29 citations · 35 across the 8 of their papers we have counts for
4 papers · 1 filter
Training Sparse Mixture Of Experts Text Embedding Models
Zach Nussbaum, Brandon Duderstadt
Transformer-based text embedding models have improved their performance on benchmarks like MIRACL and BEIR by increasing their parameter counts. However, this scaling approach intr…
CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking
Tarun Suresh, Revanth Gangi Reddy, Yifei Xu +4
Effective code retrieval plays a crucial role in advancing code generation, bug fixing, and software maintenance, particularly as software systems increase in complexity. While cur…
Nomic Embed: Training a Reproducible Long Context Text Embedder
Zach Nussbaum, John X. Morris, Brandon Duderstadt +1
This technical report describes the training of nomic-embed-text-v1, the first fully reproducible, open-source, open-weights, open-data, 8192 context length English text embedding…
GPT4All: An Ecosystem of Open Source Compressed Language Models
Yuvanesh Anand, Zach Nussbaum, Adam Treat +6
Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind…