Author: Aayush Mittal

Reflection 70B is an open-source large language model (LLM) developed by HyperWrite. This new model introduces an approach to AI cognition that could reshape how we interact with and rely on AI systems in numerous fields, from language processing to advanced problem-solving. Leveraging Reflection-Tuning, a groundbreaking technique that allows the model to self-assess and correct

Computational power has become a critical factor in pushing the boundaries of what’s possible in machine learning. As models grow more complex and datasets expand exponentially, traditional CPU-based computing often falls short of meeting the demands of modern machine learning tasks. This is where CUDA (Compute Unified Device Architecture) comes, an approach to accelerate machine

As developers and dta scientists, we often find ourselves needing to interact with these powerful models through APIs. However, as our applications grow in complexity and scale, the need for efficient and performant API interactions becomes crucial. This is where asynchronous programming shines, allowing us to maximize throughput and minimize latency when working with LLM

Diffusion models have emerged as a powerful approach in generative AI, producing state-of-the-art results in image, audio, and video generation. In this in-depth technical article, we’ll explore how diffusion models work, their key innovations, and why they’ve become so successful. We’ll cover the mathematical foundations, training process, sampling algorithms, and cutting-edge applications of this exciting

Language models has witnessed rapid advancements, with Transformer-based architectures leading the charge in natural language processing. However, as models scale, the challenges of handling long contexts, memory efficiency, and throughput have become more pronounced. AI21 Labs has introduced a new solution with Jamba, a state-of-the-art large language model (LLM) that combines the strengths of both

Two new approaches that have emerged in this field are self-reasoning frameworks and adaptive retrieval-augmented generation for conversational systems. In this article, we’ll dive deep into these innovative techniques and explore how they’re pushing the boundaries of what’s possible with language models. The Promise and Pitfalls of Retrieval-Augmented Language Models Before we delve into the

While effective, RLHF has several drawbacks: It requires training and maintaining multiple models (SFT, reward model, and RL-optimized model) The RL process can be unstable and sensitive to hyperparameters It’s computationally expensive, requiring many forward and backward passes through the models These limitations have motivated the search for simpler, more efficient alternatives, leading to the

Black Forest Labs, the team behind the groundbreaking Stable Diffusion model, has released Flux – a suite of state-of-the-art models that promise to redefine the capabilities of AI-generated imagery. But does Flux truly represent a leap forward in the field, and how does it stack up against industry leaders like Midjourney? Let’s dive deep into

Artificial Intelligence (AI) has become the most fiscussed technological advancement of this decade. As we push the boundaries of what machines can do, the ultimate goal for many tech giants is to achieve Artificial General Intelligence (AGI) – a hypothetical form of AI that can understand, learn, and apply its intelligence to solve any problem

As Large Language Models (LLMs) grow in complexity and scale, tracking their performance, experiments, and deployments becomes increasingly challenging. This is where MLflow comes in – providing a comprehensive platform for managing the entire lifecycle of machine learning models, including LLMs. In this in-depth guide, we’ll explore how to leverage MLflow for tracking, evaluating, and