Tools 12
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque aliquet consectetur vulputate. Donec posuere sapien vitae metus dapibus pretium. Praesent vulputate porta eros, eu venenatis odio tincidunt quis. Cras rhoncus elementum nisl, sed ultricies sapien viverra quis. Ut eu nulla id v
OpenCodeReasoning is the largest reasoning-based synthetic dataset to date for coding, comprises 735,255 samples in Python across 28,319 unique competitive programming questions. OpenCodeReasoning is designed for supervised fine-tuning (SFT).
Curabitur venenatis et risus in hendrerit. Phasellus vitae placerat dolor. Praesent sit amet lorem nibh. Nulla arcu elit, mollis eu interdum non, vestibulum id augue. Sed posuere eu
em ipsum dolor sit amet, consectetur adipiscing elit. Cras a risus est. Proin risus nunc, cursus id est at, consectetur eleifend nisi. Morbi nunc eros, au
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Quisque aliquet consectetur vulputate. Donec posuere sapien vitae metus dapibus pretium.
We introduce MegaMath, an open math pretraining dataset curated from diverse, math-focused sources, with over 300B tokens. MegaMath is curated via the following three efforts:]\
mattis orci erat dignissim diam. Morbi eleifend magna venenatis libero fermentum molestie. Phasellus mollis sapien a risus ornare scelerisque. Nulla trist
is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centu
Since the advent of reasoning-based large language models, many have found great success from distilling reasoning capabilities into student models. Such techniques have significantly bridged the gap between reasoning and standard LLMs on coding tasks. Despite this, much of the progress on distilling reasoning models remains locked behind proprietary datasets or lacks details on data curation, filtering and subsequent
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities—all while activating only 2.8B parameters in its language decoder (Kimi-VL-A3B).
Sed euismod enim quis nunc dapibus fringilla. Nulla feugiat dui quis lorem lacinia malesuada. Nulla sed orci ac eros lobortis fermentu. Pellentesque porttito