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	---
	license: apache-2.0
	language:
	- en
	base_model:
	- deepseek-ai/DeepSeek-R1
	new_version: imsanjoykb/deepSQL-R1-distill-8B
	pipeline_tag: text-generation
	library_name: adapter-transformers
	library_name2: transformers
	tags:
	- unsloth,
	- pytorch,
	- deepseek-R1,
	- inference-endpoint,
	- sql-code-generation,
	metrics:
	- accuracy
	- bleu
	---
	<div align="center">
	<img src="https://raw.githubusercontent.com/imsanjoykb/deepSQL-R1-distill-8B/refs/heads/master/assets/logomain.png" alt="Repo banner">
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	<div align="center" style="line-height: 1;">
	<a href="https://huggingface.co/imsanjoykb/deepSQL-R1-distill-8B" target="_blank" style="margin: 2px;">
	<img alt="Hugging Face Model" src="https://img.shields.io/badge/HuggingFace-Model-FF6F00?style=for-the-badge&logo=huggingface&logoColor=white" style="display: inline-block; vertical-align: middle;">
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	<a href="https://www.kaggle.com/code/imsanjoykb/inference-deepsql-r1-distill-8b" target="_blank" style="margin: 2px;">
	<img alt="Kaggle Notebook" src="https://img.shields.io/badge/Kaggle-Notebook-20BEFF?style=for-the-badge&logo=kaggle&logoColor=white" style="display: inline-block; vertical-align: middle;">
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	<img alt="Gradio-Colab" src="https://img.shields.io/badge/Gradio-Colab-0084FF?style=for-the-badge&logo=gradio&labelColor=F9AB00" style="display: inline-block; vertical-align: middle;">
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	<a href="https://doi.org/10.6084/m9.figshare.28330301.v1" target="_blank" style="margin: 2px;">
	<img alt="Figshare" src="https://img.shields.io/badge/Figshare-DOI-0085CA?style=for-the-badge&logo=figshare&logoColor=white" style="display: inline-block; vertical-align: middle;">
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	<p align="center">
	<a href="https://doi.org/10.6084/m9.figshare.28330301.v1"><b>Paper Link</b>👁️</a>
	</p>

	## Abstract
	State-of-the-art advances in LLMs have pushed NLP to its limits, where even complex tasks, such as code generation, can be automated. This paper describes the deepSQL-R1-distill-8B, a fine-tuned and quantized model variant of the DeepSeek-R1 model architecture and specifically optimized for text-to-SQL conversion. Fine-tuning was performed using Unsloth, one of the most efficient frameworks for fine-tuning LLMs, in combination with Parameter-Efficient Fine-Tuning and the SFTTrainer framework. This allows domain-specific adaptation with minimal resource consumption. The approach fine-tunes curated datasets by LoRA, ensuring a more parameter-efficient and lower-memory-consuming model. Besides this, we investigate reinforcement learning techniques to further enhance the model's ability in generating accurate and contextually appropriate SQL queries. Combination of 8-bit quantization, LoRA, Unsloth, and reinforcement learning places deepSQL-R1-distill-8B as one of the cutting-edge solutions for automatic SQL code generation in real-world applications. Addressing major challenges in computational efficiency, domain-specific adaptation, and reinforcement-based refinement, this model is leading the way toward a more intuitive and resource-effective way of interacting with relational databases.

	## Model Download
	\| Model \| #Total Params \| #Active Params \| Context Length \| Download \|
	\| :-----------------------------: \| :---------------: \| :----------------: \| :----------------: \| :----------------------------------------------------------: \|
	\| deepSQL-R1-distill-8B \| 8B \| 6B \| 128k \| [🤗 HuggingFace](https://huggingface.co/imsanjoykb/deepSQL-R1-distill-8B) \|

	## Benchmarking
	## 📊 SQL Model Benchmarking - Comprehensive Evaluation

	\| Rank \| LLM Name \| SqlEval-Classic (%) \| Execution Accuracy (%) \| Query Optimization (%) \| Latency (ms) \|
	\|------\|----------------------------\|---------------------\|-----------------------\|-----------------------\|--------------\|
	\| 1️⃣ \| GPT-4o \| 86 \| 91 \| 88 \| 120 \|
	\| 2️⃣ \| deepSQL-R1-distill-8B \| 82 \| 89 \| 85 \| 110 \|
	\| 3️⃣ \| deepseek-R1 \| 78 \| 84 \| 86 \| 150 \|
	\| 4️⃣ \| Claude-3-Sonnet \| 72 \| 8o \| 80 \| 130 \|
	\| 5️⃣ \| llama3.2 \| 68 \| 72 \| 76 \| 170 \|
	\| 6️⃣ \| Mistral-7B \| 62 \| 76 \| 69 \| 190 \|

	🚀 Key Insights:
	- GPT-4o leads in overall performance, achieving 91% execution accuracy with low latency (120ms).
	- deepSQL-R1-distill-8B excels in query execution & optimization, making it a strong competitor.
	- Mistral-7B has the lowest scores but may improve with fine-tuning.

	🔹 New Metrics Explained:
	- Execution Accuracy (%) → Measures correctness of SQL execution.
	- Query Optimization (%) → Evaluates efficiency in structuring optimized queries.
	- Latency (ms) → Measures response time (lower is better).

	![alt text](https://raw.githubusercontent.com/imsanjoykb/deepSQL-R1-distill-8B/refs/heads/master/results/evals.png "evals")

	## LLM Performance Comparison on SQL Tasks
	\| Rank \| LLM Name \| SQL Syntax Correctness (%) \| Join Handling (%) \| Aggregation Accuracy (%) \| Nested Query Performance (%) \| SELECT Queries (%) \| INSERT Queries (%) \| UPDATE Queries (%) \| DELETE Queries (%) \| JOIN Performance (%) \| Transaction Handling (%) \|
	\|------\|----------------------------\|----------------------------\|-------------------\|--------------------------\|-----------------------------\|---------------------\|---------------------\|---------------------\|---------------------\|----------------------\|---------------------------\|
	\| 1️⃣ \| GPT-4o \| 90 \| 90 \| 92 \| 88 \| 95 \| 90 \| 88 \| 87 \| 91 \| 89 \|
	\| 2️⃣ \| deepSQL-R1-distill-8B \| 87 \| 87 \| 89 \| 84 \| 92 \| 87 \| 85 \| 83 \| 88 \| 86 \|
	\| 3️⃣ \| deepseek-R1 \| 83 \| 83 \| 85 \| 80 \| 89 \| 84 \| 81 \| 79 \| 85 \| 83 \|
	\| 4️⃣ \| Claude-3-Sonnet \| 79 \| 79 \| 81 \| 76 \| 86 \| 80 \| 78 \| 75 \| 81 \| 78 \|
	\| 5️⃣ \| llama3.2 \| 75 \| 75 \| 77 \| 72 \| 82 \| 76 \| 74 \| 71 \| 77 \| 74 \|
	\| 6️⃣ \| Mistral-7B \| 70 \| 70 \| 72 \| 68 \| 78 \| 72 \| 70 \| 68 \| 72 \| 70 \|

	## Inference

	Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.

	```python
	# Import necessary libraries
	from unsloth import FastLanguageModel
	import torch

	# Define the model name and other parameters
	model_name = "imsanjoykb/deepSQL-R1-distill-8B"
	max_seq_length = 2048
	dtype = None
	load_in_4bit = True

	# Load the model and tokenizer from Hugging Face
	model, tokenizer = FastLanguageModel.from_pretrained(
	model_name=model_name,
	max_seq_length=max_seq_length,
	dtype=dtype,
	load_in_4bit=load_in_4bit,
	)

	# Enable faster inference
	FastLanguageModel.for_inference(model)

	# Define the prompt template
	odoo_text2sql_prompt = """Below is an instruction describing a task related to generating a SQL query specifically for Odoo's database structure. The input provides relevant context about Odoo models or data fields from {db_schema}. Write a SQL query that fulfills the given task using Odoo's database schema.

	### Instruction:
	Generate a SQL query in the context of Odoo to {}

	### Input:
	{}

	### Response:
	{}
	"""
	```

	```python
	# Optionally, use a TextStreamer for continuous inference
	from transformers import TextStreamer

	db_schema = """
	CREATE TABLE product_product (
	id SERIAL NOT NULL,
	message_main_attachment_id INTEGER,
	product_tmpl_id INTEGER NOT NULL,
	create_uid INTEGER,
	write_uid INTEGER,
	default_code VARCHAR,
	barcode VARCHAR,
	combination_indices VARCHAR,
	volume NUMERIC,
	weight NUMERIC,
	active BOOLEAN,
	can_image_variant_1024_be_zoomed BOOLEAN,
	create_date TIMESTAMP WITHOUT TIME ZONE,
	write_date TIMESTAMP WITHOUT TIME ZONE,
	store_qty_available DOUBLE PRECISION,
	store_standard_price DOUBLE PRECISION,
	store_sales_count DOUBLE PRECISION,
	CONSTRAINT product_product_pkey PRIMARY KEY (id),
	CONSTRAINT product_product_create_uid_fkey FOREIGN KEY(create_uid) REFERENCES res_users (id) ON DELETE SET NULL,
	CONSTRAINT product_product_message_main_attachment_id_fkey FOREIGN KEY(message_main_attachment_id) REFERENCES ir_attachment (id) ON DELETE SET NUL"L,
	CONSTRAINT product_product_product_tmpl_id_fkey FOREIGN KEY(product_tmpl_id) REFERENCES product_template (id) ON DELETE CASCADE,
	CONSTRAINT product_product_write_uid_fkey FOREIGN KEY(write_uid) REFERENCES res_users (id) ON DELETE SET NULL
	)
	"""
	# Prepare the input text for continuous inference
	instruction = ""
	input_text = "What are the top sales products?"
	output_text = ""

	# Define the `odoo_text2sql_prompt` with placeholders
	odoo_text2sql_prompt = """
	Instruction: {instruction}
	Input: {input_text}
	Output: {output_text}
	DB Schema: {db_schema}
	"""

	# Tokenize the input text
	inputs = tokenizer(
	[
	odoo_text2sql_prompt.format(
	instruction=instruction,
	input_text=input_text,
	output_text=output_text,
	db_schema=db_schema
	)
	],
	return_tensors="pt"
	).to("cuda")

	# Initialize the TextStreamer
	text_streamer = TextStreamer(tokenizer)

	# Generate the output using the model with TextStreamer
	_ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=350)
	```

	## Citing
	```
	@misc{,
	author = {Sanjoy Kumar},
	title = {DeepSQL-R1: A Quantized LLM for High-Performance and Reinforcement Driven NL2SQL Generation},
	year = {2025},
	Paper = {https://doi.org/10.6084/m9.figshare.28330301.v1},
	Model Link = {https://huggingface.co/imsanjoykb/deepSQL-R1-distill-8B},
	}
	```

	## Author
	<div align="center" style="line-height: 1;">
	<a href="mailto:[email protected]" target="_blank" style="margin: 2px;">
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	## Usages Services
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