Researchers trained a Deep Research agent with 32 H100s and open-sourced everything
Mirrored from r/LocalLLaMA for archival readability. Support the source by reading on the original site.
| Ohio State University's NLP team released QUEST-35B, an open-source Deep Research agent trained using ~32 H100s and ~8K synthetic samples. The team open-sourced the training recipe, code, weights and datasets. Benchmark results show competitive performance against several frontier Deep Research systems. What do you think is the biggest remaining gap between open-source Deep Research agents and frontier closed systems? Source: Professor Yusu [link] [comments] |
More from r/LocalLLaMA
-
Palantir CEO rages against closed models
Jul 2
-
SenseNova-U1-8b-MoT-Infographic-V2 (released yesterday) - An open source SOTA beast for infographic design and image editing.
Jul 2
-
[Benchmark] Kimi K2.7 Code Q3 on Mac Studio M3 Ultra + RTX PRO 6000 over llama.cpp RPC: prefill improves, no changes in token generation/decode
Jul 2
-
They fit! Mostly.... 2x 3090, Thermaltake Core p3
Jul 2
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.