datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
xlam-function-calling-60k-parsed
[PARSED] APIGen Function-Calling Datasets (xLAM)
This dataset contains the full data from the original Salesforce/xlam-function-calling-60k
Subset name
multi-turn
parallel
multiple definition
Last turn type
number of dataset
xlam-function-calling-60k
no
yes
yes
tool_calls
60000
This is a re-parsing formatting dataset for the xLAM official dataset.
Load the dataset
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/xlam-function-calling-60k-parsed.function-calling-eval-dataset-v0The hf dataset contains 2 evaluation datasets
single_turn - The converstaion length for this evaluation dataset is 2. It consists of a user ask followed by a function call by assistant.
multi_turn - The conversation length is variable here but contains a combination of user messages, assistant function calls, assistant messages & tool responses.
Information about the columns
tools - List of functions/tools with specs in JSON format. This is the list of functions the model has to choose from… See the full description on the dataset page: https://huggingface.co/datasets/fireworks-ai/function-calling-eval-dataset-v0.apigen-function-calling
Dataset card for argilla/apigen-function-calling
This dataset is a merge of argilla/Synth-APIGen-v0.1
and Salesforce/xlam-function-calling-60k, making
over 100K function calling examples following the APIGen recipe.
Prepare for training
This version is not ready to do fine tuning, but you can run a script like prepare_for_sft.py
to prepare it, and run the same recipe that can be found in
argilla/Llama-3.2-1B-Instruct-APIGen-FC-v0.1#training-procedure.
Modify the prompt… See the full description on the dataset page: https://huggingface.co/datasets/argilla/apigen-function-calling.function-calling-chatml
Dataset Card for "function-calling-chatml"
Converted glaiveai/Glaive-function-calling-v2 to chatml format.
Example entry
[ { "from": "system", "value": "You are a helpful assistant with access to the following functions. Use them if required -{\n \"name\": \"create_contact\",\n \"description\": \"Create a new contact\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\n \"type\": \"string\",\n \"description\": \"The name of the contact\"\n }… See the full description on the dataset page: https://huggingface.co/datasets/Locutusque/function-calling-chatml.xlam-function-calling-60k-hermesxlam-function-calling-60k-raw
XLAM Function Calling 60k Raw Dataset
This dataset includes train and test splits derived from Salesforce/xlam-function-calling-60k.
Train split size: 95% of the original dataset
Test split size: 5% of the original dataset
hibo-function-calling-v1
hibo-function-calling-v1
📖 Dataset Description
This dataset, named "hibo-function-calling-v1", is designed to facilitate the fine-tuning of Large Language Models (LLMs) for function calling tasks. It comprises a single 'train' split containing 323,271 data points across three columns: 'dataset_origin', 'system', and 'chat'.
The dataset is a result of merging two distinct sources: gathnex/Gath_baize and glaiveai/glaive-function-calling-v2, with an aim to provide… See the full description on the dataset page: https://huggingface.co/datasets/thibaud-perrin/hibo-function-calling-v1.function-calling
function-calling
The purpose of this dataset is to give function calling abilities to your LLM.
Derived from hypervariance/function-calling-sharegpt with a system prompt cleanup and other minor fixes.
Prompt format
With functions
## Configuration
Functions: enabled
## Available Functions
{ ... }
{ ... }
---
You are a helpful assistant.
Without functions
## Configuration
Functions: disabled
---
You are a helpful assistant.
Function… See the full description on the dataset page: https://huggingface.co/datasets/MathAndMagic/function-calling.Salesforce-xlam-function-calling-60kglaive-function-calling-v2-formatted
original dataset: glaiveai/glaive-function-calling-v2
{'system_message': 'You are a helpful assistant with access to the following functions. Use them if required -',
'function_description': '{\n "name": "get_random_quote",\n "description": "Get a random quote",\n "parameters": {}\n}',
'conversations': [{'content': 'Hi, can you help me with something?',
'role': 'user'},
{'content': "Of course! I'm here to assist you. What do you need help with?",
'role': 'assistant'}… See the full description on the dataset page: https://huggingface.co/datasets/heegyu/glaive-function-calling-v2-formatted.glaive-function-calling-v2-formatted
Dataset Card for "glaive-function-calling-v2-formatted"
More Information needed
glaive-function-calling-v2-sharegpt
Dataset Card for "glaive-function-calling-v2-sharegpt"
This dataset takes the glaive/glaive-function-calling-v2 dataset and formats it with ShareGPT using Lilac
The accompanying notebook can be found here.
The original columns "system" and "chat" still exist on the dataset.
There are 4 types of roles in the ShareGPT format:
system
user
human
function call
The original dataset has a column called 'chat' with the following structure:
USER: Hi, I need help with calculating a tip. My… See the full description on the dataset page: https://huggingface.co/datasets/lilacai/glaive-function-calling-v2-sharegpt.Universal-glaive-function-calling-v2
Dataset Card for "Universal-glaive-function-calling-v2"
More Information needed
Salesforce-xlam-function-calling-60kglaive-function-calling-v2-openai-native
glaive-function-calling-v2-openai-native
glaiveai/glaive-function-calling-v2 restructured into the native OpenAI / TRL
format: tools is a typed column and tool_calls[].function.arguments is a
real object — not JSON inside a string.
The original is widely used (69k downloads/month) but inactive for ~3 years, and
ships tool calls as <functioncall> text blobs with Python-quoted arguments.
Existing repackagings either keep ShareGPT with tools as a string, or carry
no license at all.… See the full description on the dataset page: https://huggingface.co/datasets/Archangel-system/glaive-function-calling-v2-openai-native.fiftyone-function-calling-14k
FiftyOne Function Calling 14k Dataset
Overview
This dataset is derived from the FiftyOne documentation and is designed to train AI assistants to understand and answer questions about FiftyOne's functionality. The dataset follows the format specified in the APIGen paper, structuring the data to map natural language queries to appropriate API tools and their usage.
Purpose
Train AI models to understand FiftyOne-related queries
Provide structured examples of… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/fiftyone-function-calling-14k.Funcdex-MT-Function-Calling
Funcdex-MT-Function-Calling Dataset
Funcdex-MT-Function-Calling is a multi-turn function calling dataset designed for training language models to interact with real-world tools and APIs. The dataset contains 1,787 conversations covering 10 individual toolkits and 5 multi-toolkit bundles, with comprehensive system prompts and realistic multi-turn interactions.The code used to generate the dataset can be found here.
Models trained on this dataset have excellent… See the full description on the dataset page: https://huggingface.co/datasets/prem-research/Funcdex-MT-Function-Calling.Nova-Synapse-Function-Calling
🧠 Nova-Synapse: The High-Density Function Calling Dataset
Curated by NovachronoAI
📖 Overview
Nova-Synapse is a curated, high-density training corpus designed to transform small Language Models (3B-8B) into State-of-the-Art (SOTA) function-calling agents.
Most function-calling datasets suffer from one of two problems: they are either too small to generalize (Hermes) or too noisy/repetitive (Glaive). Nova-Synapse solves this by merging the "Holy Trinity"… See the full description on the dataset page: https://huggingface.co/datasets/NovachronoAI/Nova-Synapse-Function-Calling.pythonic-function-calling
Pythonic Function Calling Dataset
This dataset contains synthetic data used for training Pythonic function calling models Dria-Agent-a-3B and Dria-Agent-a-7B.
Dria is a python framework to generate synthetic data on globally connected edge devices with 50+ models. See the network here
Dataset Summary
The dataset includes various examples of function calling scenarios, ranging from simple to complex multi-turn interactions.
It was generated synthetically using the… See the full description on the dataset page: https://huggingface.co/datasets/driaforall/pythonic-function-calling.xlam-function-calling-60k-raw-augmented
XLAM Function Calling 60k Raw Augmented Dataset
This dataset includes augmented train and test splits derived from product-science/xlam-function-calling-60k-raw.
Train split size: Original size plus augmented data
Test split size: Original size plus augmented data
Augmentation Details
This dataset has been augmented by modifying function names in the original data. Randomly selected function names have underscores replaced with periods at random positions… See the full description on the dataset page: https://huggingface.co/datasets/product-science/xlam-function-calling-60k-raw-augmented.gemma-function-calling
👉🏽 Important
This dataset is adapted from hypervariance/function-calling-sharegpt to fine-tune the Google gemma-2-2b-it model for function calling.
🔀 Changes Made
Merged consecutive "GPT" responses into single responses (affected 8.49% of examples, 7372 out of 86864).
Updated role names:
"system" → Removed (function usage instructions moved to separate column)
"human" → "user"
"gpt" → "assistant"
"function_response" → Unchanged
Changed message keys from ["from"… See the full description on the dataset page: https://huggingface.co/datasets/dinushiTJ/gemma-function-calling.hermes-function-calling-thinking-V1gemma3-pythonic-function-tool-calling-v1glaive-function-calling-v2-llama
Glaive's Function Calling V2 for Llama2
Glaive's Function Calling V2 dataset, formatted according to the Llama2 chat schema, with all the data that I wasn't able to automatically convert removed manually.
Adds a special <function> token. Here's an example prompt:
<s>[INST] <<SYS>>
<function>Available functions:
<function>{
"name": "generate_password",
"description": "Generate a random password with specified criteria",
"parameters": {
"type": "object"… See the full description on the dataset page: https://huggingface.co/datasets/rizerphe/glaive-function-calling-v2-llama.function-calling-v0.2-with-r1-cotThis dataset is a modified version of Salesforce/xlam-function-calling-60k, incorporating reasoning chains generated by deepseek-ai/DeepSeek-R1-Distill-Llama-8B.
assist-llm-function-calling
Function Calling dataset for Assist LLM for Home Assistant
This dataset is generated by using other conversation agent pipelines as teachers
from the deivce-actions-v2 dataset.
This dataset is used to support fine tuning of llama based models.
See Device Actions for a notebook for construction of this dataset and the device-actions dataset.
glaive-function-calling-openai
OpenAI Function Calling Dataset
This dataset contains examples of OpenAI function calling conversations, designed for training and evaluating language models on function calling capabilities. The dataset includes both a comprehensive collection of function calling examples and a curated subset focusing on the most frequently used functions.
Dataset Structure
The dataset consists of a complete dataset and several test subsets:
Complete Dataset
Contains all… See the full description on the dataset page: https://huggingface.co/datasets/madroid/glaive-function-calling-openai.hermes-function-calling-v1-allglm52-datagen-r11-100-agentic-function-calling-pivot-v2-tracesglaive-function-calling-2k