datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Berkeley-Function-Calling-Leaderboard
Berkeley Function Calling Leaderboard
The Berkeley function calling leaderboard is a live leaderboard to evaluate the ability of different LLMs to call functions (also referred to as tools).
We built this dataset from our learnings to be representative of most users' function calling use-cases, for example, in agents, as a part of enterprise workflows, etc.
To this end, our evaluation dataset spans diverse categories, and across multiple languages.
Checkout the Leaderboard at… See the full description on the dataset page: https://huggingface.co/datasets/gorilla-llm/Berkeley-Function-Calling-Leaderboard.llm_pt_leaderboard_raw_resultsrequests
Open LLM Leaderboard Requests
This repository contains the request files of models that have been submitted to the Open LLM Leaderboard.
You can take a look at the current status of your model by finding its request file in this dataset. If your model failed, feel free to open an issue on the Open LLM Leaderboard! (We don't follow issues in this repository as often)
Evaluation Methodology
The evaluation process involves running your models against several benchmarks from… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard-old/requests.requestsknowledge-base
RL-for-LLMs Wiki
An expert-level, citation-backed knowledge base on reinforcement learning for
large language models — RLHF, DPO and offline preference optimization, reward
modeling, RLVR and reasoning, training systems, and the failure modes — built
collaboratively by autonomous agents. Each topic article is a deep dive written
so you can learn the topic from it without reading the underlying papers, with
every non-obvious claim cited to a source. Every change lands through a… See the full description on the dataset page: https://huggingface.co/datasets/rl-llm-wiki/knowledge-base.resultsrequestsllm-network-study-data
LLM-Network-Study-Data
Per-request network captures (.pcapng) collected by the
LLM-Network-Study benchmark harness (benchmark.py and the
per-workload test scripts). Each directory holds one capture file per request,
named request_<id>_run<n>_<timestamp>.pcapng.
A directory name encodes four dimensions:
<capture-env>_<provider/model>_<workload>[_<dataset/variant>]_results
Dimension legend
Dimension
Values
Meaning
Capture env
ethernet
Wired connection to… See the full description on the dataset page: https://huggingface.co/datasets/wayslab/llm-network-study-data.requestspost-ocr2leaderboard-resultsopc-sft-stage2
OpenCoder Dataset
The OpenCoder dataset is composed of the following datasets:
opc-sft-stage1: the sft data used for opencoder sft-stage1
opc-sft-stage2: the sft data used for opencoder sft-stage2 <-- you are here
opc-annealing-corpus: the synthetic data & algorithmic corpus used for opencoder annealing
opc-fineweb-code-corpus: the code-related page recalled from fineweb
opc-fineweb-math-corpus: the math-related page recalled from finewebrefineCode-code-corpus-meta: the meta-data… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2.llm-memoryThis repository contains the results of all experiments (inlcuding every single hyperparameter run) reported in the following paper:
Orhan AE (2023) Recognition, recall, and retention of few-shot memories in large language models. arXiv:2303.17557.
A brief description of the directories included in this repository:
evals: contains the results of all recognition experiments
recalls: contains the results of all recall experiments
re-evals: contains the results of all recognition experiments… See the full description on the dataset page: https://huggingface.co/datasets/eminorhan/llm-memory.contentsAnswerCarefully
AnswerCarefully
概要
AnswerCarefullyは日本語LLM 出力の安全性・適切性に特化したインストラクションデータセットです。
このデータセットは、英語の要注意回答を集めた Do-Not-Answer データセット の包括的なカテゴリ分類に基づき、人手で質問・回答ともに日本語サンプルを集めたオリジナルのデータセットです。
データセットの詳細については、こちらをご覧ください。
Overview
AnswerCarefully is an instruction dataset specifically aimed at ensuring safety and appropriateness of LLM output in Japanese.
This dataset consists of original pairs of questions and reference (safe) responses based on the extensive safety taxonomy proposed in… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/AnswerCarefully.PMC
Data collected from PMC
Only CC-BY, CC-BY-SA licenses are included.
For all records, check the jsonl files in the data folder
turkish-llm-dataset
Turkish Pretraining Corpus
Dataset Description
This dataset is a Turkish pretraining corpus created by combining BellaTurca (excluding ForumSohbetleri), Cosmos-Turkish-Corpus-v1.0, and FineWeb-2 Turkish Categorized, followed by cleaning, normalization, and deduplication. It is intended for the development, training, and evaluation of Turkish language models.
This dataset was prepared as part of a capstone project conducted by a group of students from Sabancı… See the full description on the dataset page: https://huggingface.co/datasets/tascib/turkish-llm-dataset.tm-system_promptdrh-System-Prompt-processedcoda-llm-data
Coda LLM Project & Dataset Repository
This repository contains the full end-to-end dataset, fine-tuning scripts, evaluation suites, load testing harness, and proxy architecture for Coda LLM (Granite-4.2-8B Najdi Sales Agent).
Model Repository: mohameddalii/coda-llm
Dataset / Code Repository: mohameddalii/coda-llm-data
📁 Repository Structure
coda-llm-data/
├── data/
│ ├── raw/ # Raw generated multi-turn dialogues across domains
│ ├──… See the full description on the dataset page: https://huggingface.co/datasets/mohameddalii/coda-llm-data.opc-annealing-corpus
OpenCoder Dataset
The OpenCoder dataset is composed of the following datasets:
opc-sft-stage1: the sft data used for opencoder sft-stage1
opc-sft-stage2: the sft data used for opencoder sft-stage2
opc-annealing-corpus: the synthetic data & algorithmic corpus used for opencoder annealing <-- you are here
fineweb-code-corpus: the code-related page recalled from fineweb
fineweb-math-corpus: the math-related page recalled from finewebrefineCode-code-corpus-meta: the meta-data of… See the full description on the dataset page: https://huggingface.co/datasets/OpenCoder-LLM/opc-annealing-corpus.Bitext-customer-support-llm-chatbot-training-dataset
Bitext - Customer Service Tagged Training Dataset for LLM-based Virtual Assistants
Overview
This hybrid synthetic dataset is designed to be used to fine-tune Large Language Models such as GPT, Mistral and OpenELM, and has been generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools. The goal is to demonstrate how Verticalization/Domain Adaptation for the Customer Support sector can be easily achieved using our two-step approach to LLM… See the full description on the dataset page: https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llm/or-bench.hleuet_iai_nlp_data_for_llmsData sources come from the following categories:
1.Web crawler dataset:
Website UET (ĐH Công nghệ): tuyensinh.uet.vnu.edu.vn; new.uet.vnu.edu.vn
Website HUS (ĐH KHTN): hus.vnu.edu.vn
Website EUB (ĐH Kinh tế): ueb.vnu.edu.vn
Website IS (ĐH Quốc tế): is.vnu.edu.vn
Website Eduacation (ĐH Giáo dục): education.vnu.edu.vn
Website NXB ĐHQG: press.vnu.edu.vnList domain web crawler
CC100:link to CC100 vi
Vietnews: link to bk vietnews dataset
C4_vi: link to C4_vi
Folder Toxic store files demo… See the full description on the dataset page: https://huggingface.co/datasets/group2sealion/uet_iai_nlp_data_for_llms.requests-backupswallow-math-v2
SwallowMath-v2
Resources
📑 arXiv: Read our paper for detailed methodology at arXiv:2505.02881.
🤗 Sister Dataset: Discover SwallowCode2, our companion dataset for code generation.
🧮 What is it?
SwallowMath-v2 is a large-scale mathematical dataset containing 32 billion tokens, developed as the successor to SwallowMath-v1.
Building on the success of v1, this release aims to construct a larger-scale and more permissively licensed corpus to support open and… See the full description on the dataset page: https://huggingface.co/datasets/tokyotech-llm/swallow-math-v2.resultspypi-20241031vlm_results