datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
agent-llm-traces-v2
Exgentic Agent LLM Traces v2 — Agent Chat Only
OpenTelemetry-shaped execution traces for 10,057 agent runs across 6 benchmarks (AppWorld, SWE-bench, BrowseCompPlus, τ²-bench Airline/Retail/Telecom), filtered to the agent under test's chat-only LLM calls. This is the dataset for replay testing, behavioral analysis, or any task where you care about what the benchmarked model actually did — not the eval scaffolding around it.
This v2 release expands upon Exgentic/agent-llm-traces… See the full description on the dataset page: https://huggingface.co/datasets/Exgentic/agent-llm-traces-v2.agentic-llm-pretraining-1.7b
Agentic LLM Pretraining Dataset
A pretraining corpus for small language models (1-3B parameters) optimized for agentic tasks. The corpus emphasizes learning to comprehend language, reason, follow instructions, and use tools over memorizing factual knowledge — the assumption is that domain knowledge will be provided at runtime via RAG. The idea is that this could enable much smaller pretraining corpora by omitting the large volumes of text typically needed to memorize facts.… See the full description on the dataset page: https://huggingface.co/datasets/visionscaper/agentic-llm-pretraining-1.7b.CriticBench
Dataset Card for Dataset Name
CriticBench is a comprehensive benchmark designed to assess LLMs' abilities to generate, critique/discriminate and correct reasoning across a variety of tasks. CriticBench encompasses five reasoning domains: mathematical, commonsense, symbolic, coding, and algorithmic. It compiles 15 datasets and incorporates responses from three LLM families.
Dataset Details
Dataset Description
Curated by: THU
Funded by [optional]: [More… See the full description on the dataset page: https://huggingface.co/datasets/llm-agents/CriticBench.llm-rag-agent-papers
llm-rag-agent-papers
Research papers on LLM, RAG, and AI Agents - Knowledge base for RAG pipeline
Dataset Structure
This dataset contains three subsets:
llm: Large Language Model related content
rag: Retrieval-Augmented Generation related content
agent: AI Agent related content
Usage
from datasets import load_dataset
# Load all subsets
dataset = load_dataset("GXMZU/llm-rag-agent-papers")
# Load specific subset
llm_data =… See the full description on the dataset page: https://huggingface.co/datasets/GXMZU/llm-rag-agent-papers.llm-rag-agent-blogs
llm-rag-agent-blogs
Technical blogs on LLM, RAG, and AI Agents - Knowledge base for RAG pipeline
Dataset Structure
This dataset contains three subsets:
llm: Large Language Model related content
rag: Retrieval-Augmented Generation related content
agent: AI Agent related content
Usage
from datasets import load_dataset
# Load all subsets
dataset = load_dataset("GXMZU/llm-rag-agent-blogs")
# Load specific subset
llm_data =… See the full description on the dataset page: https://huggingface.co/datasets/GXMZU/llm-rag-agent-blogs.agentic-llm-pretraining-1.7b
Agentic LLM Pretraining Dataset
A pretraining corpus for small language models (1-3B parameters) optimized for agentic tasks. The corpus emphasizes learning to comprehend language, reason, follow instructions, and use tools over memorizing factual knowledge — the assumption is that domain knowledge will be provided at runtime via RAG. The idea is that this could enable much smaller pretraining corpora by omitting the large volumes of text typically needed to memorize facts.… See the full description on the dataset page: https://huggingface.co/datasets/travisp83/agentic-llm-pretraining-1.7b.