datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.GLM-5.3-Flash-BF16-Teacher-Logits
GLM-5.3-Flash BF16 teacher logits
This dataset contains full-vocabulary float32 teacher logits from the immutable
zai-org/GLM-5.3-Flash-BF16 revision a6c167b62691b2bac901344b65cb651a70f53e43.
It keeps the sealed final KLD panel qualification-only and publishes the
separate non-final calibration panel under role-specific paths.
Qualification-only final windows: 25
Qualification-only final prediction positions: 51175
Vocabulary size: 154880
Teacher receipt:… See the full description on the dataset page: https://huggingface.co/datasets/brandonmusic/GLM-5.3-Flash-BF16-Teacher-Logits.GLM-5.2-AgentThis dataset was generated using teich by TeichAI
GLM-5.2 Agent traces
This directory contains raw agent trace files generated by teich.
JSONL files: 319
Model metadata: glm-5.2
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them.
Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative name-derived MCP… See the full description on the dataset page: https://huggingface.co/datasets/AletheiaResearch/GLM-5.2-Agent.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/o0Biggz0o/qwen3.8-max-glm5.2-kimi-k3-distillation.GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned
GLM-5.1-Reasoning-1M-Cleaned is a cleaned and reformatted derivative of Kassadin88/GLM-5.1-1000000x. It preserves the original four-subset layout (main, PHD-Science, Multilingual-STEM, Math) while converting every example into a unified SFT-ready schema with explicit conversations, input, output, domain, and meta fields.
This release was prepared from the original dataset published by Kassadin88.
Summary
Teacher model in the data: GLM-5.1… See the full description on the dataset page: https://huggingface.co/datasets/Jackrong/GLM-5.1-Reasoning-1M-Cleaned.glm52-usersim-two-pass-gemma-audit-v1
GLM-5.2 Usersim Two-Pass Gemma Audit v1
This dataset has labels for 61,503 answers made by GLM-5.2. The prompts are artificial user prompts from lyraaaa/synthprompts_v2_250k.
The first working set had 10,000 prompts. It was sampled from 250,000 prompts with seed 20260806 and source revision f286925651e23e7f1d44b22b4f03241dbee9129e. The sample was stratified. This means it kept a similar mix of mode, language, and length.
Gemma 4 26B first checked those 10,000 prompts. It used… See the full description on the dataset page: https://huggingface.co/datasets/kalomaze/glm52-usersim-two-pass-gemma-audit-v1.qwen3.8-max-glm5.2-kimi-k3-distill
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/ansulev/qwen3.8-max-glm5.2-kimi-k3-distill.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/p-research/qwen3.8-max-glm5.2-kimi-k3-distillation.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/inferenceport-ai/qwen3.8-max-glm5.2-kimi-k3-distillation.glm53-flash-harvest
GLM-5.3-Flash On-Policy Harvest
86,006 responses / 246,034,910 generated tokens written by
zai-org/GLM-5.3-Flash from its reference FP8 weights,
across four harvest rounds, 15 registers and both serving modes (22,016 rows carry the
model's inline <think>…</think> chain). It is on-policy text: the corpus records what the target model
actually generates, which is what a speculative-decoding drafter (EAGLE-3 / DFlash / DSpark family) has to
learn to predict. Everything here is MIT.… See the full description on the dataset page: https://huggingface.co/datasets/Zek-Takai/glm53-flash-harvest.glm-5.2-coding-and-debugging-traces
GLM 5.2 Agent Traces
207 TRAJECTORIES · 1,821 TRAINING ROWS · 1 MB PARQUET · 35 MB JSONL
Generated by moonshiner — an open harness for
distilling verified instruction-following, tool-use, and agentic coding traces.
Behavior-preserving instruction-following, tool-use, and agent trajectories
from GLM 5.2 (glm-5.2). The category and row-share tables
below describe the actual mix seen during training rather than assuming a
particular task domain.
This is an actively growing… See the full description on the dataset page: https://huggingface.co/datasets/greghavens/glm-5.2-coding-and-debugging-traces.qwen3.8-max-glm5.2-distillation-51389
Qwen3.8-Max / GLM-5.2 Distillation — 51,389 Rows
A deterministic, public Parquet release of admitted teacher traces for supervised fine-tuning, reasoning-format studies, tool-use studies, and tokenizer-specific rendering experiments. The sft configuration is the default training view. The package contains data and documentation only; it does not require executable dataset code.
Credits and Attribution
Dataset assembly and release packaging: r0b0tlab.
Qwen-derived… See the full description on the dataset page: https://huggingface.co/datasets/ufrik/qwen3.8-max-glm5.2-distillation-51389.open-perfectblend-glm5.2-regen
open-perfectblend-glm5.2-regen
On-policy regeneration of the full mlabonne/open-perfectblend
with GLM-5.2-FP8, built to train speculative-decoding drafters (dspark / DFlash).
1,420,229 conversations in ShareGPT-style {id, conversations: [{from, value}], source}.
Each assistant turn was regenerated by GLM-5.2-FP8 conditioned on the preceding,
already-regenerated turns — deepspec-style per-turn on-policy regeneration, up to
8k tokens per turn. Original human turns are preserved.… See the full description on the dataset page: https://huggingface.co/datasets/mgoin/open-perfectblend-glm5.2-regen.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/bhadra123/qwen3.8-max-glm5.2-kimi-k3-distillation.GLM-5.2-BenchThis dataset was generated using teich by TeichAI
GLM-5.2 Bench results
This directory contains raw agent trace files generated by teich.
JSONL files: 42
Model metadata: z-ai/glm-5.2
Training-ready tools
Generated agent traces carry configured or recovered tool schemas so tools remain available for training even when a session did not call them.
Native Claude Code imports recover schemas for Claude Code and Claude Desktop built-ins, plus conservative name-derived… See the full description on the dataset page: https://huggingface.co/datasets/AletheiaResearch/GLM-5.2-Bench.glm-5.3-flash-distillation-chat
Private distill of domofon/finetome-cot-100k instructions through GLM-5.3-Flash (AutoClaw / Z.AI).
Split
train — successful generations only.
field
description
instruction
user prompt from FineToMe
response
GLM final answer (message.content)
reasoning
GLM chain-of-thought (reasoning_content), empty if not captured
finish
stop or length
prompt_tokens / completion_tokens / reasoning_tokens
usage
latency_s
request latency
source_index
original FineToMe… See the full description on the dataset page: https://huggingface.co/datasets/best-distill/glm-5.3-flash-distillation-chat.claude-code-glm53-swesmith-trajectories
Claude-Code-native Coding Agent Teacher Trajectories (GLM-5.3 × SWE-smith)
English | 简体中文
A private research archive of execution-verified, multi-turn coding-agent trajectories.
A strong teacher (GLM-5.3) drives a real coding-agent harness (Claude Code) inside
verified Docker environments derived from SWE-smith tasks; every trajectory is graded
in a clean verifier container against the task's exact FAIL_TO_PASS / PASS_TO_PASS tests.
⚠️ PRIVATE dataset. Raw wire traces contain… See the full description on the dataset page: https://huggingface.co/datasets/liangzhidanta/claude-code-glm53-swesmith-trajectories.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/alliabba26/qwen3.8-max-glm5.2-kimi-k3-distillation.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/Distillio/qwen3.8-max-glm5.2-kimi-k3-distillation.GLM-5.2-Conversation
GLM-5.2 · Conversation-50000x
50,000x traces distilled from GLM-5.2 on High reasoning
Token Count: 120M
Distribution:
Speaking domains:
•Greetings
•Customer Support
•Step by step explanations
•Motivational language
•Logical Questions
•Creative Writing
STEM:
•Algebra, calculus, quantum mechanics concepts
•Astromony and astrophysics
•Datascience and machine learning
•Biology
Programming:… See the full description on the dataset page: https://huggingface.co/datasets/ianncity/GLM-5.2-Conversation.GLM-5.2-Finance-80000x
GLM-5.2 · Finance-80000x
80,000x financial related traces distilled from GLM-5.2 on High reasoning
Risk · Markets · Investments · Corporate Finance · Wealth Management
Token Count: 220M
Unique prompts generated with diffusion Gemma-27B answered by GLM-5.2
You can use this dataset for any purpose and you dont need to credit me, preferably dont claim it as your own.
hi - ianncity
glm52-demolition-data
GLM-5.2-Demolition — Training & Calibration Data
Apple Silicon AI hub ·
Model release ·
MLX code sample
Preview scope, checked September 10, 2026: the default Hub viewer indexes
87,586 rows (84,231 train, 3,277 validation, 78 test). The original release
total below describes the broader JSONL repository. Use the file browser and
explicit file selections when reusing a particular corpus. The hub includes
a checked download example for the seven-row MLX code sample.
The data… See the full description on the dataset page: https://huggingface.co/datasets/philipjohnbasile/glm52-demolition-data.GLM-5.1-Reasoning
OctoMed/GLM-5.1-Reasoning
Single-turn instruction-following examples with explicit chain-of-thought reasoning,
converted to OctoMed format for SFT training.
Source
Derived from Jackrong/GLM-5.1-Reasoning-1M-Cleaned
by Jackrong. All credit for the original data collection,
distillation from GLM-5.1, and cleaning goes to the original authors.
Format
Each example contains:
question: the instruction / question text (from input field)
responses: the full model… See the full description on the dataset page: https://huggingface.co/datasets/OctoMed/GLM-5.1-Reasoning.GLM-5.1-1000000x
GLM-5.1-1000000x
1,003,589 reasoning traces distilled by GLM-5.1, using questions from KIMI-K2.5-1000000x.
Each entry contains a full chain-of-thought reasoning trace followed by the final answer, generated by GLM-5.1.
Complete! All 1,003,589 prompts distilled successfully.
████████████████████████████████ 100%
Data Distribution
Subset
Count
Proportion
Est. Tokens
Domain
main
598,366
59.6%
~3.04B
General reasoning & instruction-following
Math… See the full description on the dataset page: https://huggingface.co/datasets/clzoro/GLM-5.1-1000000x.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/Lalo42/qwen3.8-max-glm5.2-kimi-k3-distillation.glm-5.3-flash-ifbench-openrouter
GLM-5.3-Flash IFBench OpenRouter five-run results
This dataset contains content-free results from an independent five-run
evaluation of z-ai/glm-5.3-flash on the official IFBench test set through
OpenRouter's first-party Z.AI provider.
This is not an official Allen Institute for AI, Z.AI, or OpenRouter result.
The evaluated outputs were AI-generated. Prompt, response, and reasoning text
are not included.
Results
Mean prompt-level loose accuracy was 65.5333% across… See the full description on the dataset page: https://huggingface.co/datasets/noahyoungs/glm-5.3-flash-ifbench-openrouter.GLM-5.2-Logic-Puzzles
GLM-5.2 · Logical Puzzles
6000x traces distilled from GLM-5.2 on High reasoning
Token Count: 5M~?
Distribution:
Puzzles:
•Tokenization blindless ex: counting the r's in strawberry
•Goal reasoning ex: the car wash test (theres no car wash question exactly just prompts like it so its not just benchmaxxing)
•Reading comprehension traps
•Temporal reasoning
•Many other categories not worth mentioning
Prompts… See the full description on the dataset page: https://huggingface.co/datasets/ianncity/GLM-5.2-Logic-Puzzles.glm5-next-tiny-cpu-repro-v1This repository is an evidence bundle, not one root-format dataset at repository root.
first/ and repeat/ are separate complete sealed QFS root datasets; comparison/ holds
the comparison receipt and tokenwise result. panel/ is the sealed input panel. Other files
are provenance, logs and reproduction tools. Do not pass the bundle root as a QFS dataset.
GLM5-Next tiny native CPU fixture
This is a complete untrained random-initialized native Glm5NextForConditionalGeneration
wrapper… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm5-next-tiny-cpu-repro-v1.Axiom-1.0-Opus4.7-Kimi2.6-GLM5.2-Deepseek4-Mythos5-Fable5-Qwen3.7
Project Axiom 1.0 (102 GB Reasoning Corpus)
27-Billion Token Pure-Text Chain-of-Thought Corpus Across 7 Frontier Architectures
Executive Summary
Project Axiom 1.0 is a landmark, high-density, multi-architecture reasoning corpus comprising 102 GB of uncompressed, pure-text JSONL data (axiom.jsonl). Curated by Shreyan Gondaliya and the Solstice-AI research team, the dataset synthesizes ~5.74 million unique samples and ~27.3 billion tokens of… See the full description on the dataset page: https://huggingface.co/datasets/Solstice-AI/Axiom-1.0-Opus4.7-Kimi2.6-GLM5.2-Deepseek4-Mythos5-Fable5-Qwen3.7.GLM-5.2-FP8-nemotron-codealpaca
GLM-5.2-FP8-nemotron-codealpaca
Training data for UCloud-org/GLM-5.2-FP8-DFlash,
a DFlash speculative-decoding drafter for
zai-org/GLM-5.2-FP8.
A mix of code / math / chat prompts from two public instruction datasets
(see Composition); all assistant responses are regenerated by GLM-5.2-FP8 so the targets match the
verifier's own output distribution — the data recipe specified in the
DFlash paper (Appendix A.1).
800,022 single-turn conversations, English-dominant
Generation:… See the full description on the dataset page: https://huggingface.co/datasets/JessieWei/GLM-5.2-FP8-nemotron-codealpaca.