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Profil bibliographique

Aastha Jhunjhunwala

Informations fournies par OpenAlex. Research Africa ne déduit ni nationalité, ni poste, ni coordonnées personnelles.

17Publications signalées
138Citations signalées
0Affiliations récentes

Les domaines associés

Multimodal Machine Learning ApplicationsTopic ModelingSpeech Recognition and SynthesisReinforcement Learning in RoboticsGyrotron and Vacuum Electronics Research

Les publications récentes

Accès ouvert 2026 preprint OpenAlex

Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

NVIDIA, :, Aaron Blakeman, Austin Thomas et autres

We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 trillion text tokens, then extended the context length to 1M tokens, and post-trained using Supervised Fine …

0 citations arXiv (Cornell University)
Accès ouvert 2026 preprint OpenAlex

Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

NVIDIA, :, Aaron Blakeman, Austin Thomas et autres

We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 trillion text tokens, then extended the context length to 1M tokens, and post-trained using Supervised Fine …

0 citations arXiv (Cornell University)
2026 conference-paper OpenAlex

OmniScience: A Domain-Specialized LLM for Scientific Reasoning and Discovery

Vignesh Prabhakar, Md Amirul Islam, Adam Atanas, Yao‐Ting Wang et autres

Large Language Models (LLMs) have demonstrated remarkable potential in advancing scientific knowledge and addressing complex challenges. In this work, we introduce OmniScience, a specialized large reasoning model for general science, developed through three key components: (1) domain adaptive pretraining on a carefully …

1 citation EPiC series in technology
Accès ouvert 2025 preprint OpenAlex

NVIDIA Nemotron Nano V2 VL

NVIDIA, Amala Sanjay Deshmukh, Kateryna Chumachenko, Tuomas Rintamaki et autres

We introduce Nemotron Nano V2 VL, the latest model of the Nemotron vision-language series designed for strong real-world document understanding, long video comprehension, and reasoning tasks. Nemotron Nano V2 VL delivers significant improvements over our previous model, Llama-3.1-Nemotron-Nano-VL-8B, across all vision and …

0 citations arXiv (Cornell University)
Accès ouvert 2024 preprint OpenAlex

Data, Data Everywhere: A Guide for Pretraining Dataset Construction

Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Bo Liu et autres

The impressive capabilities of recent language models can be largely attributed to the multi-trillion token pretraining datasets that they are trained on. However, model developers fail to disclose their construction methodology which has lead to a lack of open information on how …

0 citations arXiv (Cornell University)
Accès ouvert 2024 preprint OpenAlex

Nemotron-4 340B Technical Report

Nvidia, Bo Adler, Niket Agarwal, Ashwath Aithal et autres

We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward. Our models are open access under the NVIDIA Open Model License Agreement, a permissive model license that allows distribution, modification, and use of the models and its outputs. These models …

7 citations arXiv (Cornell University)
Accès ouvert 2024 preprint OpenAlex

Nemotron-4 15B Technical Report

Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Mostofa Patwary et autres

We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates strong performance when assessed on English, multilingual, and coding tasks: it outperforms all existing similarly-sized open models on 4 out of 7 downstream …

0 citations arXiv (Cornell University)
Accès ouvert 2024 article OpenAlex

nach0: multimodal natural and chemical languages foundation model

Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina, Maksim Kuznetsov et autres

Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions. Our paper introduces a new foundation model, nach0, capable of solving various chemical and biological tasks: …

us, ca (code pays fourni par la source)

37 citations Chemical Science
Accès ouvert 2023 preprint OpenAlex

nach0: Multimodal Natural and Chemical Languages Foundation Model

Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina, Maksim Kuznetsov et autres

Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions. Our paper introduces a new foundation model, nach0, capable of solving various chemical and biological tasks: …

us, hk (code pays fourni par la source)

4 citations arXiv (Cornell University)

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