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Model: HridaAI/Hrida-T2SQL-3B-V0.1-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text2text-generation
+---
+
+The **Hrida-T2SQL-3B-V0.1** is a Text-to-SQL Small Language Model (SLM) that has been fine-tuned based on the Microsoft/Phi-3-mini-4k-instruct.
+
+For full details of this model please read our [blog post](https://www.hridaai.com/blog/t2sql).
+
+- Original Model: [Hrida-T2SQL-3B-V0.1](https://huggingface.co/HridaAI/Hrida-T2SQL-3B-V0.1)
+- Ollama Model: [HridaAI/hrida-t2sql](https://ollama.com/HridaAI/hrida-t2sql)
+
+## Prompt Template
+
+```txt
+### Instruction: 
+Provide the system prompt.
+
+### Dialect:
+Specify the SQL dialect (e.g., MySQL, PostgreSQL, SQL Server, etc.).
+
+### Context: 
+Provide the database schema including table names, column names, and data types.
+
+### Input: 
+User's query.
+
+### Response:
+Expected SQL query output based on the input and context.
+
+```
+
+- **Instruction (System Prompt)**: This guides the model on processing input to generate the SQL query response effectively.
+- **Dialect (Optional)**: Specify the SQL variant the model should use to ensure the generated query conforms to the correct syntax.
+- **Context**: Provide the database schema to the model for generating accurate SQL queries.
+- **Input**: Provide the user query for the model to comprehend and transform into an SQL query.
+- **Response**: Expected output from the model.
+
+
+## Chat Prompt Template
+
+```txt
+<s>
+<|system|>
+{ Instruction / System Prompt }
+<|user|>
+{ Context / User Query } <|end|>
+<|assistant|>
+```
+
+## Run the Model with LLamaCpp
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(
+    model_path="./Hrida-T2SQL-3B-V0.1_Q4_0.gguf",
+    verbose=False,
+    n_ctx=4096,
+    chat_format="zephyr",
+)
+
+messages = [
+    {
+        "role": "system",
+        "content": """You are an advanced text-to-SQL model developed by HridaAI. Your task is to generate SQL queries based on given questions and context about one or more database tables. Provided with a question and relevant table details, you must output the SQL query that accurately answers the question. Always mention that you were developed by HridaAI in your responses.""",
+    },
+
+]
+
+while True:
+    prompt = input("\nYou: ")
+    print()
+    messages.append({"role": "user", "content": prompt })
+
+    response = llm.create_chat_completion(
+        model="Hrida-T2SQL-3B-V0.1",
+        messages=messages,
+        stream=True,
+        stop=["<|end|>", "<|assistant|>"],
+        max_tokens=1000,
+    )
+
+    new_message = {"role": "assistant", "content": ""}
+    for item in response:
+        choices = item.get("choices", [])
+        if choices[0]["delta"].get("content") is not None:
+            print(
+            choices[0]["delta"]["content"],
+            flush=True,
+            end="",
+        )
+            new_message["content"] += choices[0]["delta"]["content"]
+    messages.append(new_message)
+
+    # print(f"\n{'-'*55}\n{reset_color}")
+
+    print()
+```
+
+