datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
zendocumentation-imagestrackio-datasetzen-imageexample-imagestoolcallharmonyzen-multi-imagetldr
TL;DR Dataset
Summary
The TL;DR dataset is a processed version of Reddit posts, specifically curated to train models using the TRL library for summarization tasks. It leverages the common practice on Reddit where users append "TL;DR" (Too Long; Didn't Read) summaries to lengthy posts, providing a rich source of paired text data for training summarization models.
Data Structure
Format: Standard
Type: Prompt-completion
Columns:
"pompt": The unabridged Reddit… See the full description on the dataset page: https://huggingface.co/datasets/trl-lib/tldr.Capybaraultrafeedback_binarizedtrl-test-instructionmath_shepherd
Math-Shepherd Dataset
Summary
The Math-Shepherd dataset is a processed version of Math-Shepherd dataset, designed to train models using the TRL library for stepwise supervision tasks. It provides step-by-step solutions to mathematical problems, enabling models to learn and verify each step of a solution, thereby enhancing their reasoning capabilities.
Data Structure
Format: Standard
Type: Stepwise supervision
Columns:
"pompt": The problem statement.… See the full description on the dataset page: https://huggingface.co/datasets/trl-lib/math_shepherd.DeepMath-103K
DeepMath-103K Dataset
Summary
DeepMath-103K is meticulously curated to push the boundaries of mathematical reasoning in language models.
Data Structure
Format: Conversational
Type: Prompt-only
Column:
"prompt": The input question.
"solution": The solution to the math problem.
Generation script
The script used to generate this dataset can be found here.
descriptiveness-sentiment-trl-style
TRL's Sentiment and Descriptiveness Preference Dataset
The dataset comes from https://arxiv.org/abs/1909.08593, one of the earliest RLHF work from OpenAI.
We preprocess the dataset using our standard prompt, chosen, rejected format.
Reproduce this dataset
Download the descriptiveness_sentiment.py from the https://huggingface.co/datasets/trl-internal-testing/descriptiveness-sentiment-trl-style/tree/0.1.0.
Run python examples/datasets/descriptiveness_sentiment.py… See the full description on the dataset page: https://huggingface.co/datasets/trl-internal-testing/descriptiveness-sentiment-trl-style.dolly-chatml-sftllava-instruct-mix
LLaVA Instruct Mix
Summary
The LLaVA Instruct Mix dataset is a processed version of LLaVA Instruct Mix.
Data Structure
Format: Conversational
Type: Language-modeling
Columns:
"images": The image associated with the text.
"prompt": A list of messages that form the context for the conversation.
"completion": The last message in the conversation, which is the model's response.
This structure allows models to learn from the context of the conversation… See the full description on the dataset page: https://huggingface.co/datasets/trl-lib/llava-instruct-mix.apigen-smollm-trl-FC
Dataset card for argilla-warehouse/apigen-smollm-trl-FC
This dataset is a merge of argilla/Synth-APIGen-v0.1
and Salesforce/xlam-function-calling-60k, and was prepared for training using the script
prepare_for_sft.py that can be found in the repository files.
References
@article{liu2024apigen,
title={APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets},
author={Liu, Zuxin and Hoang, Thai and Zhang, Jianguo and Zhu, Ming and… See the full description on the dataset page: https://huggingface.co/datasets/argilla-warehouse/apigen-smollm-trl-FC.essay-grammar-range-qwen3.5-4b-trl-completions
TRL Completion logs
This dataset contains the completions generated during training using trl.
Find the trained model at https://huggingface.co/bihungba1101/essay-grammar-range-qwen3.5-4b-grpo.
The completions are stored in parquet files, and each file contains the completions for a single step of training (depending on the logging_steps argument).
Each file contains the following columns:
step: the step of training
prompt: the prompt used to generate the completion
completion: the… See the full description on the dataset page: https://huggingface.co/datasets/bihungba1101/essay-grammar-range-qwen3.5-4b-trl-completions.grammar-accuracy-qwen3.5-4b-trl-completions
TRL Completion logs
This dataset contains the completions generated during training using trl.
Find the trained model at https://huggingface.co/bihungba1101/grammar-accuracy-qwen3.5-4b-grpo.
The completions are stored in parquet files, and each file contains the completions for a single step of training (depending on the logging_steps argument).
Each file contains the following columns:
step: the step of training
prompt: the prompt used to generate the completion
completion:… See the full description on the dataset page: https://huggingface.co/datasets/bihungba1101/grammar-accuracy-qwen3.5-4b-trl-completions.SQaLe-text-to-SQL-dataset
🧮 SQALE: A Large-Scale Semi-Synthetic Dataset
SQALE is a large-scale, semi-synthetic Text-to-SQL dataset grounded in real-world database schemas.
It was designed to push the boundaries of natural language to SQL generation, combining realistic schema diversity, complex query structures, and linguistically varied natural language questions.
The dataset was introduced in the paper SQaLe: A Large Text-to-SQL Corpus Grounded in Real Schemas. The code for the generation pipeline of this… See the full description on the dataset page: https://huggingface.co/datasets/trl-lab/SQaLe-text-to-SQL-dataset.essay-vocab-accuracy-qwen3.5-4b-trl-completions
TRL Completion logs
This dataset contains the completions generated during training using trl.
Find the trained model at https://huggingface.co/bihungba1101/essay-vocab-accuracy-qwen3.5-4b-grpo.
The completions are stored in parquet files, and each file contains the completions for a single step of training (depending on the logging_steps argument).
Each file contains the following columns:
step: the step of training
prompt: the prompt used to generate the completion
completion:… See the full description on the dataset page: https://huggingface.co/datasets/bihungba1101/essay-vocab-accuracy-qwen3.5-4b-trl-completions.ultrafeedback-prompt
UltraFeedback - Prompts Dataset
Summary
The UltraFeedback - Prompts dataset is a processed version of the UltraFeedback dataset for model evaluation on specific aspects like helpfulness, honesty, and instruction-following.
Data Structure
Format: Conversational
Type: Prompt-only
Column:
"pompt": The input question or instruction provided to the model.
Generation script
The script used to generate this dataset can be found here.
essay-vocab-range-qwen3.5-4b-trl-completions
TRL Completion logs
This dataset contains the completions generated during training using trl.
Find the trained model at https://huggingface.co/bihungba1101/essay-vocab-range-qwen3.5-4b-grpo.
The completions are stored in parquet files, and each file contains the completions for a single step of training (depending on the logging_steps argument).
Each file contains the following columns:
step: the step of training
prompt: the prompt used to generate the completion
completion: the… See the full description on the dataset page: https://huggingface.co/datasets/bihungba1101/essay-vocab-range-qwen3.5-4b-trl-completions.hh-rlhf-helpful-base-trl-style
TRL's Anthropic HH Dataset
We preprocess the dataset using our standard prompt, chosen, rejected format.
Reproduce this dataset
Download the anthropic_hh.py from the https://huggingface.co/datasets/trl-internal-testing/hh-rlhf-helpful-base-trl-style/tree/0.1.0.
Run python examples/datasets/anthropic_hh.py --push_to_hub --hf_entity trl-internal-testing
apigen-synth-trl
Dataset card
This dataset is a version of argilla/Synth-APIGen-v0.1 prepared for
fine-tuning using trl. To generate it, the following script was run:
from datasets import load_dataset
from jinja2 import Template
SYSTEM_PROMPT = """
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the functions can be used, point it out… See the full description on the dataset page: https://huggingface.co/datasets/argilla-warehouse/apigen-synth-trl.trl-ctbench
TRL-CTbench
Paper: arXiv:2606.09323 — TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders · Code: LOGO-CUHKSZ/TRL-Bench
Column- and table-level evaluation suite of TRL-Bench. All 27 configs are live (covering every CTbench source in the paper appendix, plus separate *_tables configs for benchmarks whose label volume + table corpus would otherwise exceed parquet's per-shard limits).
Configurations
Retrieval-style… See the full description on the dataset page: https://huggingface.co/datasets/logo-lab/trl-ctbench.grammar-accuracy-qwen3.5-4b-trl-grpo-vllm-colocate-completions
TRL Completion logs
This dataset contains the completions generated during training using trl.
Find the trained model at https://huggingface.co/bihungba1101/grammar-accuracy-qwen3.5-4b-trl-grpo-vllm-colocate.
The completions are stored in parquet files, and each file contains the completions for a single step of training (depending on the logging_steps argument).
Each file contains the following columns:
step: the step of training
prompt: the prompt used to generate the completion… See the full description on the dataset page: https://huggingface.co/datasets/bihungba1101/grammar-accuracy-qwen3.5-4b-trl-grpo-vllm-colocate-completions.ifeval-trlThe filtered subset from argilla/ifeval-like-data but formatted for using it with TRL.
trlcdk1_pickplace