Hugging Face's logo

Datasets:
Plans11
/
Organized_PreTrain_WIUAI_1.3M

Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
1M - 10M
Tags:
pretraining
reasoning
chain-of-thought
code
instruct
tool-calling
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
Organized_PreTrain_WIUAI_1.3M / README.md
Plans11's picture
Plans11
Rename README 2.md to README.md
a17909d verified
preview code
|
Raw
History Blame Contribute Delete
6.7 kB
metadata 
license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - pretraining
  - reasoning
  - chain-of-thought
  - code
  - instruct
  - tool-calling
  - chat
  - think
size_categories:
  - 1M<n<10M

ITLL/Organized_PreTrain_WIUAI

A clean, deduped, 4096-context pretrain-ready merge of 31 distillation datasets from 11-47 and WithinUsAI.

All examples >4096 tokens were 100% trashed, never truncated. Duplicates were removed globally across all sources. Outputs are grouped by category for curriculum pretraining.

  • Total Kept: 1,372,185 examples
  • Total Dropped >4096: 1,208 examples
  • Total Duplicates Dropped: ~181k+ (including shard/monolith duplicates)
  • Context: ≤4096 tokens (cl100k_base proxy, hard ceiling)
  • Dedup: SHA256 of normalized lowercased text, global across all datasets

Source Datasets (31)

All audited recursively including subfolders /data, .jsonl / .json / .parquet / .jsonl.gz with automatic gzip sniffing.

11-47/god_agent_grok4.4_cot_traces_20k
11-47/claude_opus_mythos_5k
11-47/claude_opus_4.8_max_thinking_5k_v2
11-47/cluade_mythos_preview_5k_v2
11-47/claude_opus_4.8_distill_5k
11-47/god_level_python_dataset_25k
11-47/Royal_Ghost_Coder_500k
11-47/AI_Fiends_4_This_50k
WithinUsAI/fable_5_distillation_merged_cleaned_25k
WithinUsAI/MiniMax_M2.7_Distilled_5k
WithinUsAI/Llama_4_Maverick_Distilled_5k
WithinUsAI/Meta_Muse_Spark_Distilled_5k
WithinUsAI/kimi_2.6_Thinking_Distilled_5k
WithinUsAI/Qwen3.7_Max_Thinking_dataset_5K
WithinUsAI/GLM_5.1_Thinking_Distilled_5k
WithinUsAI/DeepSeek_V4_Flash_distilled_dataset_5k
WithinUsAI/gemini_3.5_flash_distilled_25k
WithinUsAI/grok_frontier_dataset_v3_100k
WithinUsAI/microsoft_copilot_distilled_25k
WithinUsAI/DEEPMIND_Alpha_Distilled
WithinUsAI/claude_mythos_distilled_25k
WithinUsAI/Gemini_3.2_Pro_Distilled
WithinUsAI/GPT_5.5_Distilled
WithinUsAI/Grok_4.4_Distilled
WithinUsAI/claude_Opus_4.7_Distilled
WithinUsAI/Opus4.7_thinking_max_distill_god_seed_25k
WithinUsAI/GOD_Coder_Complete_DataSet
WithinUsAI/Omega_Genesis_Coder_100k
WithinUsAI/Elite_GOD_Coder_100k
WithinUsAI/python_GOD_coder_100k
WithinUsAI/GOD_Coder_100k

Build Stats (from your Kaggle run)

Dataset Kept Dropped >4096 Dropped Dupe
god_agent_grok4.4_cot_traces_20k 11 0 19989
claude_opus_mythos_5k 24 0 4976
claude_opus_4.8_max_thinking_5k_v2 4982 0 18
cluade_mythos_preview_5k_v2 4986 0 14
claude_opus_4.8_distill_5k 5000 0 0
god_level_python_dataset_25k 276 0 24724
Royal_Ghost_Coder_500k 434124 0 67876
AI_Fiends_4_This_50k 50000 0 0
fable_5_distillation_merged_cleaned_25k 25146 573 0
MiniMax_M2.7_Distilled_5k 5000 0 0
Llama_4_Maverick_Distilled_5k 5000 0 0
Meta_Muse_Spark_Distilled_5k 545 0 4455
kimi_2.6_Thinking_Distilled_5k 5000 0 0
Qwen3.7_Max_Thinking_dataset_5K 5000 0 0
GLM_5.1_Thinking_Distilled_5k 4991 0 9
DeepSeek_V4_Flash_distilled_dataset_5k 5099 0 0
gemini_3.5_flash_distilled_25k 25000 0 0
grok_frontier_dataset_v3_100k 98938 0 1062
microsoft_copilot_distilled_25k 4596 0 20404
DEEPMIND_Alpha_Distilled 2297 0 1703
claude_mythos_distilled_25k 5315 0 19685
Gemini_3.2_Pro_Distilled 19896 7 0
GPT_5.5_Distilled 18013 184 0
Grok_4.4_Distilled 15386 256 0
claude_Opus_4.7_Distilled 28300 188 0
Opus4.7_thinking_max_distill_god_seed_25k 25000 0 0
GOD_Coder_Complete_DataSet 173860 0 1140
Omega_Genesis_Coder_100k 100100 0 0
Elite_GOD_Coder_100k 100100 0 0
python_GOD_coder_100k 100100 0 0
GOD_Coder_100k 100100 0 0

Category Organization

The builder classifies every kept example with this priority:

  1. Tool_Calling - contains tool_calls, function_call, or <tool_call
  2. Think - contains <think>...</think>
  3. Thought - contains <thought>...</thought> (distinct from Think)
  4. Reasoning - CoT markers without think tags
  5. Code_Instruct - name hint coder/code/python
  6. Chat - multi-turn messages/conversations
  7. Instruct - everything else

Output layout on Hub (add-only, never overwritten):

Think/shard_00000.parquet
Think/shard_00001.parquet
...
Thought/shard_00000.parquet
Reasoning/shard_00000.parquet
Code_Instruct/shard_00000.parquet
Code_Instruct/shard_00001.parquet
Instruct/shard_00000.parquet
Chat/shard_00000.parquet
Tool_Calling/shard_00000.parquet

Each shard index is computed from the live repo listing, so re-running the builder never overwrites.

Schema

Each parquet row:

  • text: string - flattened training text, ≤4096 tokens, ready for pretraining
  • n_tokens: int - token count via cl100k_base
  • category: string - one of [Think, Thought, Reasoning, Code_Instruct, Tool_Calling, Chat, Instruct]
  • source_dataset: string - original HF repo id
  • raw: string - original record JSON (truncated to 20k chars)

How it was built (Kaggle-safe)

  • Batch size 5 datasets at a time, deletes raw download after each dataset
  • Metadata files (manifest.json, dataset_infos.json) skipped
  • Monolith train.jsonl that duplicates shard family train-00000-of-...jsonl skipped
  • Gzip magic byte sniffing (0x1f8b) for files misnamed as .jsonl
  • Global dedup set rebuilt from existing Hub shards on every resume (Kaggle /kaggle/working is not durable)
  • Uses HF_TOKEN from Kaggle Secrets label ITLL

Usage 

from datasets import load_dataset

# load everything
ds = load_dataset("ITLL/Organized_PreTrain_WIUAI")

# load only code
code = load_dataset("ITLL/Organized_PreTrain_WIUAI", data_dir="Code_Instruct")

# load Think + Reasoning for curriculum
think = load_dataset("ITLL/Organized_PreTrain_WIUAI", data_dir="Think")
reason = load_dataset("ITLL/Organized_PreTrain_WIUAI", data_dir="Reasoning")

# pretrain streaming
from datasets import load_dataset
stream = load_dataset("ITLL/Organized_PreTrain_WIUAI", streaming=True, split="train")
for ex in stream:
    print(ex["category"], ex["n_tokens"])

Intended Use

Pretraining from scratch with strong reasoning / coding / thinking traces, up to 4k context. For best results, replace cl100k_base length check with your own tokenizer for final filtering.

Builder Code

Full Kaggle cell is stored in builder.py in this repo if you want to reproduce. Requires huggingface_hub, pandas, pyarrow, tiktoken.

License

MIT - inherits from source distillations. Please check original dataset licenses for commercial use. This is a cleaned aggregation for research.