saidutta69/qwen3.8-max-distillation-50k-clean

🧠 Qwen3.8-Max Distillation 50K — Clean

A rigorously cleaned single-teacher SFT corpus of 49,661 traces from qwen3.8-max-preview — fixed broken <think> blocks, removed low-quality rows, added multi-format training views.

Priorities: Quality > Cleanliness > Signal

Clean derivative of r0b0tlab/qwen3.8-max-distillation-50k (49,772 rows). Companion to saidutta69/qwen-glm-kimi-distillation-clean.

📊 Dataset Overview

Property	Value
Total Records	49,661
Train Split	44,694 (90.0%)
Validation Split	2,483 (5.0%)
Test Split	2,484 (5.0%)
Teacher	qwen3.8-max-preview (Alibaba Cloud Model Studio)
Source Dataset	r0b0tlab/qwen3.8-max-distillation-50k (49,772)
License	other (inherits Alibaba §4.48(d)(v) + upstream NC licenses)

🧹 Quality Pipeline

Step	Removed	Reason
Raw input	49,772	Original release
Unbalanced <think> blocks	61	Assistant reasoning truncated mid-tag (<think> without </think>) — breaks chat templates
Low quality score	50	quality_score < 5
Final clean	49,661	0.22% dropped · dropped_rows.parquet audit included

Also verified: 0 duplicate ids, 0 empty assistant turns, 0 parse failures, max 42k chars (< 60k cap), no >8k-token truncation artifacts.

📦 Formats Available

Config	Rows	Description
sft (default)	49,661	messages JSON with role/content/tool_calls — ready for TRL/Axolotl/LLaMA-Factory
openai_messages	49,661	Vanilla OpenAI format (<think> already inline in assistant content per upstream raw-output policy)
prompt_completion_text	49,661	Single-turn prompt / completion / system / domain / quality_score
canonical_clean	49,661	Full audit columns incl. ground_truth, difficulty, completion_tokens, generation_time

🏗️ Domain Composition

Domain	Rows	%
math	~16.5k	33%
code	~14k	28%
reasoning	~11.6k	23%
instruction	~7.3k	15%
tool_use	126	0.3%

Quality score: mean 8.59 · median 9.0 · range 5.0–10.0 (after filter)

🎯 Usage

from datasets import load_dataset
import json

ds = load_dataset("saidutta69/qwen3.8-max-distillation-50k-clean", "sft", split="train")
msgs = json.loads(ds[0]["messages"])

pc = load_dataset("saidutta69/qwen3.8-max-distillation-50k-clean", "prompt_completion_text", split="train")

# Axolotl
datasets:
  - path: saidutta69/qwen3.8-max-distillation-50k-clean
    type: chat_template
    config: sft

⚠️ Limitations & Terms

• License other: inherits Alibaba Cloud Model Studio §4.48(d)(v) competitive-training restriction + noncommercial sources (Evol-Instruct-Code CC BY-NC-SA 4.0, SciQ CC BY-NC 3.0 ≈24% of mixture). Noncommercial research only until rights reviewed.
• Contamination: contains GSM8K, MATH, HumanEval, MBPP, ARC, SciQ benchmark items — validation/test are dev holdouts, not clean evals.

📜 Citation

@dataset{qwen38max-50k-clean,
  title={Qwen3.8-Max Distillation 50K Clean},
  author={Sai Dutta Abhishek Dash (clean) and r0b0tlab (original)},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/saidutta69/qwen3.8-max-distillation-50k-clean}
}

📁 Files

data/
  sft/train.parquet + validation.parquet + test.parquet
  openai_messages/...
  prompt_completion_text/...
  canonical_clean/...
dropped_rows.parquet          # 111 removed rows with reasons
cleanup_report.json