Qwen2.5-Coder-1.5B-FullBase677-SQL-PEFT

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A 1.5B parameter model fine-tuned from Qwen2.5-Coder-1.5B for Text-to-SQL generation, enabling natural language to SQL query translation with improved accuracy and schema understanding.

Model Details

Model Description

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This model is a 1.5B parameter transformer model fine-tuned for Text-to-SQL generation tasks, built upon Qwen/Qwen2.5-Coder-1.5B. It is designed to translate natural language questions into accurate SQL queries across various database schemas. The fine-tuning process enhances its ability to handle complex SQL structures, improve schema grounding, and generate executable queries for downstream applications such as data querying, analytics automation, and natural language interfaces to databases.

• Developed by: https://www.linkedin.com/in/kuldeep-rathoree/
• Model type: Causal language model
• Language(s) (NLP): English (en)
• License: Apache 2.0
• Finetuned from model: Qwen/Qwen2.5-Coder-1.5B

Model Sources [optional]

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• Repository: ikuldeep1/Qwen2.5-Coder-1.5B-FullBase677-SQL-PEFT
• Paper: N/A
• Demo: Coming soon (Colab demo notebook in progress)

Uses

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This model is intended for converting natural language questions into SQL queries. It can be used to power natural language interfaces for databases, automate data querying, and support research on Text-to-SQL generation.

Direct Use

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The model can be directly used to generate SQL queries from English text prompts using the 🤗 Transformers pipeline or via the Hugging Face Inference API.

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Downstream Use [optional]

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It can be integrated into applications such as:

Data analytics dashboards with natural language interfaces Chatbots that answer database-related questions Tools for automating report generation or data retrieval

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Out-of-Scope Use

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The model is not suitable for:

Executing SQL queries directly on databases without validation Handling non-English inputs (it’s trained primarily on English) Use in production systems without proper testing or query sanitization

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Bias, Risks, and Limitations

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Model Card Contact

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