Datasets:

emgena
/

omnimcp_python_fastapi_async_teaser

language:
  - en
license: apache-2.0
tags:
  - agent
  - agentic-ai
  - axolotl
  - benchmark
  - deepseek
  - evaluation
  - fine-tuning
  - function-calling
  - llama-3.1
  - mistral
  - multi-turn
  - ollama
  - python_fastapi_async
  - qwen-2.5
  - synthetic-data
  - tool-calling
  - tool-use
  - unsloth
pretty_name: Emgena - Python Fastapi Async (Free Teaser Edition)
size_categories:
  - n<1K
task_categories:
  - text-generation
  - question-answering

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🚀 Emgena AI Labs: FastAPI Async Non-Blocking Operations (Evaluation Teaser Edition)

🔥 READY FOR PRODUCTION MULTI-TURN AGENT FINE-TUNING?

You are currently inspecting the Free Evaluation Teaser (10 multi-turn samples).

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👉 Unlock the Full CodeArchitect FastAPI Async - Production Agent Dataset (33+ Production Scenarios)

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Feature 🆓 Free Evaluation Teaser 🚀 Full Production Master Package

Dataset Size 10 Samples (Preview) 33+ Production 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).

Feature	🆓 Free Evaluation Teaser	🚀 Full Production Master Package
Dataset Size	10 Samples (Preview)	33+ Production 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

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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_python_fastapi_async_teaser")

print(f"Total Teaser Rows: {len(dataset['train'])}")
print("First conversation turn:")
print(dataset["train"][0]["conversations"][:2])