---
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

```python
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.