datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
nemotron-sft-general-focused-stage1-2-ChatML-V3
Nemotron SFT Dataset (Chat Template Formatted)
Overview
This dataset is a curated supervised fine-tuning (SFT) dataset built from NVIDIA's Nemotron-Cascade-SFT-Stage-1 and Stage-2 datasets.
Important: This dataset uses the tokenizer's apply_chat_template() method to properly format conversations from the original messages/conversations fields.
Statistics
Total Samples: 496,385
Total Tokens: 1,114,218,401
Average Tokens per Sample: 2244.7
Tokenizer:… See the full description on the dataset page: https://huggingface.co/datasets/kshitijthakkar/nemotron-sft-general-focused-stage1-2-ChatML-V3.details_anthracite-org__magnum-v3-9b-chatml
Dataset Card for Evaluation run of anthracite-org/magnum-v3-9b-chatml
Dataset automatically created during the evaluation run of model anthracite-org/magnum-v3-9b-chatml.
The dataset is composed of 136 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/OALL/details_anthracite-org__magnum-v3-9b-chatml.nemotron-sft-code-focused-stage1-2-ChatML
Nemotron SFT Dataset (Chat Template Formatted)
Overview
This dataset is a curated supervised fine-tuning (SFT) dataset built from NVIDIA's Nemotron-Cascade-SFT-Stage-1 and Stage-2 datasets.
Important: This dataset uses the tokenizer's apply_chat_template() method to properly format conversations from the original messages/conversations fields.
Statistics
Total Samples: 50,000
Total Tokens: 415,605,764
Average Tokens per Sample: 8312.1
Tokenizer:… See the full description on the dataset page: https://huggingface.co/datasets/kshitijthakkar/nemotron-sft-code-focused-stage1-2-ChatML.writingprompts-10k-chatmlChatML-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.nemotron-sft-benchmark-focused-stage1-2-ChatML-V1
Nemotron SFT Dataset (Chat Template Formatted)
Overview
This dataset is a curated supervised fine-tuning (SFT) dataset built from NVIDIA's Nemotron-Cascade-SFT-Stage-1 and Stage-2 datasets.
Important: This dataset uses the tokenizer's apply_chat_template() method to properly format conversations from the original messages/conversations fields.
Statistics
Total Samples: 50,000
Total Tokens: 428,330,639
Average Tokens per Sample: 8566.6
Tokenizer:… See the full description on the dataset page: https://huggingface.co/datasets/kshitijthakkar/nemotron-sft-benchmark-focused-stage1-2-ChatML-V1.nemotron-sft-advanced-stage1-2-ChatML-V1
Nemotron SFT Dataset (Chat Template Formatted)
Overview
This dataset is a curated supervised fine-tuning (SFT) dataset built from NVIDIA's Nemotron-Cascade-SFT-Stage-1 and Stage-2 datasets.
Important: This dataset uses the tokenizer's apply_chat_template() method to properly format conversations from the original messages/conversations fields.
Statistics
Total Samples: 50,000
Total Tokens: 306,946,919
Average Tokens per Sample: 6138.9
Tokenizer:… See the full description on the dataset page: https://huggingface.co/datasets/kshitijthakkar/nemotron-sft-advanced-stage1-2-ChatML-V1.ultrachat-feedback-10k-chatmlnemotron-sft-general-focused-stage1-2-ChatML-V2
Nemotron SFT Dataset (Chat Template Formatted)
Overview
This dataset is a curated supervised fine-tuning (SFT) dataset built from NVIDIA's Nemotron-Cascade-SFT-Stage-1 and Stage-2 datasets.
Important: This dataset uses the tokenizer's apply_chat_template() method to properly format conversations from the original messages/conversations fields.
Statistics
Total Samples: 100,000
Total Tokens: 222,951,633
Average Tokens per Sample: 2229.5
Tokenizer:… See the full description on the dataset page: https://huggingface.co/datasets/kshitijthakkar/nemotron-sft-general-focused-stage1-2-ChatML-V2.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.LeroyDyer___Spydaz_Web_AI_ChatML_002-details
Dataset Card for Evaluation run of LeroyDyer/_Spydaz_Web_AI_ChatML_002
Dataset automatically created during the evaluation run of model LeroyDyer/_Spydaz_Web_AI_ChatML_002
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/LeroyDyer___Spydaz_Web_AI_ChatML_002-details.slimorca-corrected-chatmlChatML-oasst2_dpo_pairs_ensablo/oasst2_dpo_pairs_en 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("sablo/oasst2_dpo_pairs_en", split="train")
def format(columns):
return {
"prompt": tokenizer.apply_chat_template(columns["chosen"][:-1], tokenize=False, add_generation_prompt=True)… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-oasst2_dpo_pairs_en.function_call_orpo_sft_phi3_chatML_only
take dataset
hiyouga/glaive-function-calling-v2-sharegpt
load code.
from datasets import load_dataset
# unsloth_Phi_3_mini_4k_instruct_unsloth_oasst2_orpo_mix_tokenizer_phi_3_v1_0001
import os
dataset = load_dataset('NickyNicky/function_call_orpo_sft_phi3_only','default')
# sft
data_formater=data_formater.remove_columns(['prompt','chosen','rejected'])
# orpo-dpo
data_formater=data_formater.remove_columns(['Text'])
sft Column 'Text'.
You are a helpful AI… See the full description on the dataset page: https://huggingface.co/datasets/NickyNicky/function_call_orpo_sft_phi3_chatML_only.HarmfulQA-ChatMLGrill-preprod-v1_chatML
Grill-v1
This dataset contains ChatML formatted conversation between a human and AI. The goal is simulating a preproduction version of flammen.ai in order to train the Mahou series of models.
Schema
chatID - index linking conversations
idx - index of response in a conversation
prompt - ChatML formatted conversation including flammen.ai system prompt
chosen - accepted response
rejected - rejected response (for DPO)
Method
SillyTavern was used as the frontend… See the full description on the dataset page: https://huggingface.co/datasets/flammenai/Grill-preprod-v1_chatML.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.ChatML-ultrafeedback-binarized-preferencesargilla/ultrafeedback-binarized-preferences 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("argilla/ultrafeedback-binarized-preferences", split="train")
def format(columns):
return {
"prompt": f"<|im_start|>user\n{columns['instruction']}<|im_end|>\n<|im_start|>assistant\n",
"chosen": f"{columns['chosen_response']}<|im_end|>",
"rejected":… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-ultrafeedback-binarized-preferences.code_feedback_py-chatmlcritique-chatmltopic_based_chatml_dpo_pairs
DPO Pairs
This is a preprocessed version of mlabonne/chatml_dpo_pairs using Bunkatopics to extract meaningful Topics that help models converge with less data.
The objective was to create a smaller dataset than the original but buy keeping its efficiecency.To achieve this, we compared the two datasets used to train the reward model in mlabonne/chatml_dpo_pairs: the rejected Llama answers and the accepted ChatGPT answers from the DPO dataset.
We then conducted topic modeling on both… See the full description on the dataset page: https://huggingface.co/datasets/bunkalab/topic_based_chatml_dpo_pairs.ChatML-openhermes2.5-dpo-binarized-alphaargilla/OpenHermes2.5-dpo-binarized-alpha 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/openhermes2.5-dpo-binarized-alpha", split="train")
def format(columns):
return {
"prompt": tokenizer.apply_chat_template(columns["chosen"][:-1], tokenize=False… See the full description on the dataset page: https://huggingface.co/datasets/Felladrin/ChatML-openhermes2.5-dpo-binarized-alpha.socraticchat-chatmlMLB-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.Dans-DiscountModels__Dans-Instruct-Mix-8b-ChatML-details
Dataset Card for Evaluation run of Dans-DiscountModels/Dans-Instruct-Mix-8b-ChatML
Dataset automatically created during the evaluation run of model Dans-DiscountModels/Dans-Instruct-Mix-8b-ChatML
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Dans-DiscountModels__Dans-Instruct-Mix-8b-ChatML-details.Dans-DiscountModels__Dans-Instruct-Mix-8b-ChatML-V0.1.1-details
Dataset Card for Evaluation run of Dans-DiscountModels/Dans-Instruct-Mix-8b-ChatML-V0.1.1
Dataset automatically created during the evaluation run of model Dans-DiscountModels/Dans-Instruct-Mix-8b-ChatML-V0.1.1
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train"… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Dans-DiscountModels__Dans-Instruct-Mix-8b-ChatML-V0.1.1-details.Dans-DiscountModels__Dans-Instruct-Mix-8b-ChatML-V0.2.0-details
Dataset Card for Evaluation run of Dans-DiscountModels/Dans-Instruct-Mix-8b-ChatML-V0.2.0
Dataset automatically created during the evaluation run of model Dans-DiscountModels/Dans-Instruct-Mix-8b-ChatML-V0.2.0
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train"… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Dans-DiscountModels__Dans-Instruct-Mix-8b-ChatML-V0.2.0-details.snorkel_curated-chatmlDans-DiscountModels__Dans-Instruct-Mix-8b-ChatML-V0.1.0-details
Dataset Card for Evaluation run of Dans-DiscountModels/Dans-Instruct-Mix-8b-ChatML-V0.1.0
Dataset automatically created during the evaluation run of model Dans-DiscountModels/Dans-Instruct-Mix-8b-ChatML-V0.1.0
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train"… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Dans-DiscountModels__Dans-Instruct-Mix-8b-ChatML-V0.1.0-details.