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@@ -15,11 +15,11 @@ datasets:
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  - b-mc2/sql-create-context
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  ---
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- ## **Model Card for Llama3.1-3B-SQL-Expert-1Epoch**
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  ### **Model Details**
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  #### **Model Description**
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- Llama3.1-3B-SQL-Expert-1Epoch is a fine-tuned version of Meta’s Llama-3.1-3B, specifically optimized for generating SQL queries from natural language input. The model has been trained using **Unsloth** for efficient fine-tuning and inference.
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  - **Developed by:** Azzedine (GitHub: Azzedde)
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  - **Funded by [optional]:** N/A
@@ -32,7 +32,7 @@ Llama3.1-3B-SQL-Expert-1Epoch is a fine-tuned version of Meta’s Llama-3.1-3B,
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  ---
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  ### **Model Sources**
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- - **Repository:** [Hugging Face](https://huggingface.co/Azzedde/llama3.1-3B-SQL-Expert)
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  - **Paper [optional]:** N/A
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  - **Demo [optional]:** N/A
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@@ -81,8 +81,8 @@ from unsloth import FastLanguageModel
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  from transformers import AutoTokenizer
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  # Load tokenizer and model
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- tokenizer = AutoTokenizer.from_pretrained("Azzedde/llama3.1-3B-SQL-Expert")
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- model = FastLanguageModel.from_pretrained("Azzedde/llama3.1-3B-SQL-Expert")
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  # Example inference
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  sql_prompt = """Below is a SQL database schema and a question. Generate an SQL query to answer the question.
@@ -164,16 +164,16 @@ print(tokenizer.decode(outputs[0]))
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  ### **Citation [optional]**
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  #### **BibTeX:**
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  ```bibtex
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- @article{llama3.1-3B-SQL-Expert,
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  author = {Azzedde},
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- title = {Llama3.1-3B-SQL-Expert: An SQL Query Generation Model},
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  year = {2025},
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- url = {https://huggingface.co/Azzedde/llama3.1-3B-SQL-Expert}
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  }
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  ```
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  #### **APA:**
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- Azzedde. (2025). *Llama3.1-3B-SQL-Expert: An SQL Query Generation Model.* Retrieved from Hugging Face
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  ---
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  - b-mc2/sql-create-context
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  ---
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+ ## **Model Card for Llama3.2-3B-SQL-Expert-1Epoch**
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  ### **Model Details**
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  #### **Model Description**
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+ Llama3.2-3B-SQL-Expert-1Epoch is a fine-tuned version of Meta’s Llama-3.1-3B, specifically optimized for generating SQL queries from natural language input. The model has been trained using **Unsloth** for efficient fine-tuning and inference.
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  - **Developed by:** Azzedine (GitHub: Azzedde)
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  - **Funded by [optional]:** N/A
 
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  ---
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  ### **Model Sources**
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+ - **Repository:** [Hugging Face](https://huggingface.co/Azzedde/llama3.2-3b-sql-expert-1-epoch)
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  - **Paper [optional]:** N/A
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  - **Demo [optional]:** N/A
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  from transformers import AutoTokenizer
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  # Load tokenizer and model
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+ tokenizer = AutoTokenizer.from_pretrained("Azzedde/llama3.2-3b-sql-expert-1-epoch")
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+ model = FastLanguageModel.from_pretrained("Azzedde/llama3.2-3b-sql-expert-1-epoch")
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  # Example inference
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  sql_prompt = """Below is a SQL database schema and a question. Generate an SQL query to answer the question.
 
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  ### **Citation [optional]**
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  #### **BibTeX:**
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  ```bibtex
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+ @article{llama3.2-3B-SQL-Expert,
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  author = {Azzedde},
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+ title = {Llama3.2-3B-SQL-Expert: An SQL Query Generation Model},
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  year = {2025},
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+ url = {https://huggingface.co/Azzedde/llama3.2-3b-sql-expert-1-epoch}
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  }
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  ```
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  #### **APA:**
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+ Azzedde. (2025). *Llama3.2-3B-SQL-Expert: An SQL Query Generation Model.* Retrieved from Hugging Face
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  ---
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