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--- |
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license: mit |
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base_model: microsoft/Phi-3-mini-4k-instruct |
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tags: |
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- text-to-sql |
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- natural-language-to-sql |
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- phi-3 |
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- unsloth |
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- gguf |
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- ollama |
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- logic |
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datasets: |
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- b-mc2/sql-create-context |
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language: |
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- en |
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library_name: unsloth |
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pipeline_tag: text-generation |
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--- |
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# Phi-3-SQL-Expert (GGUF) |
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This is a specialized **Text-to-SQL** model fine-tuned from the **Microsoft Phi-3-mini-4k-instruct** architecture. It has been optimized using **Unsloth** to provide high-accuracy SQL generation while remaining lightweight enough to run on consumer hardware. |
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## Key Features |
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- **Architecture:** Phi-3-mini (3.8B parameters) |
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- **Quantization:** Q4_K_M GGUF (Optimized balance of speed and logic) |
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- **Training Technique:** Fine-tuned using Lora with [Unsloth](https://github.com/unslothai/unsloth). |
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- **Format:** GGUF (Ready for Ollama, LM Studio, and llama.cpp) |
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## Usage Instructions |
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### Ollama (Recommended) |
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To deploy locally: |
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1. Download the `.gguf` file. |
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2. Create a file named `Modelfile` and paste the following: |
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```dockerfile |
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FROM ./phi3-sql-expert.Q4_K_M.gguf |
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TEMPLATE """<|system|> |
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You are a helpful assistant that writes SQL queries. Given a user question and a table schema, output only the SQL code.<|end|> |
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<|user|> |
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{{ .Prompt }}<|end|> |
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<|assistant|> |
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""" |
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PARAMETER stop "<|end|>" |
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PARAMETER temperature 0.1 |
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PARAMETER num_ctx 2048 |
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``` |
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3. Run ollama create sql-expert -f Modelfile |
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4. Run ollama run sql-expert |
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## Evaluation Data |
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The model was fine-tuned on the sql-create-context dataset, focusing on: |
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Mapping natural language to complex SELECT, WHERE, and JOIN statements. |
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Understanding table schemas provided in the prompt. |
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Maintaining strict SQL syntax. |
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## Recommended Settings |
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Temperature: 0.0 or 0.1 (SQL requires deterministic output). |
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Stop Tokens: Ensure <|end|> is set as a stop sequence to prevent "infinite looping" generation. |
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Context Window: 2048 or 4096 tokens. |
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Model Developer: mrcmilo |
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Base Model: Phi-3-mini-4k-instruct |
