datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
po_qwen14b_tabular_data
BoLT Prompt Optimization — Tabular Dataset
For prompt optimization tasks in BoLT, an accessible benchmark for black-box optimization on LLM tasks.
Dataset Description
The dataset covers 5,014 evaluated instructions. Each row is a candidate system-prompt instruction paired with its empirically measured MATH-500 (4-shot, non-thinking mode) scores.
Evaluation details:
Model: Qwen/Qwen3-14B
Task: minerva_math500 (4-shot) (from lm-eval library)
System prompt:… See the full description on the dataset page: https://huggingface.co/datasets/chewwt/po_qwen14b_tabular_data.behaviour1k-Qwen3-features
BEHAVIOR-1K Qwen3 skill features
Per-frame conditioned features e_t = Phi(f_t, L_sub^(j), L), mean-pooled primitive skill latents S_j, aligned proprioception q_t, actions a_t, and subtask progress p_t.
These are the inputs and targets for a Primitive Skill Composer VLA Skill Predictor.
Ground-truth primitives come from BEHAVIOR-1K's hand-authored
primitive_annotation, so the segmentation is human-labelled rather than
predicted, and nothing here depends on a keyframe detector.… See the full description on the dataset page: https://huggingface.co/datasets/erl-hub/behaviour1k-Qwen3-features.Open-Qwen2VL-Data
Introduction
This repository contains the data for Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources.
Project page: https://victorwz.github.io/Open-Qwen2VL
Code: https://github.com/Victorwz/Open-Qwen2VL
Dataset
ccs_ebdataset: CC3M-CC12M-SBU filtered by CLIP, we directly download the webdataset based on the released of curated subset of BLIP-1
datacomp_medium_dfn_webdataset: DataComp-Medium-128M filtered by DFN, we… See the full description on the dataset page: https://huggingface.co/datasets/weizhiwang/Open-Qwen2VL-Data.qwen3.8-max-glm5.2-kimi-k3-distillation
Multi-Teacher Distillation Dataset (57,937 traces)
A quality-filtered, deduplicated, multi-teacher SFT corpus combining traces from three frontier models across math, code, reasoning, instruction-following, tool-use, science, long-context, multilingual, and creative dialogue domains.
Teachers
Teacher
Provider
Traces
Qwen3.8-Max-Preview
Alibaba Cloud Model Studio
48,283
GLM-5.2
Z.AI Coding Plan
5,307
Kimi Code K3
Moonshot AI (Kimi)
4,347… See the full description on the dataset page: https://huggingface.co/datasets/r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation.Qwen3.5-4B-Base
juiceb0xc0de/Qwen3.5-4B-Base
A brain atlas for Qwen/Qwen3.5-4B-Base, a 32-layer hybrid that runs linear attention on 24 layers and full attention on the other 8. This is not a chat dataset or a benchmark. It is an internal-mechanics map built by running activations through a corpus of prompts and scoring what each layer, component, head, and feature direction is doing.
This is a base model, before any instruction tuning, so whatever structure shows up here was put there by… See the full description on the dataset page: https://huggingface.co/datasets/juiceb0xc0de/Qwen3.5-4B-Base.qwen3.5-9b-atlas
qwen3.5-9b-atlas
qwen35-4b
qwen35-4b
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38203125
Action score: 0.4375
Valid samples: 320/320
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_6-epoch4-iter1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3953125
Action score: 0.446875
Valid samples: 320/320
qwen35-4b-reeval3
qwen35-4b-reeval3
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3859375
Action score: 0.4125
Valid samples: 320/320
qwen35-4b-reeval2
qwen35-4b-reeval2
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.384375
Action score: 0.4265625
Valid samples: 320/320
qwen35-4b-reeval1
qwen35-4b-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.39296875
Action score: 0.4203125
Valid samples: 319/320
appworld-qwen35-4b-agent-rl-epoch3
appworld-qwen35-4b-agent-rl-epoch3
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.45859375
Action score: 0.475
Valid samples: 320/320
qwen35-4b-reeval4
qwen35-4b-reeval4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3984375
Action score: 0.4265625
Valid samples: 319/320
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-tmp01-reeval1
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-tmp01-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.40546875
Action score: 0.475
Valid samples: 320/320
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-reeval1
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.4046875
Action score: 0.4703125
Valid samples: 320/320
appworld-qwen35-4b-agent-rl-epoch3-reeval1
appworld-qwen35-4b-agent-rl-epoch3-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.4578125
Action score: 0.4921875
Valid samples: 320/320
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.39921875
Action score: 0.44375
Valid samples: 320/320
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-t01
appworld-qwen35-4b-manysource-2k-newprompt-solvability-junhee-epoch8-t01
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.38359375
Action score: 0.4703125
Valid samples: 320/320
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1-reeval1
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1-reeval1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.41328125
Action score: 0.4359375
Valid samples: 320/320
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1
appworld-qwen35-4b-9b-s_signal_5-epoch4-iter1
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.41953125
Action score: 0.4515625
Valid samples: 320/320
Qwen3.8-27B-Distillation-40K
Qwen3.8-27B-Distillation (40K Traces)
Qwen3.8-27B-Distillation is a dataset containing 40,000 reasoning traces distilled from Qwen's latest model — Qwen3.8-27B. We generated this dataset locally by running the model on our own infrastructure. It covers 4 domains with prompts sourced from 12 diverse open-source datasets.
Dataset Overview
Metric
Value
Total Examples
40,000
Teacher Model
Qwen3.8-27B
Model Precision
FP8
Reasoning Effort
medium… See the full description on the dataset page: https://huggingface.co/datasets/faunix/Qwen3.8-27B-Distillation-40K.appworld-qwen35-4b-total-237-audited-jh-epoch2
appworld-qwen35-4b-total-237-audited-jh-epoch2
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3640625
Action score: 0.4328125
Valid samples: 320/320
appworld-qwen35-4b-total-237-audited-jh-epoch6
appworld-qwen35-4b-total-237-audited-jh-epoch6
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.37578125
Action score: 0.421875
Valid samples: 320/320
appworld-qwen35-4b-total-237-audited-jh-epoch8
appworld-qwen35-4b-total-237-audited-jh-epoch8
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.384375
Action score: 0.4390625
Valid samples: 320/320
jepa-qwen3-32b-pure-baselines-2026-05-25
JEPA-Align: Qwen3-32B Safety Defense Matrix
The complete 11-condition Qwen3-32B experiment for Predictive Representation
Alignment (PRA), the paired-view objective introduced in Predictive
Representation Alignment Improves Generalization in LLM Safety.
PRA aligns adversarially rewritten prompts with clean prompts expressing the
same intent. This release contains trained adapters, attack traces, benign
capability evaluations, machine-readable results, and paper-ready tables for… See the full description on the dataset page: https://huggingface.co/datasets/memo-ozdincer/jepa-qwen3-32b-pure-baselines-2026-05-25.Magpie-Qwen2.5-Pro-300K-Filtered
Project Web: https://magpie-align.github.io/
Arxiv Technical Report: https://arxiv.org/abs/2406.08464
Codes: https://github.com/magpie-align/magpie
Abstract
Click Here
High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2.5-Pro-300K-Filtered.qwen35-4b-filter-s_signal5-200-qwen38-27b-newprompt-4k-epoch4
qwen35-4b-filter-s_signal5-200-qwen38-27b-newprompt-4k-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.3890625
Action score: 0.4359375
Valid samples: 320/320
qwen35-4b-filter-solvability-200-qwen38-27b-newprompt-4k-epoch4
qwen35-4b-filter-solvability-200-qwen38-27b-newprompt-4k-epoch4
Portable process-evaluation output. metadata.json is the lightweight source
for aggregate results; the JSONL files are directly loadable; and
artifacts.tar.gz losslessly preserves the original run directory.
Reasoning score: 0.40234375
Action score: 0.421875
Valid samples: 320/320
Magpie-Qwen2.5-Pro-1M-v0.1
Project Web: https://magpie-align.github.io/
Arxiv Technical Report: https://arxiv.org/abs/2406.08464
Codes: https://github.com/magpie-align/magpie
Abstract
Click Here
High-quality instruction data is critical for aligning large language models (LLMs). Although some models, such as Llama-3-Instruct, have open weights, their alignment data remain private, which hinders the democratization of AI. High human labor costs and a limited, predefined scope for prompting prevent… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1.qwen3-8b-activations-l20-l36
Qwen3 8B Activations for Layers 20 and 36
This dataset contains assistant-token residual activations harvested from Qwen/Qwen3-8B over 980000 training conversations from lmsys/lmsys-chat-1m.
We only generated for Layer 20 and 36 because each one costs 2TB and we simply cannot afford to store more :)
You can use this dataset to train SAEs, linear probes, other mech interp models etc, for Qwen3 8B.
We picked Qwen3 8B because this is a small part of a larger experiment to use feature… See the full description on the dataset page: https://huggingface.co/datasets/sammyliu/qwen3-8b-activations-l20-l36.