datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
AFM-WebAgent-SFT-Dataset
Data Introduction
This dataset serves as the core training data for Agent Foundation Models (AFMs), specifically designed to elicit end-to-end multi-agent reasoning capabilities in large language models. Built on the novel "Chain-of-Agents (CoA)" paradigm, the dataset leverages a multi-agent distillation framework to transform collaboration processes from state-of-the-art multi-agent systems into trajectory data suitable for supervised fine-tuning (SFT), simulating dynamic… See the full description on the dataset page: https://huggingface.co/datasets/PersonalAILab/AFM-WebAgent-SFT-Dataset.lumos_web_agent_plan_iterative
🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
🌐[Website]
📝[Paper]
🤗[Data]
🤗[Model]
🤗[Demo]
We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
Lumos has following features:
🧩 Modular Architecture:
🧩 Lumos consists of planning, grounding… See the full description on the dataset page: https://huggingface.co/datasets/ai2lumos/lumos_web_agent_plan_iterative.lumos_web_agent_ground_iterative
🪄 Agent Lumos: Unified and Modular Training for Open-Source Language Agents
🌐[Website]
📝[Paper]
🤗[Data]
🤗[Model]
🤗[Demo]
We introduce 🪄Lumos, Language Agents with Unified Formats, Modular Design, and Open-Source LLMs. Lumos unifies a suite of complex interactive tasks and achieves competitive performance with GPT-4/3.5-based and larger open-source agents.
Lumos has following features:
🧩 Modular Architecture:
🧩 Lumos consists of planning, grounding… See the full description on the dataset page: https://huggingface.co/datasets/ai2lumos/lumos_web_agent_ground_iterative.embodied_web_agent_outdoor_trajectorywebagent-r1-distillkoen-web-agent-sft-mixes
Korean-English Web Agent SFT Mixes
브라우저 GUI 에이전트 SFT 용 한국어·영어 혼합 데이터. 언어 비율만 다르고 나머지는
동일하게 통제된 4개 구성이라, 비율이 성능에 미치는 영향을 직접 비교할 수 있다.
구성
config
ko : en
스텝
궤적
ko_only_20k
10 : 0
20,001
3,042
mix_ko_en_5050
5 : 5
20,008
2,663
mix_ko_en_2080
2 : 8
20,003
2,436
en_only_20k
0 : 10
20,009
2,274
비율은 궤적 수가 아니라 스텝 수 기준이다. 스텝 하나가 학습 샘플 하나인데
한국어 궤적은 평균 6.6스텝, 영어는 8.7스텝이라, 궤적 수로 5:5 를 맞추면 실제
gradient 기여가 5:5 가 되지 않는다. 궤적은 절대 쪼개지 않는다.
네 구성의 표본은 서로 중첩된다.… See the full description on the dataset page: https://huggingface.co/datasets/GUIAgentt/koen-web-agent-sft-mixes.web-agent-titles