datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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
uka-glm-5.2
🏆 uka GLM-5.2 Reasoning
Reasoning trace dataset for QLoRA fine-tuning of coding agents
📋 Overview
uka GLM-5.2 Reasoning is a curated reasoning trace dataset built from GLM-5.2 agent sessions, designed for QLoRA fine-tuning of coding agents.
Why Is It Easy to Use?
Feature
Description
🎯 Ready to Train
ChatML format — works directly with HuggingFace SFTTrainer, no conversion needed
📦 Multiple Formats
Both JSONL (readable) and… See the full description on the dataset page: https://huggingface.co/datasets/hotdogs/uka-glm-5.2.ale-bench-glm5.2-trajectories
ALE-Bench trajectories — glm5.2
CLI-agent trajectories on ALE-Bench
(Sakana AI; AtCoder Heuristic Contest long-horizon algorithm engineering),
problems ahc026 (Stack of Boxes) + ahc039 (Purse Seine Fishing),
lite version (5 public / ~150 private cases), 90-min wall-clock budget per task.
Agent: glm-5.2 requested via claude-code CLI on z.ai — NOTE: z.ai silently served glm-5.3 (per-response model field, 548/550 responses).
Harness (custom, not the paper's): agent works in… See the full description on the dataset page: https://huggingface.co/datasets/AgentNativeResearchLab/ale-bench-glm5.2-trajectories.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.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.glm-5.2-nvfp4-agentic-traces
GLM-5.2 NVFP4 agentic software traces
This snapshot contains 1,989 completed Verifiers invocation records generated
with RedHatAI/GLM-5.2-NVFP4-FP8.
manifest.jsonl is a compact index for filtering and inspection.
data/<arm>.jsonl contains the exact full graph records emitted by Verifiers.
configs/<arm>.toml contains the resolved configuration for each arm.
The snapshot retains successes, failures, truncations, and scoring metadata.
Use solved, reward, has_error, failure_labels… See the full description on the dataset page: https://huggingface.co/datasets/synquid/glm-5.2-nvfp4-agentic-traces.GLM-5.2-Science
GLM-5.2 · Science-50000x
50,000x traces distilled from GLM-5.2 on High reasoning
Physics · Chemistry · Biology
Token Count: 160M
Theres prompt overlap with my Kimi K2.5 dataset science subset, which I think those prompts are getting used in alot of places now
You can use this dataset for any purpose and you dont need to credit me, preferably dont claim it as your own.
hi - ianncity
thinkingcap-condensed-qwen3.8-glm5.2-kimi-k3
ThinkingCap Condensed — Qwen3.8 / GLM-5.2 / Kimi-K3
Condensed ThinkingCap-style reasoning traces for SFT.
1,985 traces: each row pairs a full multi-turn teacher trace (Qwen3.8-Max,
GLM-5.2 or Kimi K3, via
r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation)
with a condensed TC-style version (short <think> + definitive numbered
answer) generated by
bottlecapai/ThinkingCap-Qwen3.6-27B
using the thinkingcap system prompt.
Format: JSONL (data/condensed.jsonl), 1,985 rows, UTF-8.… See the full description on the dataset page: https://huggingface.co/datasets/Davd-b01/thinkingcap-condensed-qwen3.8-glm5.2-kimi-k3.glm-5.2-kernelgym-rollouts
GLM-5.2 KernelGym Rollouts
This dataset contains 3,200 feedback-driven GPU-kernel optimization trajectories
generated by zai-org/GLM-5.2-FP8: 100 validation tasks, two backends (inline
CUDA and Triton), and 16 rollouts per task.
Each trajectory retains the prompt/feedback message history, model responses and
reasoning, extracted kernel code, KernelGym compilation and correctness results,
profiling metadata, token usage, and stopping decision. Every published record
ended with… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/glm-5.2-kernelgym-rollouts.20260717_mini-v2.4.5_glm-5.2glm5.2-general-distill
Teacher-generated instruction/response pairs used to distill small, local student models
(the ADI / Advanced Data Intelligence series) from the frontier teacher glm-5.2.
How it was built
Teacher: glm-5.2 (served via Ollama Cloud as glm-5.2:cloud), queried with
thinking/reasoning disabled so every record is a single clean final answer.
Seed prompts: databricks/databricks-dolly-15k,
filtered to remove items that require an attached context passage — the closed_qa… See the full description on the dataset page: https://huggingface.co/datasets/AdvancedDataIntelligence/glm5.2-general-distill.GLM-5.2-FP8-nemotron-codealpaca-thinking
GLM-5.2-FP8 Nemotron-CodeAlpaca Thinking Dataset
820,790 single-turn conversations generated by zai-org/GLM-5.2-FP8
with thinking enabled.
Prompt source
Rows (public)
Nemotron-Post-Training-Dataset-v2
800,944
CodeAlpaca-20k (corrected prompts, instruction + "\n\n" + input)
19,846
Total
820,790
Generation: temperature=1.0, top_p=0.95, max_tokens=24576, thinking
enabled. The CodeAlpaca prompts here include the input field.
Relationship to… See the full description on the dataset page: https://huggingface.co/datasets/JessieWei/GLM-5.2-FP8-nemotron-codealpaca-thinking.GLM_5.2_Dataset
GLM 5.2 Distilled Reasoning Dataset
A synthetic instruction dataset of 5,000 examples spanning C#, STEM, formal reasoning, technical/systems topics, writing, and conversational exchanges — generated via distillation from GLM 5.2, with explicit chain-of-thought reasoning on every example.
Dataset Summary
Each example is a single (question, chain-of-thought, answer) triple, generated to support fine-tuning smaller open models toward stronger structured reasoning and… See the full description on the dataset page: https://huggingface.co/datasets/Jimbock/GLM_5.2_Dataset.GLM-5.2-FP8-magpie-ultrachat
GLM-5.2-FP8 Regenerated Responses (Magpie + UltraChat mix)
A combined instruction-response dataset of 507,864 single-turn conversations. The
prompts are drawn from two public instruction datasets; the responses were freshly
regenerated with zai-org/GLM-5.2-FP8.
It was built as on-policy distillation data for training speculative-decoding drafts
(DFlash / DSpark) for GLM-5.2 — i.e. so the draft learns from GLM-5.2's own output
distribution — but it is a general-purpose GLM-5.2… See the full description on the dataset page: https://huggingface.co/datasets/mgoin/GLM-5.2-FP8-magpie-ultrachat.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/ansulev/GLM-5.2-Conversation.glm5.2-distill
GLM5.2 Distillation Dataset
数据集描述
本数据集为 GLM5.2 蒸馏（Distillation）数据，包含中文和英文两部分，采用 seed_driven 策略生成，覆盖计算机科学、数学、人工智能、工程技术、医学健康等 10 个领域。可用于大语言模型的监督微调（SFT）与知识蒸馏。
数据规模
语言
文件
条目数
中文
ga_generated_zh.jsonl
17,126
英文
ga_generated_en.jsonl
18,419
合计
35,545
数据格式
每条数据为 JSON Lines 格式，字段说明如下：
字段
类型
说明
system
string
系统提示词，定义模型角色与领域
instruction
string
用户输入/指令
output
string
模型回答
metadata
object
元数据，包含… See the full description on the dataset page: https://huggingface.co/datasets/ViperEkura/glm5.2-distill.qwen3.8-max-glm5.2-kimi-k3-distillation-sua
qwen3.8-max-glm5.2-kimi-k3-distillation — System/User/Assistant format
Converted from r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation
(canonical config, current shard set train-*-of-00006; the stale of-00005 shards in the source repo were excluded).
Conversion date: 2026-08-20. License: inherited from the source — see LICENSE (controlled, noncommercial research scope).
Format
One JSON object per line, standard OpenAI-style chat format:
{"messages": [
{"role":… See the full description on the dataset page: https://huggingface.co/datasets/EuroswarmsInstitute/qwen3.8-max-glm5.2-kimi-k3-distillation-sua.SERA-GLM5.2-Django-SWEAgent-T1
SERA GLM-5.2 Django SWE-Agent — T1 (first-rollout trajectories)
167 software-engineering agent trajectories generated with the SERA SVG pipeline (paper).
Teacher: GLM-5.2 (temperature 0.6), reasoning traces preserved in <think> blocks
Harness: SWE-agent (str_replace_editor, bash, submit tools), 75-step cap, SWE-Bench Django container (django__django-7530, base commit f8fab6f9)
Stage: rollout one — vague bug prompt over 200 randomly sampled Django functions; kept submitted… See the full description on the dataset page: https://huggingface.co/datasets/thientrangngv/SERA-GLM5.2-Django-SWEAgent-T1.SERA-GLM5.2-Django-SWEAgent-Raw-T1
SERA GLM-5.2 Django SWE-Agent - RAW T1 (first rollout, thinking enabled)
258 raw, pre-postprocess first-rollout trajectories with native GLM-5.2 reasoning traces, from the SERA SVG pipeline (paper).
Released raw so you can choose your own filtering, verification threshold and reasoning-trace handling. Companion: SERA-GLM5.2-Django-SWEAgent-Raw-T2.
Schema
Mirrors allenai/Sera-*-T1/T2:
column
notes
messages
JSON string - apply json.loads(). Raw SWE-agent… See the full description on the dataset page: https://huggingface.co/datasets/thientrangngv/SERA-GLM5.2-Django-SWEAgent-Raw-T1.SERA-GLM5.2-Django-SWEAgent-Raw-T2
SERA GLM-5.2 Django SWE-Agent - RAW T2 (second rollout, thinking enabled)
204 raw, pre-postprocess second-rollout trajectories with native GLM-5.2 reasoning traces, from the SERA SVG pipeline (paper).
Each row is an independent attempt at the synthetic PR generated from a first rollout; target_patch holds that first-rollout patch so you can recompute soft verification at any r. Companion: SERA-GLM5.2-Django-SWEAgent-Raw-T1.
Schema
Mirrors allenai/Sera-*-T1/T2:… See the full description on the dataset page: https://huggingface.co/datasets/thientrangngv/SERA-GLM5.2-Django-SWEAgent-Raw-T2.GLM-5.2-BF16-KLD-Reference-Logits-20260618
GLM-5.2 BF16 KLD Reference Logits
Reference logits for local GLM-5.2 KLD checks.
Contents:
prefill/logits_0.safetensors: BF16 prefill prompt logits generated from
zai-org/GLM-5.2 with context length 2048, stride 512, one window.
decode/decode_teacher_bf16_ref_ctx2048_t17_20260618.safetensors: BF16
teacher-forced decode logits for prompt length 2048 and 17 decode tokens.
decode/decode_teacher_bf16_ref_ctx2048_t17_20260618.safetensors.json:
metadata for the decode reference.… See the full description on the dataset page: https://huggingface.co/datasets/festr2/GLM-5.2-BF16-KLD-Reference-Logits-20260618.SERA-GLM5.2-Django-SWEAgent-T2
SERA GLM-5.2 Django SWE-Agent — T2 (second-rollout trajectories, r=0 / unverified)
165 software-engineering agent trajectories generated with the SERA SVG pipeline (paper).
Teacher: GLM-5.2 (temperature 0.6), reasoning traces preserved in <think> blocks
Harness: SWE-agent (str_replace_editor, bash, submit tools), 75-step cap, SWE-Bench Django container (django__django-7530, base commit f8fab6f9)
Stage: rollout two — a fresh agent solves the synthetic PR generated from rollout… See the full description on the dataset page: https://huggingface.co/datasets/thientrangngv/SERA-GLM5.2-Django-SWEAgent-T2.GLM-5.2-Agent-Distilled
GLM-5.2-Agent-Distilled
A curated and distilled conversational dataset derived from the AletheiaResearch/GLM-5.2-Agent dataset.
This dataset reconstructs agent interactions into clean conversational trajectories while removing unnecessary metadata and hidden reasoning. It is intended for Supervised Fine-Tuning (SFT) of modern Large Language Models (LLMs) with support for tool-use workflows.
Unlike traditional chat datasets, this dataset preserves the execution flow of agent… See the full description on the dataset page: https://huggingface.co/datasets/Srijan-Chakraborty/GLM-5.2-Agent-Distilled.GLM-5.2-BF16-KLD-Reference-Logits-20260708
GLM-5.2 BF16 KLD Reference Logits 20260708
This dataset contains the current GLM-5.2 BF16 reference prompt logits used for
the July 2026 vLLM/Blackwell KLD checks.
The reference cache is intended for candidate-side KLD comparisons without
rerunning the expensive BF16 reference pass.
Files
reference-logits/logits_0.safetensors
reference-logits/manifest.json
generation-log/config.env
generation-log/scoremode_kld.log
Reference Generation
Field… See the full description on the dataset page: https://huggingface.co/datasets/festr2/GLM-5.2-BF16-KLD-Reference-Logits-20260708.GLM-5.2-Finance-80000x-ru
GLM-5.2-Finance-80000x (RU) — очищенная версия
Набор из 78 652 финансовых reasoning-трассировок на русском языке с цепочками рассуждений <think>. Подготовлен для тонкой настройки (SFT) русскоязычных LLM в финансовой области.
Характеристики
Параметр
Значение
Записей
78 652
Формат
OpenAI chat (messages с role/content)
Структура
строго user → assistant, один ход
Рассуждения <think>…</think>
100% записей
Токенов (GLM-5.2 tokenizer)
~268 млн… See the full description on the dataset page: https://huggingface.co/datasets/mizinovmv/GLM-5.2-Finance-80000x-ru.GLM-5.2-KLD-item13-Calibration-Plan
GLM-5.2 KLD item13 Calibration Panel — Plan & Row Hashes
Reproduction package for the calibration-domain KLD measurements cited on
jpsequeira/GLM-5.2-EXL3-TR3-3.40bpw-KVarN-K4V2
(the "0.0558 of-record" / "0.0555 on tr3.2" panel).
Read the model card's retraction section first. These numbers are
calibration-domain (in-distribution): the corpus family is the same one the
quantization allocation was tuned on. They measure in-distribution serving
fidelity, not held-out… See the full description on the dataset page: https://huggingface.co/datasets/jpsequeira/GLM-5.2-KLD-item13-Calibration-Plan.