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Model: HridaAI/Hrida-T2SQL-3B-128k-V0.1 Source: Original Platform
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134
README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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library_name: transformers
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pipeline_tag: text2text-generation
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tags:
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- code
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- sql
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- text-to-sql
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- text2sql
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- t2sql
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---
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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.
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For full details of this model please read our [blog post](https://www.hridaai.com/blog/t2sql-128k).
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## Prompt Template
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```txt
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### Instruction:
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Provide the system prompt.
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### Dialect:
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Specify the SQL dialect (e.g., MySQL, PostgreSQL, SQL Server, etc.).
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### Context:
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Provide the database schema including table names, column names, and data types.
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### Input:
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User's query.
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### Response:
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Expected SQL query output based on the input and context.
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```
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- **Instruction (System Prompt)**: This guides the model on processing input to generate the SQL query response effectively.
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- **Dialect (Optional)**: Specify the SQL variant the model should use to ensure the generated query conforms to the correct syntax.
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- **Context**: Provide the database schema to the model for generating accurate SQL queries.
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- **Input**: Provide the user query for the model to comprehend and transform into an SQL query.
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- **Response**: Expected output from the model.
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## Chat Prompt Template
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```txt
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<s>
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<|system|>
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{ Instruction / System Prompt }
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<|user|>
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{ Context / User Query } <|end|>
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<|assistant|>
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```
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## Run the Model
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### Using Transformers
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Define the model and tokenizer
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model_id = "HridaAI/Hrida-T2SQL-3B-128k-V0.1"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, trust_remote_code=True)
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# Define the context and prompt
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prompt = """
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Answer to the query will be in the form of an SQL query.
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### Context: CREATE TABLE Employees (
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EmployeeID INT PRIMARY KEY,
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FirstName VARCHAR(50),
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LastName VARCHAR(50),
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Age INT,
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DepartmentID INT,
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Salary DECIMAL(10, 2),
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DateHired DATE,
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Active BOOLEAN,
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FOREIGN KEY (DepartmentID) REFERENCES Departments(DepartmentID)
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);
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CREATE TABLE Departments (
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DepartmentID INT PRIMARY KEY,
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DepartmentName VARCHAR(100),
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Location VARCHAR(100)
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);
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### Input: Write a SQL query to select all the employees who are active.
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### Response:
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"""
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# Prepare the input
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messages = [{"role": "user", "content": prompt}]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
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# Generate the output
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outputs = model.generate(inputs, max_length=300)
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print(tokenizer.decode(outputs[0]))
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```
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### Using MLX
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```python
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from mlx_lm import generate, load
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model,tokenizer = load("HridaAI/Hrida-T2SQL-3B-128k-V0.1")
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prompt = """
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Answer to the quey will be in the form of SQL query.
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### Context: CREATE TABLE Employees (
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EmployeeID INT PRIMARY KEY,
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FirstName VARCHAR(50),
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LastName VARCHAR(50),
|
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Age INT,
|
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DepartmentID INT,
|
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Salary DECIMAL(10, 2),
|
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DateHired DATE,
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Active BOOLEAN,
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FOREIGN KEY (DepartmentID) REFERENCES Departments(DepartmentID)
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);
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CREATE TABLE Departments (
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DepartmentID INT PRIMARY KEY,
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DepartmentName VARCHAR(100),
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Location VARCHAR(100)
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); ### Input: Write a SQL query to select all the employees who are active. ### Response:"""
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response = generate(model=model,tokenizer=tokenizer,prompt=prompt, verbose=True)
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```
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13
added_tokens.json
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added_tokens.json
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{
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"<|assistant|>": 32001,
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"<|endoftext|>": 32000,
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"<|end|>": 32007,
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"<|placeholder1|>": 32002,
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"<|placeholder2|>": 32003,
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"<|placeholder3|>": 32004,
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"<|placeholder4|>": 32005,
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"<|placeholder5|>": 32008,
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"<|placeholder6|>": 32009,
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"<|system|>": 32006,
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"<|user|>": 32010
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}
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137
config.json
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config.json
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{
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"architectures": [
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"Phi3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"auto_map": {
|
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"AutoConfig": "configuration_phi3.Phi3Config",
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"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
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},
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"bos_token_id": 1,
|
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"embd_pdrop": 0.0,
|
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"eos_token_id": 32000,
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"hidden_act": "silu",
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"hidden_size": 3072,
|
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
|
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"model_type": "phi3",
|
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"original_max_position_embeddings": 4096,
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"pad_token_id": 32000,
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"resid_pdrop": 0.0,
|
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
|
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"long_factor": [
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1.0700000524520874,
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1.1200000047683716,
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1.149999976158142,
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1.4199999570846558,
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1.5699999332427979,
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1.7999999523162842,
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2.129999876022339,
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2.129999876022339,
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3.009999990463257,
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5.910000324249268,
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6.950000286102295,
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9.070000648498535,
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9.930000305175781,
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10.710000038146973,
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11.130000114440918,
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14.609999656677246,
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15.409998893737793,
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19.809999465942383,
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37.279998779296875,
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38.279998779296875,
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38.599998474121094,
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40.12000274658203,
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46.20000457763672,
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50.940006256103516,
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53.66000747680664,
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54.9373893737793,
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56.89738845825195,
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57.28738784790039,
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59.98738479614258,
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60.86738586425781,
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60.887386322021484,
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61.71739196777344,
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62.91739273071289,
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62.957393646240234,
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63.41739273071289,
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63.8173942565918,
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63.83739471435547,
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63.897396087646484,
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63.93739700317383,
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64.06739807128906,
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64.11434936523438,
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64.12435150146484,
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64.15435028076172,
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64.19435119628906,
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64.24435424804688,
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64.57435607910156,
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64.69000244140625,
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64.76000213623047
|
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],
|
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"short_factor": [
|
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1.1,
|
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1.1,
|
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1.1,
|
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1.3000000000000003,
|
||||
1.3500000000000003,
|
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1.3500000000000003,
|
||||
1.4000000000000004,
|
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1.5500000000000005,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
||||
2.000000000000001,
|
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2.000000000000001,
|
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2.000000000000001,
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2.000000000000001,
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2.000000000000001,
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2.000000000000001,
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2.000000000000001,
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2.000000000000001,
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2.000000000000001,
|
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2.000000000000001,
|
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2.000000000000001,
|
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2.000000000000001,
|
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2.000000000000001,
|
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2.000000000000001,
|
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2.000000000000001,
|
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2.0500000000000007,
|
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2.0500000000000007,
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2.0500000000000007,
|
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2.0500000000000007,
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2.0500000000000007,
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2.0500000000000007,
|
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2.1000000000000005,
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2.1000000000000005,
|
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2.1500000000000004,
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2.25,
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2.25,
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2.25,
|
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2.25,
|
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2.25,
|
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2.3999999999999995,
|
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2.4499999999999993,
|
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2.499999999999999,
|
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2.6999999999999984,
|
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2.6999999999999984,
|
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2.7499999999999982,
|
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2.799999999999998,
|
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2.8999999999999977,
|
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3.049999999999997
|
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],
|
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"type": "longrope"
|
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},
|
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"rope_theta": 10000.0,
|
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"sliding_window": 262144,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.40.2",
|
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"use_cache": true,
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"vocab_size": 32064
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}
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227
configuration_phi3.py
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configuration_phi3.py
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# coding=utf-8
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# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
|
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# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
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""" Phi-3 model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
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"microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
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}
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class Phi3Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
|
||||
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||||
defaults will yield a similar configuration to that of the
|
||||
[microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
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|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
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Args:
|
||||
vocab_size (`int`, *optional*, defaults to 32064):
|
||||
Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`Phi3Model`].
|
||||
hidden_size (`int`, *optional*, defaults to 3072):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 8192):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||
Number of hidden layers in the Transformer decoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||
Number of attention heads for each attention layer in the Transformer decoder.
|
||||
num_key_value_heads (`int`, *optional*):
|
||||
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||||
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||||
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||||
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||||
by meanpooling all the original heads within that group. For more details checkout [this
|
||||
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
||||
`num_attention_heads`.
|
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resid_pdrop (`float`, *optional*, defaults to 0.0):
|
||||
Dropout probability for mlp outputs.
|
||||
embd_pdrop (`int`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the embeddings.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio after computing the attention scores.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||
The maximum sequence length that this model might ever be used with.
|
||||
original_max_position_embeddings (`int`, *optional*, defaults to 4096):
|
||||
The maximum sequence length that this model was trained with. This is used to determine the size of the
|
||||
original RoPE embeddings when using long scaling.
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
|
||||
The epsilon value used for the RMSNorm.
|
||||
use_cache (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||
relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
|
||||
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether to tie weight embeddings
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
rope_scaling (`dict`, *optional*):
|
||||
The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
|
||||
contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be `longrope` and
|
||||
the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
|
||||
divided by the number of attention heads divided by 2.
|
||||
bos_token_id (`int`, *optional*, defaults to 1):
|
||||
The id of the "beginning-of-sequence" token.
|
||||
eos_token_id (`int`, *optional*, defaults to 32000):
|
||||
The id of the "end-of-sequence" token.
|
||||
pad_token_id (`int`, *optional*, defaults to 32000):
|
||||
The id of the padding token.
|
||||
sliding_window (`int`, *optional*):
|
||||
Sliding window attention window size. If `None`, no sliding window is applied.
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import Phi3Model, Phi3Config
|
||||
|
||||
>>> # Initializing a Phi-3 style configuration
|
||||
>>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
|
||||
|
||||
>>> # Initializing a model from the configuration
|
||||
>>> model = Phi3Model(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = "phi3"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=32064,
|
||||
hidden_size=3072,
|
||||
intermediate_size=8192,
|
||||
num_hidden_layers=32,
|
||||
num_attention_heads=32,
|
||||
num_key_value_heads=None,
|
||||
resid_pdrop=0.0,
|
||||
embd_pdrop=0.0,
|
||||
attention_dropout=0.0,
|
||||
hidden_act="silu",
|
||||
max_position_embeddings=4096,
|
||||
original_max_position_embeddings=4096,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=1e-5,
|
||||
use_cache=True,
|
||||
tie_word_embeddings=False,
|
||||
rope_theta=10000.0,
|
||||
rope_scaling=None,
|
||||
bos_token_id=1,
|
||||
eos_token_id=32000,
|
||||
pad_token_id=32000,
|
||||
sliding_window=None,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
|
||||
if num_key_value_heads is None:
|
||||
num_key_value_heads = num_attention_heads
|
||||
|
||||
self.num_key_value_heads = num_key_value_heads
|
||||
self.resid_pdrop = resid_pdrop
|
||||
self.embd_pdrop = embd_pdrop
|
||||
self.attention_dropout = attention_dropout
|
||||
self.hidden_act = hidden_act
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.original_max_position_embeddings = original_max_position_embeddings
|
||||
self.initializer_range = initializer_range
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.rope_scaling = rope_scaling
|
||||
self._rope_scaling_adjustment()
|
||||
self._rope_scaling_validation()
|
||||
self.sliding_window = sliding_window
|
||||
|
||||
super().__init__(
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
pad_token_id=pad_token_id,
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
def _rope_scaling_adjustment(self):
|
||||
"""
|
||||
Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
|
||||
"""
|
||||
if self.rope_scaling is None:
|
||||
return
|
||||
|
||||
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||
|
||||
# For backward compatibility if previous version used "su" or "yarn"
|
||||
if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
|
||||
self.rope_scaling["type"] = "longrope"
|
||||
|
||||
def _rope_scaling_validation(self):
|
||||
"""
|
||||
Validate the `rope_scaling` configuration.
|
||||
"""
|
||||
if self.rope_scaling is None:
|
||||
return
|
||||
|
||||
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
|
||||
raise ValueError(
|
||||
"`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
|
||||
f"got {self.rope_scaling}"
|
||||
)
|
||||
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||
rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
|
||||
rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
|
||||
if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
|
||||
raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
|
||||
if not (
|
||||
isinstance(rope_scaling_short_factor, list)
|
||||
and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
|
||||
):
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
|
||||
)
|
||||
if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
|
||||
)
|
||||
if not (
|
||||
isinstance(rope_scaling_long_factor, list)
|
||||
and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
|
||||
):
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
|
||||
)
|
||||
if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
|
||||
)
|
||||
3
model-00001-of-00002.safetensors
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3
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|
||||
}
|
||||
}
|
||||
1563
modeling_phi3.py
Normal file
1563
modeling_phi3.py
Normal file
File diff suppressed because it is too large
Load Diff
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
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|
||||
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|
||||
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|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
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|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
93463
tokenizer.json
Normal file
93463
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
||||
size 499723
|
||||
131
tokenizer_config.json
Normal file
131
tokenizer_config.json
Normal file
@@ -0,0 +1,131 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_eos_token": false,
|
||||
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|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
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|
||||
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|
||||
"normalized": false,
|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
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|
||||
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|
||||
"rstrip": true,
|
||||
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|
||||
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|
||||
},
|
||||
"32000": {
|
||||
"content": "<|endoftext|>",
|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
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|
||||
"special": true
|
||||
},
|
||||
"32001": {
|
||||
"content": "<|assistant|>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32002": {
|
||||
"content": "<|placeholder1|>",
|
||||
"lstrip": false,
|
||||
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|
||||
"rstrip": true,
|
||||
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|
||||
"special": true
|
||||
},
|
||||
"32003": {
|
||||
"content": "<|placeholder2|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"32005": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"32006": {
|
||||
"content": "<|system|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"32007": {
|
||||
"content": "<|end|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"32009": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"32010": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
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|
||||
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|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user