Evals are often used to help provide a sense of quality when it comes to models. With the recent release of our 1M context window Llama 3 8B model, we take a deeper look into one of the most popular evals – Needle in a Haystack. Take a look at why haystack diversity matters and the significant impact that haystack composition has on this eval.
As more diverse Large Language Models (LLMs) become available each year, enterprises are turning to domain-specific models to develop specialized AI solutions, tailored to navigate the unique challenges and regulations of specific industries.
Today, we’re thrilled to share the newest addition to our Agent Toolkit - audio transcription. Using Gradient’s audio transcription API, users can easily transcribe audio files that can be used in variety of use cases.
Gradient recently partnered with a prominent hospital network to help optimize the way they managed inbound questions related to medical benefits. Take a look at how they leveraged Nightingale, Gradient's domain-specific Healthcare LLM, to develop an AI-powered medical benefits chatbot to help streamline this process.
Training large amounts of LLMs at once can be complex and can take thousands or millions of GPU hours - constraining your resources. Take a look at how Gradient thinks about infrastructure and efficiency optimizations as we dive into our own proprietary distributed training platform focusing on orchestration and fault tolerance.
Discover how a large US bank worked directly with Gradient to help enhance their AML program by using AI to improve their transaction monitoring process to detect suspicious activities more efficiently.
Explore how a leading hospital network based in the US that’s serving over 100,000 patients, partnered with Gradient to harness the full potential of their data.
The process that it took to overcome the challenges of PDF extraction is an interesting one to dive into, due to inherent characteristics from PDF formats and the nature of the data it often contains. We're diving into what these challenges are and how Gradient designed their Accelerator Block for PDF Extraction to overcome them.
Gradient just announced a new partnership with Snowflake, to help provide an even more seamless experience for Snowflake customers. Check out how you can enable the use of Gradient through Snowpark Container Services and access Gradient Datapacks within Snowflake Marketplace.
Today, we’re thrilled to share our newest Accelerator Block API for PDF extraction, enabling users to easily and effectively extract data from PDFs for RAG and AI agent development.
Take a look at how a large US based bank with a leading consumer product, partnered with Gradient to implement an AI based strategy to enhance triaging for customer requests.
Multimodal AI represents a groundbreaking advancement, signaling a new era in innovation for machine learning and cognitive computing. Chris Chang, CEO and Co-Founder at Gradient, explores the essence of multimodal AI, highlighting its transformative power in improving intricate cognitive functions and spatial awareness.
In the realm of artificial intelligence and machine learning, it is the relentless pursuit of innovation and the quest for understanding complex problems that drive the field forward. At Gradient we’re excited to announce Soufiane Hayou, as the first recipient of our Gradient AI Research Fellowship - a testament to his ongoing work in the field of neural network scalability.
Take a look at how a US based asset management fund partnered with Gradient, to improve investment performance by streamlining the process to analyze data and enhancing decision-making capabilities.
A few months ago, we demonstrated in a tutorial on how you can develop a financial chatbot using Gradient, LlamaIndex, and MongoDB. Today we'll walk you through how you can create that very same financial chatbot, 10x faster using Gradient's Accelerator Blocks.
Take a look at how a leading mental health provider based in the US partnered with Gradient to revolutionize its approach to patient care by enhancing the accuracy and utility of clinical documentation.
Today, we’re thrilled to share that Gradient is SOC 2 Type 2 compliant. At Gradient, we believe that security should be considered a top priority from the start. Our platform, technology, processes, and procedures have been assessed by Johanson Group – an external, independent auditor – and we have met the highest standards.
The 2024 Gradient AI Research Award has been presented to a distinguished team of researchers in recognition of their contributions to the intersection of finance and artificial intelligence. This award is presented annually to individuals or teams that have made notable contributions in advancing the AI domain, particularly through innovative solutions for practical applications.
Gradient's Albatross LLM is transforming the financial sector, enabling advanced AI applications that create significant ROI for financial institutions. Join Leo Pekelis, Gradient's Chief Scientist, as he dives into the development of Albatross, the impact of LLMs within the financial sector, and the challenges of crafting domain-specific models within finance.
Today we’re excited to announce our AI Development Lab, a new service from Gradient to help simplify the AI development process for enterprise customers. The AI Development Lab offers enterprise organizations a white glove, end-to-end development service to help build private, custom LLMs and AI copilots.
RAG can severely improve the accuracy and quality of the responses generated by LLMs. With Gradient’s newly announced Accelerator Block for RAG, we’re making it possible to set up RAG in seconds - removing friction and complexity. Best of all, it’s powered by best-of-breed technologies like LlamaIndex and MongoDB.
Today we’re excited to announce that Alphatross, an earlier version of Gradient's Albatross model is now available on Hugging Face - providing access to a state-of-the-art financial model to help accelerate AI transformation for financial institutions.
Getting started with AI development can be challenging and resource intensive, which is why we’re excited to introduce Gradient Accelerator Blocks - comprehensive building blocks, designed to accelerate AI development through a low-code, frictionless experience.
With the rise of generative AI in financial services, financial institutions and technology providers must work together to navigate potential challenges and shape what responsible AI should look like to ensure that we are moving forward in the right direction.
Today, Gradient is excited to announce the integration of its cutting-edge LLM development platform with the Haystack framework.
Creating custom AI solutions that use both fine-tuning and retrieval augmented generation (RAG) can be challenging. However with the help of Gradient and best-in-class platforms and frameworks like MongoDB and LlamaIndex - you can create powerful and cost-effective solutions.
Enterprise businesses on Gradient are able to deploy thousands of custom LLMs at the same time, using the same compute and cost as they would with a single LLM. Discover how Gradient’s multi-model, shared foundation is providing enterprise businesses with a competitive edge.
Increasingly more business are leveraging AI to augment their organizations and large language models (LLMs) are behind what’s powering these incredible opportunities. However the process of optimizing LLMs with methods like retrieval augmented generation (RAG) can be complex, which is why we’ll be walking you through everything you should consider before you get started.
AI is creating more and more opportunities across enterprise and large language models (LLMs) are at the heart of the discussion. However getting started with fine-tuning and LLMs can be daunting, which is why we’ll be walking you through everything you should consider.
Gradient is announcing the release of the Gradient AI Cloud for Financial Services, an AI platform that accelerates AI transformation for the financial services industry. It has never been easier to build and deploy SOC 2-compliant AI solutions for your financial services organization.
We recently released the Gradient Embeddings API, to let users generate vector embeddings from text. This is a critical component to Retrieval Augmented Generation (RAG), a technique that can greatly enhance your LLM’s knowledge in a specific domain and reduce hallucinations in AI applications.
Today, Gradient is excited to announce the integration of its cutting-edge LLM fine-tuning platform with the LlamaIndex framework.
Gradient is announcing the release of the Gradient AI Cloud for Healthcare, an AI platform that accelerates AI transformation for the healthcare industry. It has never been easier to build and deploy HIPAA-compliant AI solutions for your healthcare organization.
The future of AI is a Mixture of Experts (MoE) approach, an ensemble of small models trained to be domain- or task-specific experts to give you the highest performing, cost-effective AI system tailor-made for you.
Today, Gradient is excited to announce the integration of its cutting-edge LLM fine-tuning platform with the LangChain framework.
We are introducing the Gradient Embeddings API to the Gradient Developer Platform make it easy to perform natural language tasks such as search and classification. This makes it easier for developers to create knowledge bases, no setup required.
Today we are launching the Gradient Developer Platform, an unparalleled developer platform designed to empower developers to easily customize open-source Large Language Models (LLMs) and seamlessly build them into various AI applications.
Today, we’re excited to announce that Gradient is SOC 2 Type I compliant.
At Gradient, we believe that security should be considered a top priority from the start. We prioritized obtaining SOC 2 Type I compliance to ensure our team is upholding the best practices in security. Our platform, technology, processes, and procedures have been assessed by Johanson Group – an external, independent auditor – and we have met the highest standards.
As the capabilities of large language models (LLMs) continue to advance, there is a growing curiosity among researchers and developers to understand the mechanisms behind them.
This is the first post in a series to help researchers understand the systems-level design choices involved in deploying LLMs.
“How do ML practitioners handle LLM inference without sacrificing latency or throughput?”
At Gradient, we’ve created a model that understands and produces both English and Chinese — by using an efficient, faster form of fine-tuning to enhance entirely open-source models and data.
Where will automation plug in? 1 of 3 in our Coding automation series.
AI/ML tech is moving fast. Whether you’re an engineer, CTO, or somewhere in between, you’re likely wondering how to prepare for today and for tomorrow.
