saidutta69/qwen-glm-kimi-distillation-clean

🧠 Qwen-GLM-Kimi Distillation Clean

A rigorously cleaned, finetuning-ready multi-teacher SFT corpus distilled from Qwen3.8-Max, GLM-5.2 and Kimi K3 — deduped, length-filtered and normalized for SFT with assistant-only loss.

Priorities: Quality > Cleanliness > Signal

📊 Dataset Overview

Property	Value
Total Records	57,064
Train Split	51,417 (90.1%)
Validation Split	2,833 (5.0%)
Test Split	2,814 (4.9%)
Teachers	3 (Qwen3.8-Max 47,595 / GLM-5.2 5,307 / Kimi K3 3,653)
Source Dataset	r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation (57,937 traces)
Cleaned Version	v1.0-clean (2026-08-22)
License	other (inherits heterogeneous upstream + Alibaba Cloud Model Studio §4.48(d)(v))

📦 Formats Available

This dataset is available in 8 view configs — pick what your trainer needs:

Config	Rows	Description
sft (default)	57,064	ShareGPT-style messages with reasoning_content + tool_calls + trainable — ready for TRL/Axolotl with collators/assistant_only.py
openai_messages	57,064	OpenAI Chat format — reasoning merged as <think>...</think> in assistant content, vanilla apply_chat_template works
prompt_completion_text	49,565	Single-turn prompt / completion (+ prompt_with_system) for base-model SFT
canonical_clean	57,064	Full audit columns (messages_json, tools_json, domain, format_score, etc.)
sft_tools	5,871	Tool/agent traces only (n_tool_calls>0)
sft_math	14,935	Math specialist (math + math_formal)
sft_code	14,472	Code specialist (code + executable_code)
sft_reasoning	10,463	Reasoning specialist

All splits are template-cluster-grouped, deterministic, zero leakage.

🔗 Teachers

Teacher	Provider	Original	Clean Kept	Note
Qwen3.8-Max-Preview	Alibaba Cloud Model Studio	48,283	47,595	688 long >60k removed
GLM-5.2	Z.AI Coding Plan	5,307	5,307	100% kept (tool traces)
Kimi Code K3	Moonshot AI (Kimi)	4,347	3,653	694 trainable=None + 6 long removed

🧹 Quality Pipeline

Step	Removed	Reason
Raw canonical (6-shard deduped)	57,937	Zero-leakage canonical (5-shard legacy 47k duplicates ignored)
Deduplication	0	id already unique in 6-shard set
Structural validation	694	trainable=None (K3 synthetic, format_score 1.0, missing reasoning_content structure)
Length filter	179	messages_json > 60k chars (max 241,925 → OOM on 8k context)
Parse check	0	No JSON parse failures
Final clean	57,064	1.5% dropped — see dropped_rows.parquet

Additional normalization:

• Ensured every message has role, content, reasoning_content, tool_calls, tool_call_id, name, trainable
• trainable=True only on assistant turns (including tool-calling assistants)
• Normalized tools list and <think> handling

📈 Quality & Length Distribution

Format score (1-10, 53k scored rows):

Range	Count
1.0	694 (removed)
4-5	~900
6-7	~12k
7-9	~18k
9-10	~22k

Length (chars, clean): mean 3,487 · median 1,967 · p95 10,750 · max 60,000 (capped from 241,925) · est. tokens (chars/3.5) p95 ~3,070

Quality flags retained (not filtered, for sampling):

Flag	Count
ground_truth_restored_from_pinned_upstream	2,870
expected_tool_sequence_deviation	241
final_only_instruction	168

🏗️ Domain Composition

Domain	Clean Rows	%
math + math_formal	14,935	26.2%
code + executable_code	14,472	25.4%
reasoning	10,463	18.3%
instruction (incl. strict/iterative)	7,273	12.7%
agent_tool	5,871	10.3%
grounded_long_context	1,263	2.2%
science_logic_data	844	1.5%
stateful_dialogue + creative + multilingual	1,943	3.4%

📂 Data Fields

sft / sft_* configs

Column	Type	Description
id	string	Unique trace id
split	string	train / validation / test
messages	string (JSON)	List of {role, content, reasoning_content, tool_calls, tool_call_id, name, trainable}
tools	string (JSON)	List of tool definitions (for agent traces)
sampling_weight	float	Rebalanced weight for WeightedRandomSampler
source	string	Source family (e.g. Evol-Code, MetaMathQA)
domain	string	Normalized domain
teacher_model	string	qwen3.8-max-preview / glm-5.2 / kimi-code/k3
trace_kind	string	single_turn / multi_turn / multi_turn_tool
n_tool_calls	int	Number of tool calls

openai_messages

Column	Type	Description
messages	string (JSON)	OpenAI [{role, content, tool_calls?}] — reasoning merged as <think>…</think>
tools	string (JSON)	Tool definitions

prompt_completion_text

Column	Type	Description
prompt	string	User prompt
completion	string	<think>reasoning</think>\n\nanswer
prompt_with_system	string	System + prompt

🎯 Usage

With Hugging Face Datasets

from datasets import load_dataset
import json

# Default SFT (with reasoning_content)
ds = load_dataset("saidutta69/qwen-glm-kimi-distillation-clean", "sft", split="train")
print(ds[0]["messages"][:500])
msgs = json.loads(ds[0]["messages"])
tools = json.loads(ds[0]["tools"])

# OpenAI merged format — works with vanilla chat templates
ds2 = load_dataset("saidutta69/qwen-glm-kimi-distillation-clean", "openai_messages", split="train")

# Filter specialists
math = load_dataset("saidutta69/qwen-glm-kimi-distillation-clean", "sft_math", split="train")
tools_only = load_dataset("saidutta69/qwen-glm-kimi-distillation-clean", "sft_tools", split="train")

With Axolotl

datasets:
  - path: saidutta69/qwen-glm-kimi-distillation-clean
    type: chat_template  # or sharegpt
    chat_template: qwen2
    config: sft
    split: train

With TRL + Assistant-Only Collator

from transformers import AutoTokenizer, AutoModelForCausalLM
from datasets import load_dataset
import json
from collators.assistant_only import SemanticAssistantCollator

tok = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")

ds = load_dataset("saidutta69/qwen-glm-kimi-distillation-clean", "sft")

def format_fn(ex):
    msgs = json.loads(ex["messages"])
    # collator handles masking; here just render
    return {"text": tok.apply_chat_template(msgs, tokenize=False)}

train_ds = ds["train"].map(format_fn)

from trl import SFTTrainer, SFTConfig
args = SFTConfig(max_seq_length=8192, per_device_train_batch_size=2, gradient_accumulation_steps=4)
trainer = SFTTrainer(model, args, train_dataset=train_ds, formatting_func=lambda x: x["text"],
                     data_collator=SemanticAssistantCollator(tok, max_length=8192))
trainer.train()

With LLaMA-Factory / Unsloth

from unsloth import FastLanguageModel
model, tok = FastLanguageModel.from_pretrained("unsloth/Qwen2.5-7B-Instruct", max_seq_length=8192)
# then load sft or openai_messages as above

🏗️ Chain-of-Thought (CoT)

• sft config: keeps reasoning_content separate + content as final answer. Use collators/assistant_only.py which merges via adapters/semantic_labels.py into <think> + answer with correct labels=-100 masking.
• openai_messages config: already merged as <think>reasoning</think>\n\nanswer — no custom collator needed, standard apply_chat_template works.

⚠️ Limitations & Terms

• License other: heterogeneous upstream (CC BY-NC-SA 4.0, CC BY-NC 3.0, CC BY-SA 3.0, CC BY 4.0, MIT, Apache 2.0, ODC-BY) + Alibaba Cloud Model Studio §4.48(d)(v) restricts using outputs to train competing products without authorization. Noncommercial research only until rights reviewed. This clean inherits same limits — no new rights granted.
• Contamination: contains benchmark-derived items (GSM8K, MATH, HumanEval, MBPP, ARC, SciQ, QASC, OpenBookQA). Validation/test splits are not uncontaminated eval — use fresh tasks for claims.
• Long context: max 60k chars (~17k tokens). For 8k models, SemanticAssistantCollator will raise on truncation — filter or chunk via max_length.

See CLEANING_REPORT.md, LICENSE, PROVENANCE.md (from original) for full details.

📜 Citation

@dataset{qwen-glm-kimi-distillation-clean-2026,
  title={Qwen-GLM-Kimi Distillation Clean: Finetuning-Ready Clean of Qwen3.8-Max / GLM-5.2 / Kimi K3 Distillation},
  author={Sai Dutta Abhishek Dash (clean) and r0b0tlab (original)},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/saidutta69/qwen-glm-kimi-distillation-clean}
}

Original: r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation — please cite both.

📁 Files

data/
  sft/{train,validation,test}.parquet              # 51,417 / 2,833 / 2,814
  openai_messages/{...}                            # merged <think>
  prompt_completion_text/{...}                     # 49,565 single-turn
  canonical_clean/{...}                            # full audit cols
  sft_tools/{...}                                  # 5,871 tool traces
  sft_math/{...} · sft_code/{...} · sft_reasoning/{...}
collators/assistant_only.py                        # fail-closed assistant-only masking
collators/weighted_sampler.py                      # WeightedRandomSampler helper
adapters/semantic_labels.py
curriculum_stages_clean.json
CLEANING_REPORT.md
dropped_rows.parquet                               # 873 removed rows

Questions? Open a Discussion.