leonardklin/OpenSciDER-SFT-8K
OpenSciDER This is the model repo for OpenSciDER-SFT-8K, a trajectory dataset curated from SciDER. This dataset is collected from the inference trajectories of the Qwen3.6-27B and OpenSciDER-27B model on DataSciBench, DS-1000, DS-Bench, and ScienceAgentBench. It also contains the benchmark evaluation trajectories on AI-Idea-Bench, AIRS-Bench, AstroVisBench, DiscoveryBench, MLE-Bench, and SciCode. There are 3 configurations in this dataset: openscider: the trajectories… See the full description on the dataset page: https://huggingface.co/datasets/leonardklin/OpenSciDER-SFT-8K.
<div align="center"> <img src="https://github.com/leonardodalinky/SciDER/blob/a089864587cf1df0d97099f8395505e7ed04caa8/static/images/scider_logo.webp?raw=true" width="400" /> </div>
OpenSciDER
This is the model repo for OpenSciDER-SFT-8K, a trajectory dataset curated from SciDER.
This dataset is collected from the inference trajectories of the Qwen3.6-27B and OpenSciDER-27B model on DataSciBench, DS-1000, DS-Bench, and ScienceAgentBench. It also contains the benchmark evaluation trajectories on AI-Idea-Bench, AIRS-Bench, AstroVisBench, DiscoveryBench, MLE-Bench, and SciCode.
There are 3 configurations in this dataset:
openscider: the trajectories of Qwen3.6-27B and OpenSciDER-27B on DataSciBench, DS-1000, DS-Bench, and ScienceAgentBench.benchmarks: the benchmark evaluation trajectories on AI-Idea-Bench, AIRS-Bench, AstroVisBench, DiscoveryBench, MLE-Bench, and SciCode.all: all the trajectories mentioned above.
You can load the dataset with the HuggingFace datasets library:
from datasets import load_dataset
dataset = load_dataset("leonardklin/OpenSciDER-SFT-8K", "openscider")
# or
dataset = load_dataset("leonardklin/OpenSciDER-SFT-8K", "benchmarks")
# or
dataset = load_dataset("leonardklin/OpenSciDER-SFT-8K", "all")