Datasetpublic

tokendr/soliloquy

GPT-5.6 Luna Diverse SFT 900 Diverse synthetic SFT traces distilled from openai/gpt-5.6-luna through an OpenAI-compatible endpoint. Each row is already formatted as a complete system / user / assistant conversation and can be passed directly to a tokenizer's chat template. The prompt mix is intentionally task-diverse rather than benchmark- or multiple-choice-shaped. The assistant response uses a consistent reasoning format: <think> deliberate reasoning trace </think> final… See the full description on the dataset page: https://huggingface.co/datasets/tokendr/soliloquy.

sourceHugging Faceupdated 8d agoView on Hugging Face

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Dataset Card

GPT-5.6 Luna Diverse SFT 900

Diverse synthetic SFT traces distilled from openai/gpt-5.6-luna through an OpenAI-compatible endpoint. Each row is already formatted as a complete system / user / assistant conversation and can be passed directly to a tokenizer's chat template.

The prompt mix is intentionally task-diverse rather than benchmark- or multiple-choice-shaped. The assistant response uses a consistent reasoning format:

text

<think>
deliberate reasoning trace
</think>

final response

Dataset summary

—Rows: 890
—Task families: 10
—Language: English
—Teacher model: openai/gpt-5.6-luna
—Format: chat-template-ready JSONL
—Generated: 2026-07-16
—Generator: OpenStill
—Source file: sft.jsonl

OpenStill is Empero's internal distillation toolkit and is not publicly available yet.

This release contains the 890 traces from a larger 3,500-prompt generation corpus.

Using it for SFT

The messages field is ready for apply_chat_template; no DPO conversion or prompt reconstruction is required.

python

from datasets import load_dataset
from transformers import AutoTokenizer

dataset = load_dataset("json", data_files="sft.jsonl", split="train")
tokenizer = AutoTokenizer.from_pretrained("YOUR_BASE_MODEL")

def render(example):
    return {
        "text": tokenizer.apply_chat_template(
            example["messages"],
            tokenize=False,
            add_generation_prompt=False,
        )
    }

dataset = dataset.map(render)

For trainers that accept conversational datasets directly, use messages without mapping it to a text field.

Fields

field	description
`id`	Stable trace identifier from the source prompt corpus
`topic`	Task family and generated domain, separated by `/`
`model`	Teacher model used to generate the trace (`openai/gpt-5.6-luna`)
`messages`	Ordered chat turns with `system`, `user`, and `assistant` roles
`messages[0].content`	System instruction used for the training conversation
`messages[1].content`	Diverse end-user request
`messages[2].content`	Full `<think>...</think>` reasoning trace followed by the final response

Task distribution

task family	rows	what it contains
`coding_and_debugging`	135	Implementation, debugging, refactoring, and code review
`quantitative_problem_solving`	133	Applied mathematics, statistics, estimation, and numerical reasoning
`planning_and_decision_making`	106	Practical plans with constraints, resources, and tradeoffs
`scientific_and_technical_reasoning`	106	Science and engineering explanations, calculations, and predictions
`analysis_and_argument`	90	Evidence-based analysis of claims, scenarios, and competing explanations
`extraction_and_structured_output`	90	JSON, tables, classification, extraction, and data transformation
`writing_and_transformation`	72	Rewriting, editing, summarization, and audience-aware drafting
`troubleshooting_and_diagnosis`	71	Diagnosis from symptoms, logs, or operational context
`logic_and_constraints`	45	Scheduling, ordering, logic, and constraint-satisfaction tasks
`creative_generation_with_constraints`	42	Creative artifacts with testable structural or stylistic requirements

Statistics

measurement	minimum	median	mean	maximum
Reasoning length (words)	74	164	167.1	274
User prompt length (characters)	258	541	554.5	1,127
Final response length (characters)	87	999	1,377.1	9,130

All 890 rows have the same role sequence:

text

system → user → assistant

Validation & limitations

—Every retained row passed structural validation: valid JSONL, the expected three-message role sequence, a non-empty reasoning trace, a non-empty final response, canonical <think> boundaries, and no hidden teacher/model metadata in the reasoning.
—Structural validation is not deterministic correctness verification. Code was not executed, calculations were not checked against reference answers, and open-ended outputs were not judged by a separate model.
—The data is synthetic and may contain factual mistakes, imperfect code, unsupported assumptions, or stylistic artifacts from the teacher model. Review or filter it for high-stakes use cases.
—The dataset contains explicit reasoning traces. Confirm that the target model, tokenizer, and training recipe handle <think> tags as intended.
—This is a partial snapshot: 890 traces from 3,500-prompt corpus.

Support / Donate

If this dataset or tooling helped you, consider supporting the EmperoAI project:

—BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
—LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x
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