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Model: HridaAI/Hrida-T2SQL-3B-128k-V0.1
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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)
```

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"<|assistant|>": 32001,
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"<|user|>": 32010
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{
"architectures": [
"Phi3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_phi3.Phi3Config",
"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
},
"bos_token_id": 1,
"embd_pdrop": 0.0,
"eos_token_id": 32000,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 131072,
"model_type": "phi3",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"original_max_position_embeddings": 4096,
"pad_token_id": 32000,
"resid_pdrop": 0.0,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"long_factor": [
1.0700000524520874,
1.1200000047683716,
1.149999976158142,
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57.28738784790039,
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60.887386322021484,
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63.41739273071289,
63.8173942565918,
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63.897396087646484,
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64.06739807128906,
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64.12435150146484,
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64.19435119628906,
64.24435424804688,
64.57435607910156,
64.69000244140625,
64.76000213623047
],
"short_factor": [
1.1,
1.1,
1.1,
1.3000000000000003,
1.3500000000000003,
1.3500000000000003,
1.4000000000000004,
1.5500000000000005,
2.000000000000001,
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2.000000000000001,
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2.0500000000000007,
2.0500000000000007,
2.0500000000000007,
2.0500000000000007,
2.0500000000000007,
2.1000000000000005,
2.1000000000000005,
2.1500000000000004,
2.25,
2.25,
2.25,
2.25,
2.25,
2.3999999999999995,
2.4499999999999993,
2.499999999999999,
2.6999999999999984,
2.6999999999999984,
2.7499999999999982,
2.799999999999998,
2.8999999999999977,
3.049999999999997
],
"type": "longrope"
},
"rope_theta": 10000.0,
"sliding_window": 262144,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.40.2",
"use_cache": true,
"vocab_size": 32064
}

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# coding=utf-8
# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
#
# 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.
""" Phi-3 model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
"microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
}
class Phi3Config(PretrainedConfig):
r"""
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).
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
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`.
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)}"
)

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