CoolFace
Datasetpublic

Spakie/DeepSeek-V4-Pro-distilled

DeepSeek-V4-Pro-distilled 17,670 general-purpose instruction-following examples distilled from DeepSeek-V4-Pro, fact-checked and patched using GPT-5.5 Thinking. Pipeline Distillation — responses generated via DeepSeek-V4-Pro API Fact-checking — GPT-5.5 Thinking with web search reviewed all examples for factual errors and hallucinations Format Standard chat format, compatible with most SFT frameworks. Each row is one JSON object with a messages… See the full description on the dataset page: https://huggingface.co/datasets/Spakie/DeepSeek-V4-Pro-distilled.

sourceHugging Facemitupdated 4mo agoView on Hugging Face
1likes48downloads
Dataset Card

DeepSeek-V4-Pro-distilled

17,670 general-purpose instruction-following examples distilled from DeepSeek-V4-Pro, fact-checked and patched using GPT-5.5 Thinking.

Pipeline

  1. 1.Distillation — responses generated via DeepSeek-V4-Pro API
  2. 2.Fact-checking — GPT-5.5 Thinking with web search reviewed all examples for factual errors and hallucinations

Format

Standard chat format, compatible with most SFT frameworks. Each row is one JSON object with a messages array:

json
{"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}

Loading

python
from datasets import load_dataset

dataset = load_dataset("Spakie/DeepSeek-V4-Pro-distilled")
train = dataset["train"]

Dataset Structure

  • —messages: list of chat messages
  • —messages[].role: message role, typically user or assistant
  • —messages[].content: message text

Coverage

Broad general SFT mix: factual Q&A, coding, creative writing, explanations, translations, rewrites, and casual conversation.

Notes

  • —Distilled data — responses reflect DeepSeek-V4-Pro's style and knowledge
  • —Fact-checking does not guarantee 100% accuracy
  • —Use as-is or as a base for further filtering/mixing
  • —Free to use since I'm not gonna gatekeep distilled data