datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
chatml-function-calling-v2
Dataset Conversion
This dataset is a converted version of the Glaive Function Calling v2 dataset, originally hosted on Hugging Face.
Chat Template for Dataset
Description
This chat template is designed to work with this dataset.
Template
chat_template = """{%- set tools = tools if tools is defined else None -%}
{%- set date_string = date_string if date_string is defined else "1 Sep 2024" -%}
{%- set system_message = messages[0].content if… See the full description on the dataset page: https://huggingface.co/datasets/ankush13r/chatml-function-calling-v2.Vietnamese-Locutusque-function-calling-chatml-gg-translatedJava_method2test_chatml
Java Method to Test ChatML
This dataset is based on the methods2test dataset from Microsoft. It follows the ChatML template format: [{'role': '', 'content': ''}, {...}].
Originally, methods2test contains only Java methods at different levels of granularity along with their corresponding test cases. The different focal method segmentations are illustrated here:
To simulate a conversation between a Java developer and an AI assistant, I introduce two key parameters:
The prompt… See the full description on the dataset page: https://huggingface.co/datasets/random-long-int/Java_method2test_chatml.ChatML-distilabel-capybara-dpo-7k-binarizedargilla/distilabel-capybara-dpo-7k-binarized in ChatML format, ready to use in HuggingFace TRL's DPO Trainer.
Python code used for conversion:
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("argilla/distilabel-capybara-dpo-7k-binarized", split="train")
def format(columns):
return {
"prompt": tokenizer.apply_chat_template(columns["chosen"][:-1]… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-distilabel-capybara-dpo-7k-binarized.ChatML-aya_datasetCohereForAI/aya_dataset in ChatML format, ready to use in HuggingFace TRL's SFT Trainer.
Python code used for conversion:
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("CohereForAI/aya_dataset", split="train")
def format(columns):
messages = [
{
"role": "user",
"content": columns["inputs"].strip(),
},
{… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-aya_dataset.ChatML-SlimOrca-DedupOpen-Orca/SlimOrca-Dedup in ChatML format, ready to use in HuggingFace TRL's SFT Trainer.
Python code used for conversion:
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("Open-Orca/SlimOrca-Dedup", split="train")
def format(columns):
messages = []
conversations = columns["conversations"]
for i in range(len(conversations)):
message =… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-SlimOrca-Dedup.ChatML-H4rmony_dponeovalle/H4rmony_dpo in ChatML format, ready to use in HuggingFace TRL's DPO Trainer.
Python code used for conversion:
from datasets import load_dataset
dataset = load_dataset("neovalle/H4rmony_dpo", split="train")
def format(columns):
return {
"prompt": f"<|im_start|>user\n{columns['prompt']}<|im_end|>\n<|im_start|>assistant\n",
"chosen": f"{columns['chosen']}<|im_end|>",
"rejected": f"{columns['rejected']}<|im_end|>",
}… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-H4rmony_dpo.ChatML-WebGLM-QATHUDM/webglm-qa in ChatML format.
Python code used for conversion:
from datasets import load_dataset
import pandas
import re
import random
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path="Felladrin/Llama-160M-Chat-v1"
)
dataset = load_dataset("THUDM/webglm-qa", split="train")
def format(columns):
references = "\n".join(
[
f"- {columns['references'][i].strip()}"
for i in… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-WebGLM-QA.ChatML-CapybaraLDJnr/Capybara in ChatML format, ready to use in HuggingFace TRL's SFT Trainer.
Python code used for conversion:
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("LDJnr/Capybara", split="train")
def format(columns):
messages = []
conversationColumn = columns["conversation"]
for i in range(len(conversationColumn)):
messages.append({
"role":… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-Capybara.slimorca-dedup-chatml-100k
Copy of Open-Orca/SlimOrca-Dedup in ChatML format downsample to 100k
"SlimOrca Dedup" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples.
Key Features
Removal of RLHF instances.
Deduplication using minhash and Jaccard similarity techniques.
Demo Models
Note: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version.
*… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/slimorca-dedup-chatml-100k.ChatML-OpenOrcaOpen-Orca/OpenOrca in ChatML format, ready to use in HuggingFace TRL's SFT Trainer.
Python code used for conversion:
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Minueza-32M-Base")
dataset = load_dataset("Open-Orca/OpenOrca", split="train")
def format(columns):
messages = []
system_prompt = columns["system_prompt"].strip()
if system_prompt:
messages.append({
"role": "system"… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-OpenOrca.ChatML-webGPT_x_dollystarfishmedical/webGPT_x_dolly in ChatML format, ready to use in HuggingFace TRL's SFT Trainer.
Python code used for conversion:
from datasets import load_dataset
from transformers import AutoTokenizer
import random
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("starfishmedical/webGPT_x_dolly", split="train")
def format(columns):
instruction = columns["instruction"].strip()
input = columns["input"].strip()
assistant_message =… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-webGPT_x_dolly.ChatML-deita-10k-v0hkust-nlp/deita-10k-v0 in ChatML format, ready to use in HuggingFace TRL's SFT Trainer.
Python code used for conversion:
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("hkust-nlp/deita-10k-v0", split="train")
def format(columns):
messages = []
conversation = columns["conversations"]
for i in range(len(conversation)):
message = conversation[i]… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-deita-10k-v0.ChatML-hercules-v2.0Locutusque/hercules-v2.0 in ChatML format.
Python code used for conversion:
from datasets import load_dataset
import pandas
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path="Felladrin/Llama-160M-Chat-v1"
)
dataset = load_dataset("Locutusque/hercules-v2.0", split="train")
def format(columns):
messages = []
conversation = columns["conversations"]
for i in range(len(conversation)):
message =… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-hercules-v2.0.ChatML-HelpSteernvidia/HelpSteer in ChatML format, ready to use in HuggingFace TRL's SFT Trainer.
Python code used for conversion:
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("nvidia/HelpSteer", split="train")
def format(columns):
prompt = columns["prompt"].strip()
response = columns["response"].strip()
messages = [
{
"role": "user"… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-HelpSteer.ChatML-databricks-dolly-15kdatabricks/databricks-dolly-15k in ChatML format.
Python code used for conversion:
from datasets import load_dataset
import pandas
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path="Felladrin/Llama-160M-Chat-v1"
)
dataset = load_dataset("databricks/databricks-dolly-15k", split="train")
def format(columns):
instruction = columns["instruction"].strip()
context = columns["context"].strip()
response =… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-databricks-dolly-15k.ChatML-reddit-instruct-curatedeuclaise/reddit-instruct-curated in ChatML format, ready to use in HuggingFace TRL's SFT Trainer.
Python code used for conversion:
from datasets import load_dataset
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Felladrin/Llama-160M-Chat-v1")
dataset = load_dataset("euclaise/reddit-instruct-curated", split="train")
def format(columns):
post_title = columns["post_title"].strip()
post_text = columns["post_text"].strip()
comment_text =… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-reddit-instruct-curated.TAA-ChatML-10K
TAA-ChatML-10K
A dataset of 10,438 question-answer pairs for Cyber Threat Intelligence (CTI) and Advanced Persistent Threat (APT) attribution tasks. Synthesized from 1,468 publicly available threat intelligence reports covering APT attribution, malware analysis, and threat actor TTPs. The dataset is formatted in ChatML conversation structure for fine-tuning large language models.
License
MIT
apex-food-rd-chatml-v2-expanded
Apex Food R&D ChatML v2 — Expanded Indian Functional Ingredient Dataset
This is the expanded v2 dataset for building a food formulation R&D assistant for Apex Nutrition.
Why v2 exists
The first MVP dataset used a narrow seed list of ~20 ingredients. That was too limited for Apex Nutrition's intended product space. This v2 dataset expands the ingredient universe to 137 India-relevant functional/natural/organic ingredients, including millets, pulses, seeds, spices, herbs… See the full description on the dataset page: https://huggingface.co/datasets/harshal3099/apex-food-rd-chatml-v2-expanded.Vietnamese-Locutusque-function-calling-chatml-gg-translatedpersonal-finance-chatml-dataset
Bilingual Personal Finance ChatML Dataset (EN/ES)
Dataset Description
This dataset is a professionally curated bilingual (English/Spanish) instruction dataset designed for fine-tuning large language models (LLMs) in the domain of personal finance.
It is structured in ChatML format and intended for supervised fine-tuning (SFT), domain adaptation, and financial instruction modeling.
The dataset is created and reviewed from an accounting perspective, ensuring conceptual… See the full description on the dataset page: https://huggingface.co/datasets/williamjmorenor/personal-finance-chatml-dataset.slimorca-dedup-chatml
Copy of Open-Orca/SlimOrca-Dedup in ChatML format
"SlimOrca Dedup" is a deduplicated, unfiltered subset of the SlimOrca dataset, excluding RLHF instances, resulting in 363k unique examples.
Key Features
Removal of RLHF instances.
Deduplication using minhash and Jaccard similarity techniques.
Demo Models
Note: These models were trained on the full SlimOrca dataset, not the deduplicated, unfiltered version.
*… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/slimorca-dedup-chatml.apex-food-rd-chatml-v3-flavour
Apex Food R&D ChatML v3 — Expanded Ingredients + Flavour & Taste System Design
This v3 dataset extends the Apex Food R&D v2 dataset by adding a dedicated 12th capability:
12. Flavour & Taste System Design
The new capability covers:
Indian flavour palette design
sweetness modulation
bitterness masking systems
acid-sweet balance
spice-flavour pairing
dairy vs water flavour differences
natural flavour systems
flavour top/middle/base notes
flavour release in powders… See the full description on the dataset page: https://huggingface.co/datasets/harshal3099/apex-food-rd-chatml-v3-flavour.Stage2-3k_Arabic_ChatMLapex-food-rd-chatml
Apex Food Formulation R&D ChatML Dataset
Synthetic supervised fine-tuning dataset for a food formulation R&D assistant focused on Indian clean-label functional foods for Apex Nutrition.
Intended model
Recommended base model: Qwen/Qwen3-4BReason: verified Qwen3ForCausalLM architecture, Apache-2.0 license, strong quality at ~4B parameters, practical LoRA training target when GPU is available later.
Contents
5,500 ChatML examples
Splits: train 4,950 /… See the full description on the dataset page: https://huggingface.co/datasets/harshal3099/apex-food-rd-chatml.pakistan-political-leaders-chatml-dataset
🇵🇰 Pakistan Political Leaders ChatML Dataset
🚀 A high-quality instruction-style dataset designed for fine-tuning Large Language Models (LLMs) on Pakistani political history and leadership.
This dataset contains approximately 2500 curated question-answer pairs in ChatML format, enabling models to understand and respond to queries about major political figures in Pakistan.
🎯 Objective
The goal of this dataset is to:
Train LLMs to act as a knowledgeable political… See the full description on the dataset page: https://huggingface.co/datasets/Hamzasajjad38/pakistan-political-leaders-chatml-dataset.nano_finance_200k_en_es_chatML_gemma_orpo_dpoMLB-2024-ChatML-Matchups
MLB 2024 ChatML Matchups
MLB 2024 ChatML Matchups is a compact, inspection-friendly instruction dataset derived from the public MLB Stats API for the 2024 Major League Baseball regular season. It converts structured game, offense, and pitching data into ChatML-style examples that can be used to study grounded sports reasoning, structured response generation, and retrieval-conditioned assistant behavior.
The dataset is intentionally small enough to audit directly, but structured… See the full description on the dataset page: https://huggingface.co/datasets/clarkkitchen22/MLB-2024-ChatML-Matchups.MLB-2025-ChatML-Matchups
MLB 2025 ChatML Matchups
MLB 2025 ChatML Matchups is a compact instruction-tuning dataset built from the public MLB Stats API for the 2025 Major League Baseball regular season. It converts structured game, team offense, and team pitching information into ChatML-style rows that are ready for supervised fine-tuning, retrieval-augmented evaluation, sports analytics prototypes, or prompt engineering tests.
Each row is written from one team's perspective for one completed MLB… See the full description on the dataset page: https://huggingface.co/datasets/clarkkitchen22/MLB-2025-ChatML-Matchups.MLB-2023-ChatML-Matchups
MLB 2023 ChatML Matchups
MLB 2023 ChatML Matchups is a compact, inspection-friendly instruction dataset derived from the public MLB Stats API for the 2023 Major League Baseball regular season. It converts structured game, offense, and pitching data into ChatML-style examples that can be used to study grounded sports reasoning, structured response generation, and retrieval-conditioned assistant behavior.
The dataset is intentionally small enough to audit directly, but structured… See the full description on the dataset page: https://huggingface.co/datasets/clarkkitchen22/MLB-2023-ChatML-Matchups.