Datasetpublic

JollyFraud/crimeopus-distill-v2

CrimeOpus 4.7 â€” Distilled Coding Dataset Dataset di fine-tuning per CrimeOpus 4.7-v2 (LoRA training). Sources Source Count Type DeepSeek-Chat distillation 157 Multi-domain coding/reasoning Git commit-diff (CrimeCode-IDE) 91 Real codebase patterns Uncensored seed (toxic-dpo + orpo-mix) 179 Refusal-free helpfulness Total 427 Format ChatML messages array: { "messages": [ {"role": "system", "content": "..."}, {"role":… See the full description on the dataset page: https://huggingface.co/datasets/JollyFraud/crimeopus-distill-v2.

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

0likes19downloads

Dataset Card

CrimeOpus 4.7 â€” Distilled Coding Dataset

Dataset di fine-tuning per CrimeOpus 4.7-v2 (LoRA training).

Sources

Source	Count	Type
DeepSeek-Chat distillation	157	Multi-domain coding/reasoning
Git commit-diff (CrimeCode-IDE)	91	Real codebase patterns
Uncensored seed (toxic-dpo + orpo-mix)	179	Refusal-free helpfulness
Total	427

Format

ChatML messages array:

json

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

Stats

—Avg user message: ~281 chars
—Avg assistant response: ~2745 chars
—Total tokens (est): ~323k
—Languages: Italian + English + code
—Refusals filtered: yes (zero detected)

Use

python

from datasets import load_dataset
ds = load_dataset("JollyFraud/crimeopus-distill-v2", split="train")

Used for QLoRA fine-tuning of huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated.