datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hermes-function-calling-v1
Hermes Function-Calling V1
This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models.
This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational scenarios… See the full description on the dataset page: https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1.glaive-function-calling-v2xlam-function-calling-60k
APIGen Function-Calling Datasets
Paper | Website | Models
This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness.
We conducted human evaluation over 600 sampled data points, and… See the full description on the dataset page: https://huggingface.co/datasets/lockon/xlam-function-calling-60k.xlam-function-calling-60k
APIGen Function-Calling Datasets
Paper | Website | Models
This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness.
We conducted human evaluation over 600 sampled data points… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k.xlam-function-calling-60kglaive-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.function-calling-sharegptThis is a dataset for finetuning models on function calling based on glaiveai/glaive-function-calling-v2.
The dataset includes 86,864 examples of chats that include function calling as part of the conversation. The system prompt includes either 0, 1, or 2 functions that the assistant can use, and instructions on how the agent can use it.
Changes include:
Using ShareGPT format for chats
Adding "function_response" as a role
Removing code examples
Removing examples with invalid JSON as function… See the full description on the dataset page: https://huggingface.co/datasets/hypervariance/function-calling-sharegpt.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.
xlam-function-calling-60k-shareGPTShareGPT converted version of Salesforce/xlam-function-calling-60k
Function_Calling_Unfilteredglaive-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.Nemotron-RL-Agentic-Function-Calling-Pivot-v1
Dataset Description:
This is a RL dataset for general function-calling by utilizing existing expert tool-use trajectories. We pose each assistant step of the trajectory as a separate behavior cloning problem where the policy model is incentivized to match the tool call choices of the expert model.
This dataset is released as part of NVIDIA NeMo Gym, a framework for building reinforcement learning environments to train large language models. NeMo Gym contains a growing collection of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Agentic-Function-Calling-Pivot-v1.function_callingsmall_function_callingglaive-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.hermes-function-calling-v1-jsonl
Hermes Function-Calling V1
This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models.
This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational scenarios… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/hermes-function-calling-v1-jsonl.hermes-function-calling-v1
Hermes Function-Calling V1
This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models.
This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational scenarios… See the full description on the dataset page: https://huggingface.co/datasets/interstellarninja/hermes-function-calling-v1.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.synthetic-pii-function-calling
Dataset Summary
A function calling dataset created by filtering the urchade/synthetic-pii-ner-mistral-v1 dataset.
multi-hop-qa-function-calling-format-V1.0This dataset is converted from khaimaitien/qa-expert-multi-hop-qa-V1.0 to OpenAI function calling format.
Each data point is a list of messages with role=user, assistant or function:
message that role=user, content is the question
message that role=assistant, content is not None, function_call is None: --> assistant responds with text only
message that role=assistant and function_call is not None --> assistant asks to execute a function call
function_call is of the form: {"name": "retrieve"… See the full description on the dataset page: https://huggingface.co/datasets/khaimaitien/multi-hop-qa-function-calling-format-V1.0.hermes-function-calling-nepali
hermes-function-calling-nepali
Single-turn function calling with the user request re-spoken in Nepali — Devanagari
(ne_deva) and romanized Latin (ne_latn) — voice-assistant style, with tool calls
verified against the English ground truth. Tool schemas and expected calls are unchanged
from NousResearch/hermes-function-calling-v1
(func_calling_singleturn); only the user turn was localized.
Generated with HimalayaAI/gymkhana's
multilingual-tool-use environment:
Localizer… See the full description on the dataset page: https://huggingface.co/datasets/himalaya-ai/hermes-function-calling-nepali.gem-viggo-function-calling
Dataset Summary
A function calling dataset created from the GEM/viggo dataset.
agentmujo-function-calling
agentmujo-function-calling (v0.1.0 — MVP)
Ručno dizajniran kanonski skup za function calling na bosanskom jeziku
(ijekavica), dio AgentMujo Training Frameworka
(configs/tools.yaml je Single Source of Truth za alate).
Verzija: 0.1.0 · Uzoraka: 147 · Jezik: bs-ijekavica
Format: JSONL; svaki red: id, version, language, task, difficulty, enable_thinking, messages[] (user/assistant/tool + tool_calls[]), metadata{}
(schema: schemas/dataset.schema.json u framework repou).
Alati:… See the full description on the dataset page: https://huggingface.co/datasets/shaban2024/agentmujo-function-calling.test-glaiveai-function-callingnarrative-function-calling-v1
Narrative Function Calling v1
Welcome to Narrative Function Calling v1! This dataset is purpose-built for training (or fine-tuning) models that produce consistent, structured function calls in conversation-like settings. The dataset integrates and normalizes data from both Glaive Function Calling v2 (Apache License 2.0) and Salesforce XLAM function calling data (CC-BY-4.0)[^liu2024apigen]. It provides a clean, rich, and comprehensive set of examples that guide large language models… See the full description on the dataset page: https://huggingface.co/datasets/narrative-io/narrative-function-calling-v1.Berkeley-Function-Calling-Leaderboard-Fix
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/AndyChen123/Berkeley-Function-Calling-Leaderboard-Fix.function-calling-dataset
Function-Calling Dataset
High-quality synthetic dataset for training function-calling / tool-use capabilities in LLMs.
Dataset Details
9723 examples across 6 domains
30 unique function definitions with JSON Schema parameters
5 complexity levels: simple, moderate, complex, multi-tool, conversational
Generated using Gemini 2.0 Flash with careful prompt engineering and validation
Domains
Domain
Description
Functions
E-commerce
Shopping… See the full description on the dataset page: https://huggingface.co/datasets/Johin/function-calling-dataset.llama-2-oai-function-callingfunction-calling-v1xlam-function-calling-60k_langchainReformatted dataset from "Salesforce/xlam-function-calling-60k" (from Hugging Face) for the purposes of fine tuning LLMs for tool calling for the LangChain and LangGraph frameworks
license: mit