Farseen0/opus-4.6-reasoning-sft-12k

Opus 4.6 Reasoning SFT 12k

A unified, pre-cleaned reasoning dataset built from 4 Claude Opus 4.6 distillation sources. Ready for supervised fine-tuning — just load and train.

Why This Dataset Exists

The source datasets have different schemas, null values, and reasoning stored in non-standard keys that apply_chat_template() silently drops. This dataset fixes all of that:

• Reasoning traces merged into assistant content using <think>...</think> tags
• Null/empty content handled (broken samples dropped)
• All schemas unified to standard messages format
• Single dataset load replaces 4 separate downloads + complex formatting logic

Quick Start

from datasets import load_dataset

dataset = load_dataset("Farseen0/opus-4.6-reasoning-sft-12k", split="train")
print(f"{len(dataset)} samples ready for training")

With TRL / Unsloth

from datasets import load_dataset
from trl import SFTTrainer, SFTConfig

dataset = load_dataset("Farseen0/opus-4.6-reasoning-sft-12k", split="train")

# Apply your model's chat template
def format_to_text(example):
    text = tokenizer.apply_chat_template(
        example["messages"], tokenize=False, add_generation_prompt=False,
    )
    return {"text": text}

dataset = dataset.map(format_to_text, num_proc=8, remove_columns=dataset.column_names)

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset,
    dataset_text_field="text",
    args=SFTConfig(output_dir="output", num_train_epochs=2),
)
trainer.train()

Dataset Details

Stat	Value
Total samples	12,929
With reasoning (<think> tags)	12,611 (97.5%)
Without reasoning	318 (2.5%)
Format	messages (list of {role, content} dicts)
Roles	user, assistant
Messages per sample	2 (single-turn)
Content length p50	790 chars
Content length p90	4,310 chars
Content length p99	30,802 chars
Max content length	62,560 chars

Sources

Built from 4 public datasets, all generated using Claude Opus 4.6:

Source	Kept	Dropped	Reason for drops	License
Roman1111111/claude-opus-4.6-10000x	9,632	1	Null content	MIT
Crownelius/Opus-4.6-Reasoning-3300x	2,151	9	Problem text < 10 chars	Apache 2.0
TeichAI/Claude-Opus-4.6-Reasoning-887x	886	0	—	Apache 2.0
crownelius/Opus4.6-No-Reasoning-260x	260	0	—	Apache 2.0

What was fixed per source

Roman1111111 — Reasoning was stored as a separate reasoning key on message dicts (not in content). apply_chat_template() silently ignores this key, so all reasoning traces would be lost without preprocessing. Also had 1 sample with content: null. Generic system prompt removed.

Crownelius/Reasoning — Flat format (problem/thinking/solution columns, not messages). Converted to chat format with thinking embedded as <think> tags. 9 samples dropped for broken/truncated problems under 10 characters.

TeichAI — Messages had thinking and name keys alongside content. The thinking key is silently dropped by chat templates. 58 samples had thinking: null (direct answers without CoT) — kept as-is. System prompt removed.

Crownelius/No-Reasoning — Flat format (original_question/response). Converted to chat format. No reasoning traces by design — these provide general assistant balance to prevent over-reliance on chain-of-thought.

Schema

{
  "messages": [
    {"role": "user", "content": "What is 3x + 7 = 22? Solve for x."},
    {"role": "assistant", "content": "<think>\nI need to solve for x.\n3x + 7 = 22\n3x = 15\nx = 5\n</think>\n\nx = 5. Subtracting 7 from both sides gives 3x = 15, dividing by 3 gives x = 5."}
  ],
  "source": "roman",
  "has_reasoning": true
}

Columns

Column	Type	Description
messages	list[{role: str, content: str}]	Standard chat format, ready for apply_chat_template()
source	str	Origin dataset: roman, crownelius_reasoning, teichai, crownelius_no_reasoning
has_reasoning	bool	Whether the assistant response contains <think> tags

Content Categories

• Mathematics — algebra, calculus, number theory, geometry, GSM8K, MATH (majority of dataset)
• Logic — puzzles, deductive reasoning, constraint satisfaction
• Programming — algorithm design, debugging, code optimization
• Science — physics, chemistry, biology problems
• Bullshit detection — identifying false claims (from TeichAI's Bullshit Bench)
• General knowledge — graduate-level topics in topology, cryptography, astrophysics (from No-Reasoning set)

Model Compatibility

This dataset is model-agnostic. It uses standard messages format that works with any model's chat template:

• Gemma 4 — tokenizer.apply_chat_template() (auto-converts assistant to model role)
• Qwen 3.5 — Direct compatibility
• Llama 3 — Direct compatibility
• Mistral — Direct compatibility

Preprocessing Script

The dataset was built using scripts/build_dataset.py. To rebuild or modify:

python scripts/build_dataset.py --push Farseen0/opus-4.6-reasoning-sft-12k

Acknowledgments

This dataset would not exist without the work of the original dataset creators:

• Roman1111111 — claude-opus-4.6-10000x
• Crownelius — Opus-4.6-Reasoning-3300x and Opus4.6-No-Reasoning-260x
• TeichAI — Claude-Opus-4.6-Reasoning-887x

License

Apache 2.0 (most permissive common license across all sources). The Roman1111111 source uses MIT. Please review source dataset pages for full terms.