datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GLM-5.3-Flash-TR3-partsbin-v1
GLM-5.3-Flash TR3 parts bin v1 — K6 + K8 payload stores under one transform seed
This dataset is the parts bin for the GLM-5.3-Flash TR3 quantization
campaign (2026-08-27/28): the complete per-choice payload stores of the two
published uniform quants, plus the preparation artifacts and provenance
receipts that produced them.
malaiwah/GLM-5.3-Flash-TR3-6bpw (uniform K6)
malaiwah/GLM-5.3-Flash-TR3-8bpw (uniform K8)
What a parts bin is
TR3 (trellis) encoding is… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/GLM-5.3-Flash-TR3-partsbin-v1.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-5.3-Flash-fidelity-suite-v1
GLM-5.3-Flash Fidelity Suite v1
Historical distribution-fidelity evidence for GLM-5.3-Flash (released 2026-08-26):
BF16-reference and FP8-as-served hidden-state captures, a shared LM head, and
receipts from the declared capture/replay path. Compatible candidate captures
can be compared on matching published positions without holding the 643 GB
reference; this is not a universal native-serving or task-quality score. Protocol: the Qwen3.8-27B fidelity-suite v5 methodology… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/GLM-5.3-Flash-fidelity-suite-v1.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.osworld-glm-5.3-flash-trajThese are the trajectory results from our GLM-5.3-Flash evaluation on OSWorld.
For detailed evaluation results, configuration, and additional information, please refer to the following GitHub issue:
https://github.com/xlang-ai/OSWorld/issues/591
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-5.3-BF16-full-logitsGLM-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-5.3-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.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.GLM-4.7-358B-logits
Dataset Description
This dataset contains model logits extracted from zai-org/GLM-4.7-FP8 using DistillKit.
Source Data
Logits were generated from prompts drawn from the following publicly available datasets:
BEE-spoke-data/fineweb-100k_en-med
flytech/python-codes-25k
agentlans/multilingual-text
These sources collectively provide a mix of English web text, programming-related content, and multilingual natural language data.
Intended Use
This dataset is… See the full description on the dataset page: https://huggingface.co/datasets/JackBinary/GLM-4.7-358B-logits.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/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-ocr-bnk-finetuning
GLM-OCR Fine-Tuning Pipeline
Fine-tuning GLM-OCR 0.9B (CogViT encoder + GLM-0.5B decoder) for Korean financial document table recognition using LoRA via LLaMA-Factory.
Performance Targets
Metric
Target
TEDS (2-level nested)
>= 90%
TEDS (3-level nested)
>= 85%
Korean CER
<= 1%
Latency
<= 0.5s/page
Directory Structure
glm_ocr_finetuning/
├── config/ # Training/eval YAML configs
│ ├── training_config.yaml #… See the full description on the dataset page: https://huggingface.co/datasets/omarelsherif010/glm-ocr-bnk-finetuning.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.GLM_5.2_Training_Dataglm-simple-evals-dataset
glm-simple-evals-dataset
This repository is dedicated to storing various evaluation data required for the glm-simple-evals evaluation project, to enable industry researchers and developers to reproduce the performance of the GLM-4.5 series models on reported benchmarks.
Currently, this repository covers the data required for the following evaluation tasks:
AIME
GPQA
HLE
LiveCodeBench
MATH 500
SciCode
MMLU Pro
Usage Instructions
To use these evaluation datasets… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/glm-simple-evals-dataset.arc-agi3-cc-glm5.2-ar25
ARC-AGI-3 ar25 — Agent Trajectories (cc-glm5.2)
Gameplay trajectories from the harness×model pair cc-glm5.2 playing the
ARC-AGI-3 game ar25, part of the
ARA-as-world-model generalization experiment. The agent builds a structured world model
(an Agent-Native Research Artifact) live during play and consults it to crack levels it
cannot solve from cold exploration.
One dataset repo per harness×model×game: sibling repos
arc-agi3-<harness>-<model>-<game> hold the same game played by… See the full description on the dataset page: https://huggingface.co/datasets/AgentNativeResearchLab/arc-agi3-cc-glm5.2-ar25.arc-agi3-cc-glm5.2-su15
ARC-AGI-3 su15 — Agent Trajectories (cc-glm5.2)
Gameplay trajectories from the harness×model pair cc-glm5.2 playing the
ARC-AGI-3 game su15, part of the
ARA-as-world-model generalization experiment. The agent builds a structured world model
(an Agent-Native Research Artifact) live during play and consults it to crack levels it
cannot solve from cold exploration.
One dataset repo per harness×model×game: sibling repos
arc-agi3-<harness>-<model>-<game> hold the same game played by… See the full description on the dataset page: https://huggingface.co/datasets/AgentNativeResearchLab/arc-agi3-cc-glm5.2-su15.arc-agi3-cc-glm5.2-ls20
ARC-AGI-3 ls20 — Agent Trajectories (cc-glm5.2)
Gameplay trajectories from the harness×model pair cc-glm5.2 playing the
ARC-AGI-3 game ls20, part of the
ARA-as-world-model generalization experiment. The agent builds a structured world model
(an Agent-Native Research Artifact) live during play and consults it to crack levels it
cannot solve from cold exploration.
One dataset repo per harness×model×game: sibling repos
arc-agi3-<harness>-<model>-<game> hold the same game played by… See the full description on the dataset page: https://huggingface.co/datasets/AgentNativeResearchLab/arc-agi3-cc-glm5.2-ls20.Multi-SWE-smith-Rust-GLM-4.6-trajectoriesglm52-datagen-r11-106-instruction-following-citation-tracesqwen3.8-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.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.
arc-agi3-cc-glm5.2-ft09
ARC-AGI-3 ft09 — Agent Trajectories (cc-glm5.2)
Gameplay trajectories from the harness×model pair cc-glm5.2 playing the
ARC-AGI-3 game ft09, part of the
ARA-as-world-model generalization experiment. The agent builds a structured world model
(an Agent-Native Research Artifact) live during play and consults it to crack levels it
cannot solve from cold exploration.
One dataset repo per harness×model×game: sibling repos
arc-agi3-<harness>-<model>-<game> hold the same game played by… See the full description on the dataset page: https://huggingface.co/datasets/AgentNativeResearchLab/arc-agi3-cc-glm5.2-ft09.arc-agi3-cc-glm5.2-r11l
ARC-AGI-3 r11l — Agent Trajectories (cc-glm5.2)
Gameplay trajectories from the harness×model pair cc-glm5.2 playing the
ARC-AGI-3 game r11l, part of the
ARA-as-world-model generalization experiment. The agent builds a structured world model
(an Agent-Native Research Artifact) live during play and consults it to crack levels it
cannot solve from cold exploration.
One dataset repo per harness×model×game: sibling repos
arc-agi3-<harness>-<model>-<game> hold the same game played by… See the full description on the dataset page: https://huggingface.co/datasets/AgentNativeResearchLab/arc-agi3-cc-glm5.2-r11l.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.