empero-ai/hy3-sft-1500x

Tencent Hy3 Diverse SFT 1500

Diverse synthetic SFT traces distilled from tencent/hy3 through OpenRouter. 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. Unlike summary-style distillation sets, the reasoning here is the teacher's native chain-of-thought: the end-user prompt was sent unmodified and Hy3's own reasoning stream was captured, not a post-hoc explanation. The assistant response uses a consistent reasoning format:

<think>
deliberate reasoning trace
</think>

final response

Dataset summary

• Rows: 1,500
• Task families: 10
• Language: English
• Teacher model: tencent/hy3
• 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 is a 1,500-row split of a larger run: 4,347 completed traces from a 5,000-prompt generation corpus, sampled with a fixed seed and stratified by task family so the split preserves the full set's task distribution. Only completed, structurally valid SFT rows are included.

Using it for SFT

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

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 (tencent/hy3)
messages	Ordered chat turns with system, user, and assistant roles
messages[0].content	Generic system instruction (You are a helpful assistant.)
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	225	Implementation, debugging, refactoring, and code review
quantitative_problem_solving	225	Applied mathematics, statistics, estimation, and numerical reasoning
planning_and_decision_making	180	Practical plans with constraints, resources, and tradeoffs
scientific_and_technical_reasoning	180	Science and engineering explanations, calculations, and predictions
analysis_and_argument	150	Evidence-based analysis of claims, scenarios, and competing explanations
extraction_and_structured_output	150	JSON, tables, classification, extraction, and data transformation
writing_and_transformation	121	Rewriting, editing, summarization, and audience-aware drafting
troubleshooting_and_diagnosis	119	Diagnosis from symptoms, logs, or operational context
logic_and_constraints	75	Scheduling, ordering, logic, and constraint-satisfaction tasks
creative_generation_with_constraints	75	Creative artifacts with testable structural or stylistic requirements

Statistics

measurement	minimum	median	mean	maximum
Reasoning length (words)	90	182	183.5	637
User prompt length (characters)	113	338	356.3	900
Final response length (characters)	3	479	539.0	2,584

All 1,500 rows have the same role sequence:

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, no truncation at the generation token limit, and no hidden teacher/model metadata in the reasoning.
• Prompt generation rejects quiz wording and A/B/C/D multiple-choice formatting. The dataset is designed for general SFT rather than MMLU-style continuation training.
• Because the reasoning is native chain-of-thought, some quantitative rows carry the entire derivation in the reasoning trace and end with a terse final answer (for example, a bare number with units). This is intentional but worth knowing when mixing with datasets that expect verbose final responses.
• 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.

Support / Donate

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

• BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
• LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x
• XMR: 42Dbm5xg5Nq26fdyzfEU7KBnAJfhi7Cvz5J2ex5CzHXkfKuNEJzYCcmJ1GTbgjFZ5MBx72sdG1G9239Cd6rsZfv4QeDkYJY

Links

• Project: https://empero.org
• Teacher route: tencent/hy3
• Generated with OpenStill, Empero's internal distillation toolkit (not publicly available yet), via the OpenRouter chat-completions API