Datasetpublic

marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens-n8

Open Thoughts 4 - Math (Qwen3-32B, 32K tokens, n=8) This dataset contains math reasoning problems with 8 independent responses generated by Qwen3-32B. Overview Source: marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens Model: Qwen/Qwen3-32B Temperature: 0.8 Max tokens: 32,768 Columns Column Description instruction_seed The math problem prompt _source Source dataset identifier gpt41_mini_response Reference… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens-n8.

sourceHugging Faceapache-2.0updated 8mo agoView on Hugging Face

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Dataset Card

Open Thoughts 4 - Math (Qwen3-32B, 32K tokens, n=8)

This dataset contains math reasoning problems with 8 independent responses generated by Qwen3-32B.

Overview

—Source: marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens
—Model: Qwen/Qwen3-32B
—Temperature: 0.8
—Max tokens: 32,768

Columns

Column	Description
`instruction_seed`	The math problem prompt
`_source`	Source dataset identifier
`gpt41_mini_response`	Reference response from GPT-4.1 mini
`generated_text`	Response 1 (from source dataset)
`generated_text2` - `generated_text8`	Responses 2-8 (generated by Qwen3-32B)
`ms_id`	Unique identifier
`length`	Token length

Generation Details

Each of the 7 additional responses (generated_text2 through generated_text8) was generated sequentially using the same prompt (instruction_seed) with Qwen3-32B's chat template applied. The generation used temperature=0.8 to produce diverse reasoning chains.

Usage

python

from datasets import load_dataset

ds = load_dataset("marin-community/open-thoughts-4-30k-math-qwen3-32b-annotated-32768-tokens-n8")