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-5.1-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.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-5.1-1000000x
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.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-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-5.1-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/EngMuhammadAtef/GLM-5.1-Reasoning-1M-Cleaned.carnice-glm5-hermes-traces
Carnice GLM-5 Hermes Traces
This dataset is a merged release bundle of GLM-5 traces collected through the Hermes Agent harness.
It was generated by running the carnice_trace_prompt_bank_v4 prompt bank through Hermes Agent with:
z-ai/glm-5 via OpenRouter
local/file/terminal/code-execution tools for local tasks
Hermes browser tools plus Tavily-backed web_search / web_extract for web tasks
isolated disposable workspaces per prompt
This release is prepared for Hugging Face upload and… See the full description on the dataset page: https://huggingface.co/datasets/kai-os/carnice-glm5-hermes-traces.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.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:… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/glm-5.1-reasoning-1m-cleaned.glm5.2-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-5.1-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/zhangbo2008/GLM-5.1-Reasoning-1M-Cleaned.glm-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.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-5.1-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/rlandismd/GLM-5.1-Reasoning-1M-Cleaned.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.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
208… See the full description on the dataset page: https://huggingface.co/datasets/abhishekgahlot/GLM-5.1-1000000x.GLM-5.1-Reasoning-1M-filtered
GLM-5.1-Reasoning-1M-filtered
Refusal-filtered subset of zai-org/GLM-5.1-Reasoning-1M.
Filtering
Conservative regex/string match for explicit refusal patterns (e.g., "I cannot",
"I can't", "I'm not able to", "as an AI", "죄송합니다", etc.) applied on the
assistant response field. Drop ratio: ~0.00% (very few refusals in source).
Files
File
Size
Records (kept)
main.jsonl
19 GB
(full main subset, refusals removed)
Math.jsonl
4.1 GB
mathematics reasoning… See the full description on the dataset page: https://huggingface.co/datasets/Jongsim/GLM-5.1-Reasoning-1M-filtered.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.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/txchmechanicus/GLM-5.1-Reasoning-1M-Cleaned.GLM51-swebench-replay
GLM51-swebench-replay
中文
这是一个 GLM-5.1 在 SWE-bench 上的 agentic replay 数据集仓库。目标是让使用者不需要部署 SWE-bench，也不需要复现 Docker/benchmark 环境，就可以直接查看和重放模型的多轮推理、工具调用和最终 patch。
数据来源
轨迹使用 EvalScope 收集，benchmark 使用 EvalScope 中的 official SWE-bench agentic 数据集：
swe_bench_verified_agentic
swe_bench_lite_agentic
运行时使用 SWE-bench 官方容器镜像，收集形态为 agentic + toolcall。模型调用使用 GLM-5.1 的 OpenAI-compatible 接口。
分数汇总
verified_agentic: 363 / 500, Acc/Pass@1 = 72.6… See the full description on the dataset page: https://huggingface.co/datasets/fxiao0369/GLM51-swebench-replay.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.