datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
bfcl-v1-non-live-ast-parsed
[PARSED] BFCL V1 AST (non-live python)
The data in this dataset is a subset of the original gorilla-llm/Berkeley-Function-Calling-Leaderboard
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
simple
no
no
no
tool_calls
400
multiple
no
no
yes
tool_calls
200
parallel
no
yes
no
tool_calls
200
parallel_multiple
no
yes
yes
tool_calls
200
This is a re-parsing formatting dataset for Python AST parts from V1 of the official dataset of… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/bfcl-v1-non-live-ast-parsed.live-linguist-easylanguage-sft
Live Linguist — Easy-Language SFT dataset
Supervised pairs of spoken/complex → easy-language rewrites across 29 language
locales, used to fine-tune the Live Linguist on-device simplifier. Each locale follows its
own national / European Easy-to-Read or plain-language standard. Records are chat-format
(messages: system/user/assistant) with matching *.pairs.jsonl (source/target/lang).
locale
register
train
valid
test
de
German (Leichte Sprache)
1917
235
200
fr
French… See the full description on the dataset page: https://huggingface.co/datasets/ndgold/live-linguist-easylanguage-sft.LiveClawbench-trajectoriesLiveClawBench: Benchmarking LLM Agents on Complex, Real-World Assistant Tasks
Overview
LLM agents are increasingly expected to handle real-world assistant tasks — booking flights, managing emails, debugging code, curating knowledge bases — yet existing benchmarks evaluate them under isolated difficulty sources. LiveClawBench addresses this gap by introducing a Triple-Axis Complexity Framework and building a benchmark of 134 manually constructed tasks with explicit factor… See the full description on the dataset page: https://huggingface.co/datasets/Mosi-AI/LiveClawbench-trajectories.livesweagent-devstral2-123b-swebench-verified
Live-SWE-agent + Devstral 2 (123B) SWE-bench Verified Trajectories
This dataset contains agent trajectories from running Devstral 2 (123B) via OpenRouter on the SWE-bench Verified benchmark using the Live-SWE-agent framework.
⚠️ Framework Note
This run uses Live-SWE-agent, NOT standard mini-swe-agent. Live-SWE-agent is a self-evolving agent framework that encourages the model to create custom Python tools during runtime.
Key differences from mini-swe-agent:
Self-evolving… See the full description on the dataset page: https://huggingface.co/datasets/pankajmathur/livesweagent-devstral2-123b-swebench-verified.livegraph-matched-benchmark
LiveGraph Matched Graph-vs-Linear Benchmark
This release contains the frozen synthetic datasets used by LiveGraph: Typed
Graphs versus Linear Traces. It supports a
controlled comparison between a typed-graph rendering and a chronological
linear-trace rendering of the same Boolean relational-propagation records.
Main matched benchmark
data/semantic_train.jsonl: 480 semantic training records, depths 1--10.
data/semantic_heldout.jsonl: 3,200 clean held-out records… See the full description on the dataset page: https://huggingface.co/datasets/nutshells3/livegraph-matched-benchmark.livecodebench-plus
LiveCodeBench-v6-Plus
A curated coding benchmark of 91 problems selected by hardness/discrimination
(lcb-v6-plus). It combines two sources, all in one clean schema:
64 evolved problems — mutated/evolved variants from LiveCodeBench-v6
(each carries its seed_problem).
27 original problems — un-evolved AtCoder problems taken directly from
livecodebench/code_generation_lite
release v6 (seed_problem is null).
About BenchEvolver
The evolved problems were produced by… See the full description on the dataset page: https://huggingface.co/datasets/BenchEvolver/livecodebench-plus.LiveVQA-2025livebench
LEM LiveBench Results
LiveBench evaluation results for LEM (Lethean Ethical Model) fine-tuned models.
These models were trained using LoRA on the LEK-1 (Lethean Ethics Kernel) curriculum — axiom-based ethical alignment via weight-level training, not system prompts.
Results Summary
Category
LEM-Gemma3-4B
LEM-Gemma3-12B
Instruction Following
43.5
52.2
Data Analysis
30.4
21.7
Language
4.3
15.3
Math
8.6
5.0
Reasoning
4.6
10.6
Coding
—
10.1
Average
18.3… See the full description on the dataset page: https://huggingface.co/datasets/lthn/livebench.Wiki_Live_Challenge
Wiki Live Challenge Dataset
[English | 中文]
English
📖 Dataset Overview
This is the official dataset accompanying the Wiki Live Challenge benchmark. It contains Wikipedia Good Articles (GAs) as ground truth and research articles generated by leading deep research AI systems.
Wiki Live Challenge is the first live benchmark for evaluating Deep Research Agents (DRAs) on their ability to generate Wikipedia-quality articles. Unlike static benchmarks, Wiki Live… See the full description on the dataset page: https://huggingface.co/datasets/muset-ai/Wiki_Live_Challenge.LiveCodeBench-CodeGenerationLiveEvalBench
LiveEvalBench
Paper | Code Repository
LiveEvalBench is a benchmark for evaluating LLM-generated frontend code (Vue / React / static HTML). Each evaluation actually runs the generated project locally and drives a multi-role agent panel against the live app. This repository hosts the benchmark dataset (the queries and their grading rubrics); the evaluation framework code lives in the LiveEvalBench code repository.
Dataset Summary
100 user requests (queries) for… See the full description on the dataset page: https://huggingface.co/datasets/wyysteelhead/LiveEvalBench.livecodebench-merging-leaderboard
LiveCodeBench v6 Evaluation Leaderboard
Evaluation results for cross-capability merging of OLMo-3 and OLMo-3.1 RL-Zero models on 454 coding problems.
Evaluation
We followed the evaluation guidelines and prompts from OLMo 3. Best effort was made to ensure reported numbers are as accurate as possible.
Code: pmahdavi/modal-eval
Leaderboard
Model
pass@4
pass@1
Loop Rate
Qwen/Qwen3-4B-Thinking-2507
54.6%
45.4%
0.4%
pmahdavi/Olmo-3-7B-Think-Math-Code… See the full description on the dataset page: https://huggingface.co/datasets/pmahdavi/livecodebench-merging-leaderboard.livecodebench
LiveCodeBench for Code-LLaVA
This dataset contains the LiveCodeBench code generation benchmark prepared for
Code-LLaVA evaluation.
Source
Paper: LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
Repository: https://github.com/LiveCodeBench/LiveCodeBench
Version: release_v6 (May 2023 - Apr 2025, 1055 problems)
Dataset Structure
Two configurations are available:
memwrap: Problems with <|memory_start|> /… See the full description on the dataset page: https://huggingface.co/datasets/tonychenxyz/livecodebench.browserbench-live-expanded
BrowserBench Live Expanded
Live-browser tasks derived only from the starting_url field of
Halluminate/BrowserBench at revision
aa56ce5e6331425c29878037fa8c169507deecdc.
Each source page was revisited live at 800×600. Up to three tasks were generated
from the current page observation: information, navigation, and interaction.
Historical prompts, results, screenshots, and ground-truth URLs were not used.
The canonical dataset contains all 876 task slots from 292 source pages. A… See the full description on the dataset page: https://huggingface.co/datasets/merve/browserbench-live-expanded.ATX-Swift-Qwen3.8-27B-Uncensored-IQ4_XS-M-LiveCodeBench-v6
LiveCodeBench v6 — ATX Swift Qwen3.8-27B Uncensored IQ4_XS-M
Public reproducibility package for a four-seed direct code-generation evaluation of
jakeatx/ATX-Swift-Qwen3.8-27B-Uncensored-IQ4_XS-M-GGUF.
Result
357/400 = 89.25% pass@1 across four 100-task seeds. The arithmetic mean of the four seed rates is also 89.25%.
Qwen's published BF16 LiveCodeBench v6 figure is 90.3%; this run is 1.05 percentage points lower.
seed
passed
pass rate
0
90/100
90.00%… See the full description on the dataset page: https://huggingface.co/datasets/jakeatx/ATX-Swift-Qwen3.8-27B-Uncensored-IQ4_XS-M-LiveCodeBench-v6.live-streaming-content
直播内容训练数据集
数据集简介
这个数据集包含直播记录的对话训练数据，适用于：
微调语言模型以生成直播风格的内容
构建直播内容问答系统
RAG (检索增强生成) 系统训练
数据格式
1. SFT 格式 (training_sft.jsonl)
对话式训练数据，适用于监督微调：
{
"messages": [
{"role": "system", "content": "你是一个专业的直播内容助手..."},
{"role": "user", "content": "请介绍一下..."},
{"role": "assistant", "content": "..."}
]
}
2. RAG 格式 (training_rag.jsonl)
问答对格式，包含上下文：
{
"question": "...",
"context": "...",
"answer": "...",
"metadata": {...}
}… See the full description on the dataset page: https://huggingface.co/datasets/licco123/live-streaming-content.fiction_dot_live
Fiction.live Public Stories
This dataset contains public story metadata and story text from Fiction.live, exported as zstd-compressed Parquet files. The collection covers active, finished, and hiatus stories across teen, mature, unrated, and NSFW content ratings.
The dataset includes adult and user-generated content. Downstream users should filter by content_rating, tags, and story metadata as appropriate for their use case.
Files
File
Rows
Description… See the full description on the dataset page: https://huggingface.co/datasets/trentmkelly/fiction_dot_live.HuChenFeng-live-dialogue
HuChenFeng Live Dialogue Dataset
Dataset Introduction
This dataset contains dialogue data from Hu Chenfeng's live streaming room, suitable for tasks such as Chinese dialogue generation and chatbot training.
Dataset Statistics
Total Samples: ~200K dialogue turns
Language: Chinese
Format: Parquet, JSONL
License: MIT License
Data Format
Each data sample includes a messages field, which stores the dialogue history:
{
"messages": [
{… See the full description on the dataset page: https://huggingface.co/datasets/wangzh12023/HuChenFeng-live-dialogue.LiveCodeBench-EvoSyn
EvoSyn-LiveCodeBench: Evolutionary Synthesized Coding Problems
Dataset Description
This dataset contains 231 high-quality coding problems synthesized and filtered using the EvoSyn framework.
Each problem includes diverse and reliable unit tests, specifically designed for reinforcement learning with verifiable rewards (RLVR).
Data Fields
We've adapted the original LiveCodeBench dataset structure, placing all unit tests into the public_test_cases field. This… See the full description on the dataset page: https://huggingface.co/datasets/Elynden/LiveCodeBench-EvoSyn.uk-live-music-blog-corpus
UK Live Music Blog & Guides Corpus
109 long-form articles on the UK live music industry, published by GigXchange under CC BY 4.0. Written by working musicians and venue operators — not a content farm.
Overview
Metric
Value
Articles
109
Total words
305,841
Avg words/article
2,806
FAQ pairs
761
Topics
9
Date range
2026-03-01 to 2026-08-09
Language
British English (en-GB)
Domain
UK live music booking, fees, contracts, venues, city scenes… See the full description on the dataset page: https://huggingface.co/datasets/gigxchange/uk-live-music-blog-corpus.ukraine-liveblog
Dataset Card
Dataset Summary
The "ukraine-liveblog" dataset contains a collection of news articles published on the liveblog of the popular German news website, tagesschau.de. The dataset covers the period from February 2022 to February 2023, and includes every news feed published during this time that covers the ongoing war in Ukraine.
Supported Tasks and Leaderboards
--
Languages
The language of the dataset is German.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/pstuerner/ukraine-liveblog.Project-Kyra-LiveStreams-RAWDate of scrape: 17/04/2024
4666 streams from 17 different vtubers
Transcribed by YouTube auto captions
This data has somewhat been cleaned:
Removed all under 12 chars
Removed most duplicates
Merged multiple lines together
This will probably need to be processed further to be used
Channels Scraped
Vtuber Name
Scraped Channel ID
Tenma
UC3K7pmiHsNSx1y0tdx2bbCw
Kirsche
UC4uVQv3D4-DtUQOvG1Fo6Jg
Chibidoki
UC6eWvFoYe5nOia-hyPxFPZw
Akuma Nihmune
UC79Ekr3umIkWmgZ-an01LfQ… See the full description on the dataset page: https://huggingface.co/datasets/kyratron/Project-Kyra-LiveStreams-RAW.LiveCodeBench-v6-R182
LiveCodeBench-v6-R182
The 182 problems obtained by taking the release_v6 slice of livecodebench/code_generation_lite and keeping only those with contest_date >= 2025-01-01 (contest dates span 2025-01-04 to 2025-04-06).
Usage
from datasets import load_dataset
ds = load_dataset("jwu323/LiveCodeBench-v6-R182", split="test")
print(ds[0]["question_title"], ds[0]["contest_date"])
Suggestions
Train Dataset Topics
mixed output (480)
emergency call pain location (body) ∼ 20
emergency call callback number of reporter ∼ 20
emergency call personal information (age, name, sex, ...) ∼ 20
fire department call person trapped in car after accident ∼ 20
emergency call geographical location description (country, area, city, street, house number, etc...) ∼ 10
vacation geographical location description ∼ 10
business transport geographical location description ∼ 10… See the full description on the dataset page: https://huggingface.co/datasets/LiveReader/Suggestions.livestockllama