Irfanuruchi/building-engineering-synthetic-dataset-v5 is a synthetic dataset for training engineering reasoning models. It was generated using physics-based engineering equations and structured prompts suitable for LLM fine-tuning. The dataset was used to train the model Irfanuruchi/qwen2.5-1.5b-buildeng-precheck-lora-v5.
Use Cases
- Fine-tuning large language models for building engineering calculations based on physics-based equations.
- Training models to perform sanity checks on building engineering designs based on structured prompts.
- Developing AI assistants for building engineering tasks based on synthetic training data.
Strengths
- Dataset is synthetic and generated using physics-based engineering equations, suggesting controlled and consistent data generation.
- Dataset was used to train a specific model (Irfanuruchi/qwen2.5-1.5b-buildeng-precheck-lora-v5), indicating practical application.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
Provenance
- Source
- Irfanuruchi
- Collection Method
- Generated using physics-based engineering equations and structured prompts.
- Freshness
- Last updated 2026-03-10 20:54:15; freshness should be verified.