Jackrong/DeepSeek-V4-Pro-Distilled-200K

DeepSeek‑V4‑Pro‑Distilled‑200K

High-quality Math & STEM reasoning distilled from DeepSeek‑V4‑Pro in Max mode

Reasoning traces · Proofs · Verification · Mathematics · Physics · Chemistry · Biology

Overview

DeepSeek‑V4‑Pro‑Distilled‑200K is a supervised fine-tuning collection of long-form mathematical and scientific reasoning. Its responses were generated with DeepSeek‑V4‑Pro in Max inference mode, then normalized into a compact conversational schema for model distillation.

The release is intentionally simple: every example belongs to one of only two configurations—Math or STEM—and every record uses the same six top-level fields.

Dataset at a Glance

	Math	STEM	Total
Records	158,383	29,644	188,027
Shards	32	6	38
Split	train	train	train
Teacher	DeepSeek‑V4‑Pro‑Max	DeepSeek‑V4‑Pro‑Max	—

Data Schema

Every row contains exactly six top-level fields:

Field	Type	Description
id	string	Stable record identifier
conversations	list	One human turn followed by one gpt turn
input	string	User prompt
output	string	Teacher-generated reasoning response and final answer
domain	string	Fine-grained source domain
meta	object	Minimal provenance metadata for the selected configuration

{
  "id": "sample-id",
  "conversations": [
    {"from": "human", "value": "Solve the problem..."},
    {"from": "gpt", "value": "<think>...</think>\nFinal answer..."}
  ],
  "input": "Solve the problem...",
  "output": "<think>...</think>\nFinal answer...",
  "domain": "math",
  "meta": {
    "license": "cc-by-4.0",
    "dataset": "Nemotron-SFT-Math-v4",
    "teacher_model": "DeepSeek-V4-Pro-Max"
  }
}

Quality Controls

• 188,027 unique IDs and 188,027 unique normalized inputs.
• Twenty-four duplicated normalized inputs removed globally.
• Conversation turns match input and output exactly.
• Metadata rebuilt from strict configuration-specific allowlists.
• Teacher label normalized to DeepSeek-V4-Pro-Max on every record.
• All gzip JSONL shards passed streaming parse and schema validation.

Downstream Reference Results

The following results measure a 9B reference checkpoint produced by this DeepSeek‑V4‑Pro distillation program. They are included as practical downstream evidence, not as a controlled dataset ablation: model architecture, training recipe, decoding settings, context length, and quantization can all affect the result.

GSM8K

The reference checkpoint achieved 95.28% mean exact-answer accuracy over four independent passes of the complete 1,319-question GSM8K test split.

Run	Correct	Accuracy
1	1,256 / 1,319	95.22%
2	1,252 / 1,319	94.92%
3	1,263 / 1,319	95.75%
4	1,256 / 1,319	95.22%
Mean	5,027 / 5,276	95.28%

For a cross-model comparison, the published reference scores below are shown alongside the distilled checkpoint. The DeepSeek‑V4‑Pro distilled 9B row uses the same 95.28% mean accuracy reported in the four-run table above.

Model	Accuracy
MiMo-V2.5-Pro	99.60%
Llama-3.1-405B-Instruct	96.80%
Llama-3.3-70B-Instruct	94.84%
DeepSeek‑V4‑Pro distilled 9B	95.28%
DeepSeek-V4-Pro	92.60%
DeepSeek-V3	89.30%

Evaluation settings: temperature=1.0, top_p=0.95, 32K context, Q8 MTP checkpoint, exact normalized numeric-answer matching.

MMLU‑Pro selected subjects

On the 1,500-question comparison covering Math, Physics, and Chemistry, the distilled checkpoint reached 90.53% average accuracy. Under the reported evaluation, it led Qwen3.5‑9B official by 0.93 percentage points, Claude Mythos-distilled 27B by 4.33 points, and Claude Mythos-distilled 9B by 9.46 points. These results are downstream reference measurements rather than a controlled dataset ablation.

Model	Math	Physics	Chemistry	Average
DeepSeek‑V4‑Pro distilled 9B	92.4%	89.4%	89.8%	90.53%
Qwen3.5‑9B official	90.6%	89.0%	89.2%	89.60%
Claude Mythos-distilled 27B	88.4%	87.6%	82.6%	86.20%
Claude Mythos-distilled 9B	86.0%	78.6%	78.6%	81.07%

Each model answered the same 500 questions per subject, for 1,500 questions per model. The comparison values and display names are synchronized with the DeepSeek‑V4‑Pro‑Qwen3.5‑9B model card. The source card reports the 9B rows at 8K context and Claude Mythos-distilled 27B at 32K context, so the comparison should be interpreted under those documented evaluation builds rather than as a strict architecture-only comparison.

Loading

from datasets import load_dataset

math = load_dataset(
    "Jackrong/DeepSeek-V4-Pro-Distilled-200K",
    "Math",
    split="train",
    token=True,
)

stem = load_dataset(
    "Jackrong/DeepSeek-V4-Pro-Distilled-200K",
    "STEM",
    split="train",
    token=True,
)

Intended Use

• Supervised fine-tuning for mathematical and scientific reasoning.
• Proof generation, critique, and verification research.
• Long-form chain-of-thought and tool-integrated reasoning experiments.
• Controlled Math/STEM mixture studies.

Limitations

• Model-generated answers can contain factual, mathematical, scientific, or reasoning errors.
• The release is strongly weighted toward Math.
• Exact-input deduplication does not remove all semantic near-duplicates.
• Long reasoning traces may not be appropriate for concise production assistants.
• Benchmark results are reference-checkpoint measurements, not causal proof of improvement from this dataset alone.
• Benchmark comparisons should only be interpreted under the documented evaluation and extraction settings.

Sources, Licensing & Attribution

The consolidated records were selected and transformed from the following source datasets. Their original cards remain authoritative for creation methodology, attribution, ownership, and terms:

1. nvidia/Nemotron‑Math‑Proofs‑v2 — CC BY 4.0.
2. nvidia/Nemotron‑SFT‑Math‑v4 — a record-level mixture of CC BY 4.0 and CC BY-SA 4.0.
3. nvidia/Nemotron‑SFT‑Science‑v2 — CC BY-SA 4.0.

The per-record meta.license value is authoritative when filtering or redistributing examples. Users are responsible for satisfying all attribution and share-alike obligations attached to the records they use. The source datasets were published by NVIDIA Corporation; this consolidated release does not imply endorsement by NVIDIA or DeepSeek.

Disclaimer

This dataset is provided for research and development. Validate data and model outputs independently before consequential use.