File size: 3,195 Bytes
f6216d6 d3a6d76 635d222 9e609dc 012fa32 9e609dc 4716bd4 012fa32 4716bd4 1ac2713 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | ---
license: mit
datasets:
- gretelai/synthetic_text_to_sql
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
library_name: transformers
tags:
- text-to-sql
---
## Model Details
This model is a fine-tuned version of Llama-3.2-3B-Instruct designed specifically for Text-to-SQL tasks. It was trained to accept a database schema and a natural language question, and output a valid SQL query along with a brief explanation of the logic.
It is lightweight (3B parameters), making it suitable for local deployment on consumer GPUs using 4-bit quantization.
### Model Description
1) Base Model: unsloth/Llama-3.2-3B-Instruct
2) Fine-tuning Framework: Unsloth (QLoRA)
3) Dataset: gretelai/synthetic_text_to_sql
## Uses
The model was trained using the Alpaca prompt format. For best results, structure your input as follows:
## How to Get Started with the Model
```python
import torch
from transformers import pipeline
model_id = "Ary-007/Text-to-sql-llama-3.2"
# Load the pipeline
pipe = pipeline(
"text-generation",
model=model_id,
device_map="auto",
)
# Define the schema (Context)
schema = """
CREATE TABLE employees (
id INT,
name TEXT,
department TEXT,
salary INT,
hire_date DATE
);
"""
# Define the user question
question = "Find the name and salary of employees in the 'Engineering' department who earn more than 80000."
# Format the prompt exactly as trained
prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Company Database : {schema}
### Input:
SQL Prompt :{question}
### Response:
"""
outputs = pipe(
prompt,
max_new_tokens=200,
do_sample=True,
temperature=0.1,
top_p=0.9
)
print(outputs[0]["generated_text"])
```
## Training Details
The model was fine-tuned using Unsloth on a Tesla T4 GPU (Google Colab).
Hyperparameters
1) Rank (r): 16
2) LoRA Alpha: 16
3) Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
4) Quantization: 4-bit (Normal Float4)
5) Max Sequence Length: 2048
6) Learning Rate: 2e-4
7) Optim: adamw_8bit
8) Max Steps: 60
## Dataset Info
The model was trained on the gretelai/synthetic_text_to_sql dataset, utilizing the following fields:
1) sql_context: Used as the database schema context.
2) sql_prompt: The natural language question.
3) sql: The target SQL query.
4) sql_explanation: The explanation of the query logic.
## Limitations
1) Training Steps: This model was trained for a limited number of steps (60) as a proof of concept. It may not generalize well to extremely complex or unseen database schemas.
2) Hallucination: Like all LLMs, it may generate syntactically correct but logically incorrect SQL. Always validate the output before running it on a production database.
3) Scope: It is optimized for standard SQL (similar to SQLite/PostgreSQL) as presented in the GretelAI dataset.
## License
This model is derived from Llama-3.2 and is subject to the Llama 3.2 Community License. |