datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n8-rejection-sampling-soft-match
N8 Rejection Sampling (Soft Match)
Overview
This dataset was created via rejection sampling from the Qwen3-4B response dataset using Qwen3-32B answers as ground truth.
Source dataset (Qwen3-4B, 8 responses per prompt): marin-community/open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n8-reformatted
Verifier dataset (Qwen3-32B, 1 response per prompt): marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens
Creator: The Marin Project
How… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n8-rejection-sampling-soft-match.OpenVul_Rejection_Sampling_based_Vulnerability_Reasoning_Dataset_for_SFTThis dataset provides high-quality, correctness-filtered vulnerability reasoning data to support the SFT of specialized VD LLMs for future research.
open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n8-rejection-sampling-strict-match
N8 Rejection Sampling (Strict Match)
Overview
This dataset was created via rejection sampling from the Qwen3-4B response dataset using Qwen3-32B answers as ground truth.
Source dataset (Qwen3-4B, 8 responses per prompt): marin-community/open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n8-reformatted
Verifier dataset (Qwen3-32B, 1 response per prompt): marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens
Creator: The Marin Project… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n8-rejection-sampling-strict-match.open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n1-rejection-sampling-quantity-match
N1 Rejection Sampling (Quantity Match)
Overview
This dataset was created via rejection sampling from the Qwen3-4B response dataset using Qwen3-32B answers as ground truth.
Source dataset (Qwen3-4B, 8 responses per prompt): marin-community/open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n8-reformatted
Verifier dataset (Qwen3-32B, 1 response per prompt): marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens
Creator: The Marin Project… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/open-thoughts-4-30k-math-qwen3-4b-annotated-32768-tokens-n1-rejection-sampling-quantity-match.rejection_sampling_10627_fixed
Dataset Card for "rejection_sampling_10627_fixed"
More Information needed
open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens-n1-rejection-sampling-quantity-match
Qwen3-32B Math Rejection Sampling (Quantity Match) with Qwen3-235B-A22B Verifier
Overview
This dataset was created via rejection sampling from the Qwen3-32B response dataset using Qwen3-235B-A22B answers as ground truth.
Source dataset (Qwen3-32B, 8 responses per prompt): marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens-n8-reformatted
Verifier dataset (Qwen3-235B-A22B, 1 response per prompt):… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens-n1-rejection-sampling-quantity-match.rejection-sampling-llada-1.0-sft-gsm8krejection-sampling-replace-repeat
🧠 Amazon Semantic-ID Recommendation + Reasoning — GPT-5.4 regenerated
GPT-5.4 enrichment variant of yufan/recsys-genrec-dataset · Reasoning over Semantic IDs Enhances Generative Recommendation
This repo mirrors the format of the original yufan/recsys-genrec-dataset, but the
LLM-enriched fields are regenerated with GPT-5.4 (Azure OpenAI). The Video Games domain is fully populated.
<cat> below is Video_Games.
✅ Currently available
config… See the full description on the dataset page: https://huggingface.co/datasets/budgiesarecooliguess/rejection-sampling-replace-repeat.rejection-sampling-QA
Rejecction Sampling Q&A
This dataset is a very small curated question-answer pairs.
The questions were hand-crafted to test the model's capabilities to follow instruction across various domains.
The answers were generated using Microsoft's Phi-2 and curated using OpenAssistant's Large DeBERTa v3 Reward Model v2.
Dataset Details
The answers of this dataset were generated by prompting Microsoft's Phi-2 using a prompt format inspired by Stanford's Alpaca to help the LLM… See the full description on the dataset page: https://huggingface.co/datasets/alehc/rejection-sampling-QA.hh-rlhf-safety-v2-rejection-samplingrejection_samplingrejection_sampling_23251_messagesgemma2b_combined_iter1_rejection_sampling300_RejectionSampling_LLMInputigc1-top50-rejection-sampling
🧠 Amazon Semantic-ID Recommendation + Reasoning — GPT-5.4 regenerated
GPT-5.4 enrichment variant of yufan/recsys-genrec-dataset · Reasoning over Semantic IDs Enhances Generative Recommendation
This repo mirrors the format of the original yufan/recsys-genrec-dataset, but the
LLM-enriched fields are regenerated with GPT-5.4 (Azure OpenAI). The Video Games domain is fully populated.
<cat> below is Video_Games.
✅ Currently available
config… See the full description on the dataset page: https://huggingface.co/datasets/budgiesarecooliguess/igc1-top50-rejection-sampling.demo_rejection_sampling_QA_phi-2_deberta-v3-large-v2_temp0.2This is a demo constructed dataset for alignment/preference learning.
With paritially handcrafted questions (prompts), the answers are genreated by the phi-2 model with temperature 0.2 and the answers are scores select by the deberta-large-v2.
The dataset containing questions and the selected answers from highest to lowest, decoding with rejection sampling K=8.
Example loading:
import datasets
ds = datasets.load_dataset('yizhilll/demo_rejection_sampling_QA_phi-2_deberta-v3-large-v2_temp0.2')… See the full description on the dataset page: https://huggingface.co/datasets/yizhilll/demo_rejection_sampling_QA_phi-2_deberta-v3-large-v2_temp0.2.rejection-sampling-V4
🧠 Amazon Semantic-ID Recommendation + Reasoning — GPT-5.4 regenerated
GPT-5.4 enrichment variant of yufan/recsys-genrec-dataset · Reasoning over Semantic IDs Enhances Generative Recommendation
This repo mirrors the format of the original yufan/recsys-genrec-dataset, but the
LLM-enriched fields are regenerated with GPT-5.4 (Azure OpenAI). The Video Games domain is fully populated.
<cat> below is Video_Games.
✅ Currently available
config… See the full description on the dataset page: https://huggingface.co/datasets/budgiesarecooliguess/rejection-sampling-V4.rejection_sampling_1722360907rejection_sampling_1722361005open-thoughts-code-rejection-samplingV2-rejection-sampling-dataset
🧠 Amazon Semantic-ID Recommendation + Reasoning — GPT-5.4 regenerated
GPT-5.4 enrichment variant of yufan/recsys-genrec-dataset · Reasoning over Semantic IDs Enhances Generative Recommendation
This repo mirrors the format of the original yufan/recsys-genrec-dataset, but the
LLM-enriched fields are regenerated with GPT-5.4 (Azure OpenAI). The Video Games domain is fully populated.
<cat> below is Video_Games.
✅ Currently available
config… See the full description on the dataset page: https://huggingface.co/datasets/budgiesarecooliguess/V2-rejection-sampling-dataset.rejection_sampling_1722011047rejection_sampling_1722293991qwen-7b-verified-7k-rejection-sampling-alpaca-formatrejection_sampling_1722361801rejection-sampling
🧠 Amazon Semantic-ID Recommendation + Reasoning — GPT-5.4 regenerated
GPT-5.4 enrichment variant of yufan/recsys-genrec-dataset · Reasoning over Semantic IDs Enhances Generative Recommendation
This repo mirrors the format of the original yufan/recsys-genrec-dataset, but the
LLM-enriched fields are regenerated with GPT-5.4 (Azure OpenAI). The Video Games domain is fully populated.
<cat> below is Video_Games.
✅ Currently available
config… See the full description on the dataset page: https://huggingface.co/datasets/budgiesarecooliguess/rejection-sampling.rejection_sampling_1722011582Rejection_Sampling_based_Vulnerability_Reasoning_Dataset_for_SFTrejection_sampling_1722011125rejection_sampling_1722011436