ufrik/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 rows: the pinned r0b0tlab/qwen3.8-max-distillation-50k input release, with its upstream attribution and license metadata retained in the canonical view.
• GLM-derived rows: the pinned local distillation-glm52 run full-20260724-175443, admitted after deterministic replay and family-oracle checks.
• Teacher systems: Qwen3.8-Max via Alibaba Cloud Model Studio and GLM-5.2 via the recorded Z.ai coding service run.
• Upstream benchmark and instruction sources: see PROVENANCE.md for pinned repositories, revisions, source counts, and terms.
• Hosting and dataset tooling: Hugging Face Datasets and Parquet/Arrow.

Dataset Summary

Property	Value
Canonical rows	51,389
Train / validation / test	46,250 / 2,569 / 2,570
Public configs	11
Parquet shards	80
Default config	sft
Card license metadata	other

Config	Rows	Train	Validation	Test
sft	51,389	46,250	2,569	2,570
sft_reasoning	43,132	38,814	2,152	2,166
sft_final	51,389	46,250	2,569	2,570
sft_tools	5,307	4,780	263	264
glm47_native	51,389	46,250	2,569	2,570
openai_messages	51,389	46,250	2,569	2,570
prompt_completion_text	46,082	41,470	2,306	2,306
rl_tool_prompts	5,307	4,780	263	264
canonical	51,389	46,250	2,569	2,570
token_stats	51,389	46,250	2,569	2,570
smoke	256	224	13	19

Version 1 is not a preference dataset. It publishes single-trace, single-completion, rendered-text, and tool-prompt views; it contains no pairwise training surface.

Terms and Release Scope

This release has mixed source rights and is therefore labeled license: other. It is intended for controlled, noncommercial research only while the applicable upstream licenses and service terms are reviewed. In particular, Alibaba Cloud Model Studio terms restrict use of Model Studio outputs for training or developing competing products or services without authorization. See LICENSE and PROVENANCE.md; these notices are not legal advice.

The validation and test splits are benchmark-derived development holdouts, not clean capability evaluations. Benchmark-family rows remain present in the selected release, so these splits and overlapping source benchmarks must not be used as uncontaminated post-training evidence. Use fresh, non-overlapping, date-bounded, or privately held tasks for evaluation.

Loading

The package is ordinary Parquet with explicit Hugging Face dataset configurations. For example:

from datasets import load_dataset

dataset = load_dataset("r0b0tlab/qwen3.8-max-glm5.2-distillation-51389", "sft")

No loading script or repository-executed dataset code is shipped. Training-oriented details and the meaning of each view are in TRAINING.md and SCHEMA.md.

Provenance and Quality

• Input JSONL SHA-256: 0733182e2ae59d357e5022c4971ad0cae2482fd1762f8f1447c3ae2c32c517b9
• Materialization report SHA-256: a32836e98511d4902c666dc8ef1d8fc4da12c15fae14689efc54536db7667648
• Native renderer: zai-org/GLM-4.7-Flash@7dd20894a642a0aa287e9827cb1a1f7f91386b67
• The public package contains only the materialized release views and reviewed assets; admission artifacts and source-side quarantine records are not included.

Read PROVENANCE.md, QUALITY.md, CHANGELOG.md, and manifest.json before redistributing or training.