datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
glaive-function-calling-v2glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
glaive-function-callingThis dataset consists of 52k samples generated through Glaive for the task of function calling, in the following format-
SYSTEM: You are an helpful assistant who has access to the following functions to help the user, you can use the functions if needed-
{
JSON function definiton
}
USER: user message
ASSISTANT: assistant message
Function call invocations are formatted as-
ASSISTANT: <functioncall> {json function call}
Response to the function call is formatted as-
FUNCTION RESPONSE: {json… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/glaive-function-calling.glaive-function-calling-v2-sharegptThe glaive-function-calling-v2 dataset in sharegpt format.
You can use it in LLaMA Factory by specifying --dataset glaive_toolcall_100k.
glaive-function-calling-v2-llama-factory-convertThis is a converted dataset for https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2 that allows sft in https://github.com/hiyouga/LLaMA-Factory for function calling fine tuning.
You need to add the following to the datasets.json file, and changed the file_name to your local path.
"glaive-function-calling-v2": {
"file_name": "./glaive-function-calling-v2/simple-function-calling-v2_converted.json",
"columns": {
"prompt": "instruction",
"query": "input"… See the full description on the dataset page: https://huggingface.co/datasets/Yhyu13/glaive-function-calling-v2-llama-factory-convert.glaive-code-assistant-v3
Glaive-code-assistant-v3
Glaive-code-assistant-v3 is a dataset of ~1M code problems and solutions generated using Glaive’s synthetic data generation platform.
This is built on top of the previous version of the dataset that can be found here. This already includes v1 and v2 of the dataset.
To report any problems or suggestions in the data, join the Glaive discord
glaive-code-assistant
Glaive-code-assistant
Glaive-code-assistant is a dataset of ~140k code problems and solutions generated using Glaive’s synthetic data generation platform.
The data is intended to be used to make models act as code assistants, and so the data is structured in a QA format where the questions are worded similar to how real users will ask code related questions.
The data has ~60% python samples.
To report any problems or suggestions in the data, join the Glaive discord
glaive-function-calling-v2Modified version of the glaiveai/glaive-function-calling-v2 dataset
All samples in the glaive dataset is converted into the following format for better interoperability
[
{
"role":"system",
"content":"You are a helpful assistant with access to the functions.",
"functions":[
{
"name":"generate_password",
"description":"Generate a random password with specified criteria",
"parameters":{… See the full description on the dataset page: https://huggingface.co/datasets/Dulsara/glaive-function-calling-v2.glaive-code-assistant-v2
Glaive-code-assistant-v2
Glaive-code-assistant-v2 is a dataset of ~215k code problems and solutions generated using Glaive’s synthetic data generation platform.
This is built on top of the previous version of the dataset that can be found here
To report any problems or suggestions in the data, join the Glaive discord
evalarc-casebook
EvalArc Casebook
The same 93.75% score can pass one acceptance gate and fail another.
Inspect the rules, actual failed checks and original Docker records in a
filterable table. This is the data companion to the
interactive evidence lab.
In the default suite_jobs view, compare support-partial and
support-protected. Both use the same frozen defective policy, score 93.75%
and fully resolve 0/2 attempts. The deliberately permissive rule accepts partial
progress; the rule requiring… See the full description on the dataset page: https://huggingface.co/datasets/glayguo/evalarc-casebook.glaive-function-calling-vicuna数据集格式说明： glaiveai/glaive-function-calling · Datasets at Hugging Face 的 SFT 格式
我们高兴地宣布，数据集 "glaiveai/glaive-function-calling" 已经根据 SFT（Supervised Fine-Tuning）的需求进行了格式转换，以支持大型语言模型的训练。以下是有关这一新格式的简要说明：
数据集概述:
数据集 "glaiveai/glaive-function-calling" 基于 CC-BY-4.0 协议发布，原始数据集包含标识符和对话信息，这些数据已被转换为适应 SFT 训练的结构。
数据格式： 转换后的数据集格式包含以下关键信息：
id: 整数类型的标识符，用于唯一标识每个数据样本。
conversations: 一个数组，其中包含对话信息。每个对话可以由多个句子组成，以更好地呈现函数调用的上下文。
数据集用途：转换后的数据集适用于 SFT 的训练，主要用途包括但不限于：
函数调用理解：… See the full description on the dataset page: https://huggingface.co/datasets/Deepexi/glaive-function-calling-vicuna.glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
noteflow-research-pilots
Keep the failed attempts. Check the artifact.
Versioned public development evidence from Robot Reel × Skills Anywhere × EvalArc, recorded 14 September 2026 on an NVIDIA L40S, with separate scripted Harbor controls on CPU and separate GPU context-control and agent-requested MCP handoff cohorts recorded 19 September 2026. This is an inspectable engineering casebook, not a held-out benchmark or training corpus with established efficacy.
Configuration
Actual experiment
What… See the full description on the dataset page: https://huggingface.co/datasets/glayguo/noteflow-research-pilots.godot_4_docsDataset generated for Godot 4 docs using Glaive.
glaucoma-expert-cot-raw-1077
Glaucoma Expert Chain-of-Thought
Ophthalmologist six-step reasoning reports for fundus photographs, each paired with
a binary glaucoma label. 1,074 cases from LAG and Papila.
Files
file
rows
split
expert_cot_trainval.jsonl
915
train (823) + val (92)
expert_cot_test.jsonl
159
test
images/
1,074
<source>_<id>.jpg
Record schema
{
"id": "1689",
"source": "LAG",
"image": "LAG_1689.jpg",
"split": "train"… See the full description on the dataset page: https://huggingface.co/datasets/yuzhench/glaucoma-expert-cot-raw-1077.pine-of-glass-sessions
Coding agent session traces for thomasmustier/pine-of-glass-sessions
This dataset contains redacted coding agent session traces collected while working on tmustier/pine-of-glass. The traces were exported with pi-share-hf from a local pi workspace. The traces were filtered to keep only sessions that passed deterministic redaction and LLM review.
Data description
Each *.jsonl file is a redacted pi session. Sessions are stored as JSON Lines files where each line is a… See the full description on the dataset page: https://huggingface.co/datasets/thomasmustier/pine-of-glass-sessions.test-glaiveai-function-callingRAG-v1
Glaive-RAG-v1
Glaive-RAG-v1 is a dataset with ~50k samples built using the Glaive platform, for finetuning models for RAG use cases.
Each row has:
List of documents for context
Question
Answer Mode
Answer
The answer mode is to define if the model should output only grounded responses or if it should combine it's internal information as well.
The answers have Cited documents at the beginning and also <co: 1> tags in the text to mark citations.
To report any problems or suggestions… See the full description on the dataset page: https://huggingface.co/datasets/glaiveai/RAG-v1.glaive_toolcall_zhBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
Translated by GPT-3.5.
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_zh.
fin-glaive
Fin-Glaive: 645K Financial Instruction and Reasoning Examples
Fin-Glaive is a large-scale English dataset for financial instruction tuning, financial question answering, and reasoning-focused language-model post-training. It contains 645,232 question–reasoning–answer examples mined from Glaive Reasoning v1 20M.
The dataset and its role in the post-training pipeline are described in Data-Centric Post-Training for Financial Reasoning: Mining, Distillation, and Verifiable Learning.… See the full description on the dataset page: https://huggingface.co/datasets/whoisjiji/fin-glaive.glaive-function-calling-v2-sharegptglaive-function-calling-v2-ko
Original Dataset: glaiveai/glaive-function-calling-v2
ChatGPT를 이용해서 번역, 전체 데이터셋 중 15000개만 번역됨
Prompt:
You are a Korean translator. Data in the format of a given json array contains conversations between user and assistant. Each element in the array has roles and contents. You must translate the content value of the element when the role is user or assistant. You must also meet the following conditions.
1. The result must be preserved in json format.
2. The tone of the translated text should… See the full description on the dataset page: https://huggingface.co/datasets/heegyu/glaive-function-calling-v2-ko.glaive-function-calling-v2-ru-parsed
Glaive Function Calling V2 Parsed (RU)
This dataset is a fork of an original mizinovmv/glaive-function-calling-v2-ru.
Structure
train (102860 rows)
messages
functions
test (9681 rows)
messages
functions
Messages field contains JSON string with list of messages. Each message has role (one of: system, user, assistant, function_call, function_response) and content (message string) fields.
Functions field contains JSON string with list of functions for current chat… See the full description on the dataset page: https://huggingface.co/datasets/korotkov/glaive-function-calling-v2-ru-parsed.ovos-wake-word-bench-picovoice-view-glass
OVOS wake_word bench — picovoice-view-glass
Per-clip detection decisions predictions of the registered
OVOS Plugin Arena
wake_word fighters over
Picovoice/wake-word-benchmark.
One dedicated repo per modality; one dataset split per language; one JSONL
file per fighter under predictions/<lang>/<competitor_id>.jsonl. Rows follow
the arena §3.2 contract (pinned dataset_revision, plugin_version,
latency_ms). Produced by the reproducible benchmark script in the arena repo;
the arena's… See the full description on the dataset page: https://huggingface.co/datasets/OpenVoiceOS/ovos-wake-word-bench-picovoice-view-glass.glaive_agentive_tool_callingstartup-interviewsglaive-function-calling-v2-parsed
Glaive Function Calling V2 Parsed
This dataset is a fork of an original - glaiveai/glaive-function-calling-v2.
Structure
train (100199 rows)
messages
functions
test (12553 rows)
messages
functions
Messages field contains JSON string with list of messages. Each message has role (one of: system, user, assistant, function_call, function_response) and content (message string) fields.
Functions field contains JSON string with list of functions for current chat (their… See the full description on the dataset page: https://huggingface.co/datasets/korotkov/glaive-function-calling-v2-parsed.shlyokavitsa-pairs
Shlyokavitsa → Cyrillic restoration pairs
210,236 (Latin, Cyrillic) phrase pairs for restoring shlyokavitsa (Bulgarian typed on a
Latin keyboard) back into Cyrillic. Built from Bulgarian Wikipedia, so it can be shared
under the same licence as its source.
{"latin": "sreshta se na dalbochina okolo", "cyrillic": "среща се на дълбочина около", "n_words": 5, "page_id": 1041}
Filed under translation because that is the closest category the Hub offers, but the task is
script… See the full description on the dataset page: https://huggingface.co/datasets/glassbox/shlyokavitsa-pairs.evalarc-independent-swe
Independent-source SWE workflow records
36 Qwen3-8B attempts compare four fixed workflows on three public SWE-bench
Verified tasks. There are no accepted attempts: 31 have assessable native
reports and five retain an upstream infrastructure flag, so their task outcome
is uncertain. Eight attempts produced nonempty patches. One generation request
has incomplete usage.
Inspect the interactive report
· English method · 中文方法
· Offline review and exact raw records
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/glayguo/evalarc-independent-swe.Multi-Turn-Instruct
Multi-Turn-Instruct Dataset
Dataset introduced in paper "Can Language Models Follow Multiple Turns of Entangled Instructions?"
📌 Overview
This repository contains the dataset, evaluation code, and benchmarks for the Multi-Turn-Instructdataset introduced in:
Can Language Models Follow Multiple Turns of Entangled Instructions?Chi Han, Xin Liu, Haodong Wang, Shiyang Li, Jingfeng Yang, Haoming Jiang, Zhengyang Wang, Qingyu Yin, Liang Qiu, Changlong Yu, Yifan Gao, Zheng… See the full description on the dataset page: https://huggingface.co/datasets/Glaciohound/Multi-Turn-Instruct.