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README.md

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+---
+tags:
+- text-to-image
+- lora
+- diffusers
+- template:diffusion-lora
+widget:
+- output:
+    url: images/Gemini_Generated_Image_s9km42s9km42s9km.png
+  text: '-'
+base_model: Qwen/Qwen2.5-7B-Instruct
+instance_prompt: Model Training, LoRA, QWEN, PEFT
+license: apache-2.0
+---
+# Energy-Intelligence
+
+<Gallery />
+
+## Model description 
+
+This model is a fine-tuned version of Qwen2.5-7B-Instruct specifically optimized for generating complex TimescaleDB queries within the Energy &amp; Utilities domain.
+
+Model Description:
+While general LLMs are proficient in standard SQL, they often struggle with the specialized syntax required for time-series analysis, such as Hypertable operations, Continuous Aggregates, and Materialized Views.
+
+This model was trained on a proprietary dataset of user questions mapped to highly optimized TimescaleDB queries. It understands specific energy-sector nuances, such as:
+
+Time-bucketed power consumption analysis.
+
+Downsampling high-frequency sensor data.
+
+Querying across Materialized Views designed for energy forecasting.
+
+Domain-specific aggregates like approx_percentile and time_weight.
+
+This model was developed using a Distillation&#x2F;Teacher-Student framework to ensure high precision in a compact parameter size:
+
+Synthetic Data Generation (Teacher): We utilized Gemini Pro as a &quot;Teacher&quot; model. We provided it with our specific TimescaleDB schema, DDLs, and energy-domain business logic.
+
+Dataset Curation: The Teacher model generated thousands of complex natural language questions and their corresponding optimized SQL queries, specifically targeting Continuous Aggregates and Hypertable joins.
+
+Fine-Tuning (Student): The Qwen base model was then fine-tuned on this high-precision dataset. This allows the smaller model to achieve &quot;Pro-level&quot; accuracy regarding our specific database structure without the latency or cost of a larger LLM.
+
+## Trigger words
+
+You should use `Model Training` to trigger the image generation.
+
+You should use `LoRA` to trigger the image generation.
+
+You should use `QWEN` to trigger the image generation.
+
+You should use `PEFT` to trigger the image generation.
+
+
+## Download model
+
+
+[Download](/InnovativeEngineers/Energy-Intelligence/tree/main) them in the Files & versions tab.