datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
FlashRAG_datasets
⚡FlashRAG: A Python Toolkit for Efficient RAG Research
FlashRAG is a Python toolkit for the reproduction and development of Retrieval Augmented Generation (RAG) research. Our toolkit includes 36 pre-processed benchmark RAG datasets and 16 state-of-the-art RAG algorithms.
With FlashRAG and provided resources, you can effortlessly reproduce existing SOTA works in the RAG domain or implement your custom RAG processes and components.
For more information, please view our GitHub repo… See the full description on the dataset page: https://huggingface.co/datasets/RUC-NLPIR/FlashRAG_datasets.medical_meadow_medical_flashcards
Dataset Card for Medical Flashcards
Dataset Summary
Medicine as a whole encompasses a wide range of subjects that medical students and graduates must master
in order to practice effectively. This includes a deep understanding of basic medical sciences, clinical knowledge,
and clinical skills. The Anki Medical Curriculum flashcards are created and updated by medical students and cover the
entirety of this curriculum, addressing subjects such as anatomy, physiology… See the full description on the dataset page: https://huggingface.co/datasets/medalpaca/medical_meadow_medical_flashcards.GLM-5.3-Flash-calibration-activations-v1
GLM-5.3-Flash calibration activations v1 (BF16, natural routing)
Per-layer block-input activations of zai-org/GLM-5.3-Flash-BF16 @ b1967181 over 92x2048
tokens of the exllamav3 standard_cal_data corpus (pinned): per context, layer_NNN.attn_in
and layer_NNN.mlp_in (bf16, post-norm linear inputs; mlp_in is the router + expert gate/up
input) and layer_NNN.router_logits (fp32, natural top-8 routing ground truth).
Per-expert Hessians E[xx^T], routing statistics and down-proj inputs… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/GLM-5.3-Flash-calibration-activations-v1.bird-critic-1.0-flash-exp
BIRD-CRITIC-1.0-Flash
BIRD-Critic is the first SQL debugging benchmark designed to answer a critical question:
Can large language models (LLMs) fix user issues in real-world database applications? Each task in BIRD-CRITIC has been verified by human experts on the following dimensions:
Reproduction of errors on BIRD env to prevent data leakage.
Carefully curate test case functions for each task specifically.
Soft EX: This metric can evaluate SELECT-ONLY tasks.
Soft EX + Parsing:… See the full description on the dataset page: https://huggingface.co/datasets/birdsql/bird-critic-1.0-flash-exp.ox-alpha-glm-5.3-flash-distillation-coding-17k-raw
Ox Alpha GLM-5.3-Flash Distillation Coding 17K Raw
A raw collection of 17,138 synthetic coding samples generated with GLM-5.3-Flash, previously exposed through OpenCode under the stealth-model alias Ox Alpha.
The dataset is intended for experimentation with LLM distillation, code-generation models, instruction tuning, supervised fine-tuning, evaluation, and agentic coding systems.
20260729_mini-v2.2.8_gemini-3-5-flashglm53-flash-fidelity-root-v1
fidelity--glm53flash.malaiwah.root.bf16
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from zai-org/GLM-5.3-Flash-BF16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it). Same cut… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-flash-fidelity-root-v1.DeepSeek-V4-Flash-0731-Teacher-Distillation-40513x
DeepSeek V4 Flash 0731 Teacher Distillation — 40,513 Retained Rows
Teacher-distillation corpus generated with
deepseek-ai/DeepSeek-V4-Flash-0731.
The original manifest contained 45,000 unique seeds.
Following generation, QC, retry-based repair, quarantine auditing,
and recovery adjudication, 40,513 rows were retained.
Composition
Bucket
Rows
Coding
5,601
Agentic
9,982
Cyber blue
13,000
Controlled cyber red
6,999
Tool use
4,931
Total
40,513… See the full description on the dataset page: https://huggingface.co/datasets/trjxter/DeepSeek-V4-Flash-0731-Teacher-Distillation-40513x.20260731_mini-v2.4.2_gemini-3-6-flashDeepSeek-v4-Flash-ChatThis dataset was generated using teich by TeichAI
Prepare these datasets for supervised fine-tuning in just a few lines of code — see the Conversion section below.
Teich Test
This directory contains newline-delimited JSON training examples generated by teich.
All assistant responses were generated by deepseek/deepseek-v4-flash.
Rows: 6313
Format
Each file is newline-delimited JSON where every line is already a training example.
Chat-only datasets include messages… See the full description on the dataset page: https://huggingface.co/datasets/TeichAI/DeepSeek-v4-Flash-Chat.medical-meadow-medical-flashcards
Dataset Card for medical-meadow-medical-flashcards
This dataset originates from the medAlpaca repository.
The medical-meadow-medical-flashcards dataset is specifically used for models training of medical question-answering.
Dataset Details
Dataset Description
Each sample is comprised of three columns: instruction, input and output.
Language(s): English
Dataset Sources
The code from the original repository was adopted to post it here.
Repository:… See the full description on the dataset page: https://huggingface.co/datasets/flwrlabs/medical-meadow-medical-flashcards.glm53-flash-fidelity-exl3-tr3-6bpw-v1
fidelity--glm53flash.malaiwah.quant.tr3-6bpw
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/GLM-5.3-Flash-TR3-6bpw.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-flash-fidelity-exl3-tr3-6bpw-v1.gemini-3-flash-preview
Gemini 3 Flash Preview
This is a reasoning dataset created using Gemini 3 Flash Preview with a reasoning depth set to high.
The dataset is meant for creating distilled versions of Gemini 3 Flash Preview by fine-tuning already existing open-source LLMs.
This dataset contains a collection of prompts categorized by themes such as benchmarks, psychology, web development, embedded systems, creative writing, design, finance, legal, marketing, and science. No real benchmark questions were… See the full description on the dataset page: https://huggingface.co/datasets/TeichAI/gemini-3-flash-preview.dolma-reddit-to-flashcards-0625
Overview
Dolma Reddit to Flashcards is a dataset of synthetically-generated QA items created on the basis of filtered Reddit data.
The creation of this dataset was motivated by the observation in Dolma (Soldaini et al. 2024) that the original Dolma Reddit data showed no benefit from inclusion of thread-level context over isolated submissions and comments, and that clean performance distinctions between tested Reddit versions were limited mainly to the HellaSWAG benchmark.
The… See the full description on the dataset page: https://huggingface.co/datasets/allenai/dolma-reddit-to-flashcards-0625.glm53-flash-fidelity-fp8-v1
fidelity--glm53flash.malaiwah.quant.official-fp8
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from zai-org/GLM-5.3-Flash.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-flash-fidelity-fp8-v1.KrynexAI-Dataset-Flash-Instruction
🧠 KrynexAI Dataset
English | Русский
📌 Overview
KrynexAI Dataset is a high-quality, synthetically expanded collection of 10,000+ instruction-response pairs designed for fine-tuning Large Language Models (LLMs).
The dataset covers a wide range of topics including:
💻 Programming (Python, algorithms, data structures)
🤖 AI & Machine Learning (neural networks, transformers, LLMs)
🔭 Science (physics, cosmology, biology, neuroscience)
🧠 Philosophy & Psychology… See the full description on the dataset page: https://huggingface.co/datasets/KrynexLabs/KrynexAI-Dataset-Flash-Instruction.agent-traces-flashmaniaswival.dev agent traces for single-shot "Reimplement trackmania" prompt using Gemini-3.5-flash.
Generated code: https://github.com/dip-proto/flashmania
gemini_3.5_flash_distilled_25k
Gemini 3.5 Flash Distilled Dataset (25k)
A 25,000-sample synthetic distilled dataset designed to replicate the core capabilities of Gemini 3.5 Flash: frontier-level agentic execution, rapid multi-step reasoning, dense context analysis, and advanced autonomous coding — all optimized for low-latency inference.
Dataset Summary
This dataset was created via template-based evolutionary synthesis with content-normalized SHA-256 deduplication. Every sample features… See the full description on the dataset page: https://huggingface.co/datasets/WithinUsAI/gemini_3.5_flash_distilled_25k.swebench-verified-deepseek-v4-flash-failure-analysis
SWE-bench Verified runs & failure analysis — DeepSeek-V4-flash (local) × mini-swe-agent
Per-instance analysis of SWE-bench Verified runs of a locally-served DeepSeek-V4-flash model
driven by mini-swe-agent, graded with the official
SWE-bench harness. Each instance carries the full agent trajectory, a readable transcript, the
submitted patch, the harness test output, deterministic metrics, and a hand-verified qualitative
root-cause diagnosis.
Current numbers (resolve rates… See the full description on the dataset page: https://huggingface.co/datasets/daaain/swebench-verified-deepseek-v4-flash-failure-analysis.DeepSeek_V4_Flash_distilled_dataset_5k
DeepSeek V4 Flash — Distilled Reasoning Dataset
A synthetic dataset of 5,099 unique reasoning traces designed to mirror the step-by-step thinking style of DeepSeek V4 Flash. Generated entirely with template-based parameterized generation (no LLM API calls).
Format
JSONL (one JSON object per line):
{
"id": "ds4f_math_000042",
"domain": "mathematics",
"subdomain": "algebra",
"difficulty": "easy",
"prompt": "Solve 3x + 7 = 22.",
"reasoning_trace":… See the full description on the dataset page: https://huggingface.co/datasets/WithinUsAI/DeepSeek_V4_Flash_distilled_dataset_5k.HALO-Gemini-3-Flash-AppWorld
Dataset Card: Gemini 3 Flash Traces on AppWorld (test-normal)
Dataset Overview
This dataset contains agent execution traces of Gemini 3 Flash running on the AppWorld benchmark, specifically evaluated on the test-normal dataset split. The traces capture the full span-level execution detail of the model interacting with AppWorld's simulated app ecosystem.
Field
Value
Model
Gemini 3 Flash
Benchmark
AppWorld
Split
test-normal
Total Traces
168
Total Spans
3… See the full description on the dataset page: https://huggingface.co/datasets/inference-net/HALO-Gemini-3-Flash-AppWorld.Gemini-3-Flash-Preview-VIBE
Gemini 3 Flash Preview VIBE
This dataset is our first attempt at an agentic coding SFT dataset.
All of the prompts for this dataset were sourced from MiniMaxAI/VIBE.
Each prompt was given to Gemini 3 Flash Preview with the follow tools and system prompt:
read_file - Read file contents from workspace
write_file - Write content to a file
edit_file - Replace text in a file
list_directory - List files and directories
search_code - Search for patterns in files
run_command - Execute… See the full description on the dataset page: https://huggingface.co/datasets/TeichAI/Gemini-3-Flash-Preview-VIBE.glm-5.3-flash-function-calling
GLM-5.3-Flash Function Calling (synthetic)
A synthetic function-calling dataset generated with zai-org/GLM-5.3-Flash via Hugging Face Inference Providers.
513 examples in 8 domains: weather, calendar, finance, travel, e-commerce, devops, smart home, communication.
Categories: single-turn tool calls, parallel/multiple calls in one turn, multi-turn trajectories with tool results, and no-tool-needed turns.
Format: OpenAI-style — each row has tools (JSON-schema function… See the full description on the dataset page: https://huggingface.co/datasets/Rallex3/glm-5.3-flash-function-calling.20260815_mini-v2.4.2_gemini-3-7-flashglm-5.3-flash-mathnet-bon
glm-5.3-flash-mathnet-bon
This is the continuation and the final set of ox-alpha-mathnet-bon.
Verified chain-of-thought reasoning traces for competition mathematics, generated with GLM-5.3-Flash via best-of-N rejection sampling against the ShadenA/MathNet dataset (ICLR 2026).
Statistics (this split)
Metric
Value
Records (problem × attempt)
6,181
Distinct problems
848
Attempts per problem
7.29 (mean), 8 (max)
Accepted (answer_correct = true)
3,705… See the full description on the dataset page: https://huggingface.co/datasets/zakoman/glm-5.3-flash-mathnet-bon.glm53-flash-fidelity-exl3-tr3-4bpw-miaailab-v1
fidelity--glm53flash.malaiwah.quant.miaailab-4bpw
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from Mia-AiLab/GLM-5.3-Flash-EXL3-TR3-4bpw.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-flash-fidelity-exl3-tr3-4bpw-miaailab-v1.RAGPulse
RAGPulse: A Real-World RAG Workload Trace to Optimize RAG Serving Systems
🌐 Github Link |
🤗 Workload Trace |
📑 Arxiv Paper |
🤖 How to use?
RAGPulse is a real-world RAG workload trace collected from an university-wide Q&A service scenario. The system has been serving over 40,000 students and faculties since April 2024, providing intelligent policy Q&A services. The trace contains a total of 7,106 records entries, sampled from one week of our Q&A service.
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/flashserve/RAGPulse.Deepseek-V4-Flash-11000x
Sherlock Thinking Alpha DeepSeek V4 Flash Distillation
Seed Prompt Dataset
Prompts are sourced from TeichAI/sherlock-thinking-alpha-11000x.
Model
Solutions and reasoning traces were generated with deepseek-ai/DeepSeek-V4-Flash.
DeepSeek-V4-Flash is part of the DeepSeek-V4 preview series. Its model card describes it as a Mixture-of-Experts language model with 284B total parameters, 13B activated parameters, and a 1M-token context length. The model repository is… See the full description on the dataset page: https://huggingface.co/datasets/SLoonker/Deepseek-V4-Flash-11000x.deepseek-v41-flash-thinking-tooluse20260429_mini-v2.2.6_gemini-3-flash