id stringclasses 2
values | conversations listlengths 0 0 | instruction stringclasses 2
values | target_tool stringclasses 1
value |
|---|---|---|---|
async_deadlock_detector_1 | [] | [⚡ OmniMCP Free Evaluation Teaser | Full Production Suite (100+ AST Scenarios & Unsloth Scripts): https://bacardy.gumroad.com | Use coupon LAUNCH20 for 20 € off]
| remediate_async_task_deadlock |
async_deadlock_detector_2 | [] | remediate_async_task_deadlock |
🚀 Emgena AI Labs: Asyncio TaskGroup Deadlock & Lock Inversion Defense (Evaluation Teaser Edition)
🔥 READY FOR PRODUCTION MULTI-TURN AGENT FINE-TUNING?
You are currently inspecting the Free Evaluation Teaser (10 multi-turn samples).
🚀 1-Click Interactive Colab Notebook:
(Fine-tune on Free Google Colab T4 GPU in 10 mins)
🏆 Get the Complete Production Suite on Gumroad:
👉 Unlock the Full Asyncio TaskGroup Deadlock & Lock Inversion Defense Package🎟️ Limited Community Launch Coupon: Use code
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🎁 Want a 100% Free Full Package? 👉 Claim the Free FastAPI Async Starter Pack (0 € with code STARTER100)
Feature 🆓 Free Evaluation Teaser 🚀 Full Production Master Package Dataset Size 10 Samples (Preview) 30 - 100+ Enterprise Scenarios Self-Healing & Retries Basic (<20%) >60% Real Error Injections & AST Repairs Turnkey Fine-Tuning Raw Parquet / JSON 1-Click Unsloth Google Colab Notebook & PyTorch Code Commercial Rights Non-commercial Eval 100% Commercial EULA & EU AI Act Article 50/53 Audit Format Compatibility Parquet only Parquet + ShareGPT + Alpaca JSON + AST Schemas ⚡ Instant Download after checkout. 0.0% Syntax Error Guarantee (Pydantic AST Verified).
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Every Emgena dataset is engineered as a precision-fit building block within a comprehensive enterprise AI hierarchy:
🏛️ THE 4-TIER OMNIMCP AI CIVILIZATION HIERARCHY:
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• The unified Emgena Enterprise Campus (900+ multi-turn scenarios).
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| 🏙️ Metropolis | Emgena Master Metropolis | All 19 Villages & 64 Bricks (900+ Samples, 8.11 MB) | Buy on Gumroad (299 €) |
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⚡ 3-Line Python Quickstart (Loading Teaser Data)
from datasets import load_dataset
# Load the free evaluation teaser directly into Hugging Face Datasets
dataset = load_dataset("bacard/omnimcp_async_deadlock_detector_teaser")
print(f"Total Teaser Rows: {len(dataset['train'])}")
print("First conversation turn:")
print(dataset["train"][0]["instruction"])
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