datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SWE-Fixer-Train-110K
SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution
📃 Paper |
🚀 GitHub
SWE-Fixer is a simple yet effective solution for addressing real-world GitHub issues by training open-source LLMs. It features a streamlined retrieve-then-edit pipeline with two core components: a code file retriever and a code editor.
This repo holds the data SWE-Fixer-Train-110K we curated for SWE-Fixer training.
For more information, please visit our project page.… See the full description on the dataset page: https://huggingface.co/datasets/internlm/SWE-Fixer-Train-110K.WildClawBench-TrajectoriesWildClawBench Trajectories
Complete OpenClaw agent trajectories from the WildClawBench evaluation — every message, reasoning block, tool call, and tool result from real long-horizon agent runs, released for independent verification, side-by-side comparison, and trace-level analysis.
Each evaluated model covers the full 60-task suite, and the collection is continuously updated as new models join the leaderboard. The directories under sessions/ always reflect the current model roster.… See the full description on the dataset page: https://huggingface.co/datasets/internlm/WildClawBench-Trajectories.InteractScience
InteractScience: Programmatic and Visually-Grounded Evaluation of Interactive Scientific Demonstration Code Generation
InteractScience is a benchmark specifically designed to evaluate the capability of large language models in generating interactive scientific demonstration code. This project provides a complete evaluation pipeline including model inference, automated testing, and multi-dimensional assessment.
📊 Dataset… See the full description on the dataset page: https://huggingface.co/datasets/internlm/InteractScience.Condor-SFT-20K
Condor
✨ Introduction
[🤗 HuggingFace Models]
[🤗 HuggingFace Datasets]
[📃 Paper]
The quality of Supervised Fine-Tuning (SFT) data plays a critical role in enhancing the conversational capabilities of Large Language Models (LLMs).
However, as LLMs become more advanced,
the availability of high-quality human-annotated SFT data has become a significant bottleneck,
necessitating a greater reliance on synthetic training data.
In this work, we introduce… See the full description on the dataset page: https://huggingface.co/datasets/internlm/Condor-SFT-20K.