Copilots: Using GenAI as a copilot to efficiently create evaluation and fine-tuning datasets, iterate on prompts, benchmark performance, and scale labeling efforts. - Automation with SMEs: Collaborate with Subject Matter Experts (SMEs) and GenAI to refine prompts, review performance, and iterate, reducing manual labeling time and effort significantly. 🔹 Building Sophisticated UIs and Tools: - Customizable Labeling UIs: Develop fully customizable labeling interfaces for fine-tuning and evaluating large language models, tailored to specific project needs. - Pairing UIs with Automation: Enhance efficiency and accuracy by automating data labeling processes. Models can flag poor responses, which are then reviewed and corrected by human experts to ensure high-quality training data. 🔹 Scaling High-Quality Dataset Creation: - Human Alignment Teams: Ensure quality assurance, faster scaling, and improved feedback with accessible human alignment teams. Expert management is essential to handle complexities and iterate efficiently. By integrating GenAI with expert networks and project management, organizations can scale the creation of high-quality datasets, leading to more effective and valuable Generative AI applications.✨ Read more in our new blog article here 👇 #GenAI #DataLabellingSolutions #Datasets

Multilayered image annotations are opening new frontiers across various industries. However, industries like the healthcare sector require highly detailed annotations, such as labelling different parts of an object or capturing fine-grained details in medical images.💡 Here’s some significant use cases of Multilayer views in the Healthcare industry: - Annotate medical scans where different parts of the body are precisely labeled. This multilayered approach can help AI/ML systems detect and analyse complex structures, leading to better diagnosis and treatment planning. - Labelling different regions of interest in MRI scans, for example, allows for precise diagnosis and treatment planning, ultimately improving patient outcomes. Read the blog to know more about the use cases of Multilayer views in image annotation! #AI #MultilayerAnnotation #labeling

We all know that accurate image data annotation is crucial for training our computer vision models. The better the data quality of your image annotation dataset, the more effective your machine learning model is.💡 With our latest update, our platform provides data scientists and annotators with multilayer views for image annotations. This improves image annotation projects for better model performances. Similar to geospatial image annotation tasks, a multilayer approach enhances the quality of the labeled data.🚀 Here’s how this innovative method can transform the process: - Complex Interactions: Accurately capture overlapping objects in autonomous driving scenes. - Varied Scales: Annotate objects at different scales and angles in aerial surveillance. - Contextual Relationships: Understand spatial relationships in industrial automation. - Occlusions: Maintain accuracy despite partially hidden objects in retail management. - Detailed Annotations: Achieve precise labeling in healthcare, improving diagnosis and treatment. Multilayer views in image annotation not only improve model performance but also ensure the annotations capture the full complexity and variability of visual data. This approach is pivotal for tasks ranging from object detection and image classification to scene understanding.✨ By leveraging multilayer views, we're committed to pushing the boundaries of what's possible in computer vision, enabling more accurate and reliable models for a wide range of applications. #ImageAnnotation #KiliTechnology #AI #Innovation #MultilayerAnnotation

😤 Ever felt frustrated because you couldn’t rotate the image you’re working on? We fixed that! 🥳 🙂↕️ With the latest update on Kili Technology, you can now rotate your project when you work, and the annotations follow their initial places. That means labels also rotate with your image! 🌍 Rotating a geospatial image for annotation purposes can be necessary for several reasons: 1 - Aligning with Map Orientation: Geospatial images often need to be rotated to align with the standard orientation of maps or other geospatial data. 2 - Improving Interpretability: Rotating an image can sometimes provide a clearer perspective, making it easier for annotators to interpret and annotate features accurately. 3 - Optimizing Annotation Efficiency: Rotating an image may optimize the annotation process by presenting features in a more natural or intuitive orientation. 4 - Correcting Sensor Orientation: Geospatial images captured by aerial or satellite sensors may have inherent rotation due to sensor orientation or flight path, which can be corrected by rotating the image to ensure accurate annotations relative to the Earth's surface. 🔥 Try it out right now with the updated Kili Technology web app! #product #update #data

What an event! 🔥🥰 We were delighted to have our CTO and co-founder Edouard D.’archimbaud on the stage of Adopt AI Summit by Arterfact. 💪✨ We also want to thank every one of those we saw there especially! 🧡💚💜 - Jaap Zuiderveld (NVIDIA) - Sébastien Jamon (Salesforce) - Arthur Mensch (Mistral AI) - Vincent LucIAni (Artefact) - Damien GROMIER and Alexis Poujade (Artefact) Looking forward the next event! 👀 #event #LLM #AI

In light of the huge AI announcements during Apple's #WWDC, it's a great time to explore on-device language models, starting with Apple's open-sourced #OpenELM model shared in April. 🔹 Training Overview: Apple's OpenELM model is designed to run efficiently on personal devices, offering robust natural language understanding and generation capabilities. 🔹 Datasets: The model used a mix of diverse and open-sourced datasets: RefinedWeb, PILE, RedPajama, and Dolma v1.6. 🔹 Training Methods: They did on-the-fly tokenization where they dynamically tokenized and filtered text during training so they can quickly experiment and iterate faster. 🔹 Instruction Tuning: For fine-tuning they used UltraFeedback, specifically designed to improve language models through #RLHF. Dive deeper here: https://lnkd.in/e7K6-eka #wwdc #AI #LLM

👀 What a pleasure to see our CTO and Co-founder Edouard D. on the stage of Adopt AI Summit, organized by Artefact. He discussed data being the new frontier in the GenAI era and the transformative impact of data-centric approaches on AI innovation. 🤩✨ It was also great to hear Sebastien Jamon (Salesforce) about the capabilities of EinsteinAI and Jaap Zuiderveld (NVIDIA) sharing insights on the directions industries are taking thanks to the influence of AI . 👋 #event #speaker #AI

⚔️ The versatility of an application depends on how well its model is data-trained. Open ELM by Apple is one of those models that leverages its diverse training datasets to achieve efficiency and accuracy. 🚀 Here are some of the datasets used by Apple for Open ELM 👇 🔗 Read the full article to learn more about the datasets behind OpenELM and their impact! #AI #MachineLearning #AppleOpenELM

🍎 The latest update by Apple (not the IOS update)! Let’s talk about OpenELM by Apple 🚀 The evolving nature of AI and Apple’s Open ELM are examples of change towards smaller, more efficient language models that can be deployed directly on local devices ✨ Have a look at some of the stand-out notes on Open ELM 👇 Click below to read the full article! #AI #MachineLearning #AppleOpenELM