marktechpost5d
Meta AI Releases Meta’s Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
The discovery of new materials is crucial to addressing pressing global challenges such as climate change and advancements in next-generation computing. However, existing computational and ...
marktechpost5d
CREAM: A New Self-Rewarding Method that Allows the Model to Learn more Selectively and Emphasize on Reliable Preference Data
One of the most critical challenges of LLMs is how to align these models with human values and preferences, especially in generated texts. Most generated text outputs by models are inaccurate, biased, ...
marktechpost5d
Open Collective Releases Magnum/v4 Series Models From 9B to 123B Parameters
In the rapidly evolving world of AI, challenges related to scalability, performance, and accessibility remain central to the efforts of research communities and open-source advocates. Issues such as ...
marktechpost5d
RealHumanEval: A Web Interface to Measure the Ability of LLMs to Assist Programmers
The growing reliance on large language models for coding support poses a significant problem: how best to assess real-world impact on programmer productivity? Current approaches, such as static ...
marktechpost6d
Scaling Diffusion transformers (DiT): An AI Framework for Optimizing Text-to-Image Models Across Compute Budgets
Large language models (LLMs) have demonstrated consistent scaling laws, revealing a power-law relationship between pretraining performance and computational resources. This relationship, expressed as ...
marktechpost6d
Rethinking Direct Alignment: Balancing Likelihood and Diversity for Better Model Performance
The problem of over-optimization of likelihood in Direct Alignment Algorithms (DAAs), such as Direct Preference Optimisation (DPO) and Identity Preference Optimisation (IPO), arises when these methods ...
marktechpost6d
This AI Paper from Google DeepMind Explores Inference Scaling in Long-Context RAG
Long-context Large language models (LLMs) are designed to handle long input sequences, enabling them to process and understand large amounts of information. As the interference computation power is ...
marktechpost6d
Self-Data Distilled Fine-Tuning: A Solution for Pruning and Supervised Fine-tuning Challenges in LLMs
Large language models (LLMs) like GPT-4, Gemini, and Llama 3 have revolutionized natural language processing through extensive pre-training and supervised fine-tuning (SFT). However, these models come ...
marktechpost4d
IBM Releases Granite 3.0 2B and 8B AI Models for AI Enterprises
Artificial intelligence is advancing rapidly, but enterprises face many obstacles when trying to leverage AI effectively. Organizations require models that are adaptable, secure, and capable of ...
marktechpost5d
This AI Research from Cohere for AI Compares Merging vs Data Mixing as a Recipe for Building High-Performant Aligned LLMs
Large language models (LLMs) have revolutionized the field of artificial intelligence by performing a wide range of tasks across different domains. These models are expected to work seamlessly in ...
marktechpost5d
aiXcoder-7B: A Lightweight and Efficient Large Language Model Offering High Accuracy in Code Completion Across Multiple Languages and Benchmarks
Large language models (LLMs) have revolutionized various domains, including code completion, where artificial intelligence predicts and suggests code based on a developer’s previous inputs. This ...
marktechpost4d
JAMUN: A Walk-Jump Sampling Model for Generating Ensembles of Molecular Conformations
The dynamics of protein structures are crucial for understanding their functions and developing targeted drug treatments, particularly for cryptic binding sites. However, existing methods for ...