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@@ -28,9 +28,9 @@ Phi-3 Mini is a transformer-based language model fine-tuned to generate SQL quer
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  - **Model type:** Transformer-based Language Model for SQL Generation
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  - **Language(s) (NLP):** English (and SQL)
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  - **License:** MIT
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- - **Finetuned from model [optional]:** Phi-3-mini-4k-instruct base model
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- ### Model Sources [optional]
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  <!-- Provide the basic links for the model. -->
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@@ -42,7 +42,7 @@ Phi-3 Mini is a transformer-based language model fine-tuned to generate SQL quer
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  Phi-3 Mini can be used to translate natural language instructions into SQL queries, making it a powerful tool for database querying and management. Users can input descriptive text, and the model will generate the corresponding SQL commands.
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- ### Downstream Use [optional]
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  This model can be integrated into applications such as chatbots or virtual assistants that interact with databases. It can also be used in tools designed for automatic query generation based on user-friendly descriptions.
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@@ -192,7 +192,7 @@ After fine-tuning, the updated model and tokenizer were saved locally and then u
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  <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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  Ignore columns other than "sql_prompt", "sql_context", "sql" from the dataset.
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  - **Model type:** Transformer-based Language Model for SQL Generation
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  - **Language(s) (NLP):** English (and SQL)
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  - **License:** MIT
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+ - **Finetuned from model :** Phi-3-mini-4k-instruct base model
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+ ### Model Sources
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  <!-- Provide the basic links for the model. -->
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  Phi-3 Mini can be used to translate natural language instructions into SQL queries, making it a powerful tool for database querying and management. Users can input descriptive text, and the model will generate the corresponding SQL commands.
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+ ### Downstream Use
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  This model can be integrated into applications such as chatbots or virtual assistants that interact with databases. It can also be used in tools designed for automatic query generation based on user-friendly descriptions.
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  <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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+ #### Preprocessing
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  Ignore columns other than "sql_prompt", "sql_context", "sql" from the dataset.
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