Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text2text-generation
+tags:
+- code
+- sql
+- text-to-sql
+- text2sql
+- t2sql
+---
+
+Introducing Hrida-T2SQL-3B-128k-V0.1, our latest small language model (SLM) tailored for data scientists and industry professionals. This advanced model marks a significant upgrade from our previous release, now equipped with an expanded 128k token context window for handling even the most intricate data queries with precision. Powered by the Phi 3 architecture, it effortlessly converts natural language queries into precise SQL commands, enhancing data analysis efficiency and decision-making capabilities. 
+
+For full details of this model please read our [blog post](https://www.hridaai.com/blog/t2sql-128k).
+
+
+## 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
+
+### Using Transformers
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Define the model and tokenizer
+model_id = "HridaAI/Hrida-T2SQL-3B-128k-V0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, trust_remote_code=True)
+
+# Define the context and prompt
+prompt = """
+Answer to the query will be in the form of an SQL query.
+### Context: CREATE TABLE Employees (
+    EmployeeID INT PRIMARY KEY,
+    FirstName VARCHAR(50),
+    LastName VARCHAR(50),
+    Age INT,
+    DepartmentID INT,
+    Salary DECIMAL(10, 2),
+    DateHired DATE,
+    Active BOOLEAN,
+    FOREIGN KEY (DepartmentID) REFERENCES Departments(DepartmentID)
+); 
+
+CREATE TABLE Departments (
+    DepartmentID INT PRIMARY KEY,
+    DepartmentName VARCHAR(100),
+    Location VARCHAR(100)
+); 
+### Input: Write a SQL query to select all the employees who are active.
+### Response:
+"""
+# Prepare the input
+messages = [{"role": "user", "content": prompt}]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
+
+# Generate the output
+outputs = model.generate(inputs, max_length=300)
+print(tokenizer.decode(outputs[0]))
+
+
+```
+
+### Using MLX
+
+```python
+from mlx_lm import generate, load
+
+model,tokenizer = load("HridaAI/Hrida-T2SQL-3B-128k-V0.1")
+
+prompt = """
+Answer to the quey will be in the form of SQL query.
+### Context: CREATE TABLE Employees (
+    EmployeeID INT PRIMARY KEY,
+    FirstName VARCHAR(50),
+    LastName VARCHAR(50),
+    Age INT,
+    DepartmentID INT,
+    Salary DECIMAL(10, 2),
+    DateHired DATE,
+    Active BOOLEAN,
+    FOREIGN KEY (DepartmentID) REFERENCES Departments(DepartmentID)
+); 
+
+CREATE TABLE Departments (
+    DepartmentID INT PRIMARY KEY,
+    DepartmentName VARCHAR(100),
+    Location VARCHAR(100)
+); ### Input: Write a SQL query to select all the employees who are active. ### Response:"""
+
+response = generate(model=model,tokenizer=tokenizer,prompt=prompt, verbose=True)
+
+```