Machine learning pioneer Hugging Face has launched Transformers version 4.42, a meaningful update to its well-regarded machine-learning library. Significant enhancements include the introduction of several advanced models, improved tool and retrieval-augmented generation support, GGUF fine-tuning, and quantized KV cache incorporation among other enhancements.
The release features the addition of new models like Gemma 2, RT-DETR, InstructBlip,…
Multimodal large language models (MLLMs) are crucial tools for combining the capabilities of natural language processing (NLP) and computer vision, which are needed to analyze visual and textual data. Particularly useful for interpreting complex charts in scientific, financial, and other documents, the prime challenge lies in improving these models to understand and interpret charts accurately.…
In the rapidly advancing field of Artificial Intelligence (AI), evaluating the outputs of models accurately becomes a complex task. State-of-the-art AI systems such as GPT-4 are using Reinforcement Learning with Human Feedback (RLHF) which implies human judgement is used to guide the training process. However, as AI models become intricate, even experts find it challenging…
Artificial intelligence (AI) is growing at a rapid pace, giving rise to a branch known as AI agents. These are sophisticated systems capable of executing tasks autonomously within specific environments, using machine learning and advanced algorithms to interact, learn, and adapt. The burgeoning infrastructure supporting AI agents involves several notable projects and trends that are…
The field of software engineering has made significant strides with the development of Large Language Models (LLMs). These models are trained on comprehensive datasets, allowing them to efficiently perform a myriad of tasks which comprise of code generation, translation, and optimization. LLMs are increasingly being employed for compiler optimization. However, traditional code optimization methods require…
Jina AI has launched a new advanced model, the Jina Reranker v2, aimed at improving the performance of information retrieval systems. This advanced transformer-based model is designed especially for text reranking tasks, efficiently reranking documents based on their relevance towards a particular query. The model operates on a cross-encoder model, taking a pair of query…
Large Language Models (LLMs) have made significant strides in addressing various reasoning tasks, such as math problems, code generation, and planning. However, as these tasks become more complex, LLMs struggle with inconsistencies, hallucinations, and errors. This is especially true for tasks requiring multiple reasoning steps, which often operate on a "System 1" level of thinking…
The Imbue Team announced significant progress in their recent project in which they trained a 70-billion-parameter language model from the ground up. This ambitious endeavor is aimed at outperforming GPT-4 in zero-shot scenarios on several reasoning and coding benchmarks. Notably, they achieved this feat with a training base of just 2 trillion tokens, a reduction…
Natural Language Processing (NLP), a field within artificial intelligence, is focused on creating ways for computers and human language to interact. It's used in many technology sectors such as machine translation, sentiment analysis, and information retrieval. The challenge presently faced is the evaluation of long-context language models, which are necessary for understanding and generating text…
Large language models (LLMs), despite their significant advancements, often struggle in situations where information is spread across long stretches of text. This issue, referred to as the "lost-in-the-middle" problem, results in a diminished ability for LLMs to accurately find and use information that isn't located near the start or end of the text. Consequently, LLMs…
Large language models (LLMs), despite their advancements, often face difficulties in managing long contexts where information is scattered across the entire text. This phenomenon is referred to as the ‘lost-in-the-middle’ problem, where LLMs struggle to accurately identify and utilize information within such contexts, especially as it becomes distant from the beginning or end. Researchers from…