datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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-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-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.MLB-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.