Items
1,182 pairs
Cost
Rp 0 (free)
Languages
ID + EN
When I started building a bilingual agentic AI dataset, I had two hard constraints: no budget for commercial API calls, and the data needed to be genuinely diverse across 8 complex agentic task categories — not just basic Q&A.
The Architecture
The solution: run Ollama locally with Mistral-7B and Llama 3. These models are capable enough when prompted correctly. The pipeline was simple but deliberately designed:
code# Core synthesis loop
for category in AGENTIC_CATEGORIES:
for _ in range(TARGET_PER_CATEGORY):
prompt = build_template(category, lang="id")
response = ollama.generate(
model="mistral",
prompt=prompt,
options={"temperature": 0.8}
)
entry = validate_and_clean(response)
if entry: dataset.append(entry)The 8 Agentic Categories
- 01Tool Use & Function Calling
- 02Multi-step Planning & Reasoning
- 03Memory Retrieval & Summarization
- 04Web Search Simulation
- 05Code Generation & Debugging
- 06Document Analysis
- 07Decision Making Under Uncertainty
- 08Cross-lingual Instruction Following
The key insight: prompt template quality matters far more than model size. A well-structured template forces the model to produce consistent, schema-valid outputs. I spent roughly 60% of my time refining templates — not running the pipeline.
What I Learned
Open-source LLMs at 7B parameters are more than capable of producing publishable research-grade data when you constrain the output format precisely. The dataset is now live on Hugging Face and is being actively used by researchers for cross-lingual agentic model evaluation. Open publishing compounds — one dataset becomes 10 citations becomes a reputation.