Property	Value
Base Model	Qwen/Qwen3.5-9B (9B params, hybrid DeltaNet+Attention)
Fine-tuning Method	QLoRA (4-bit NF4 + LoRA rank=16, alpha=32)
Training Data	Custom synthetic (81 examples, 8 domains) + b-mc2/sql-create-context (3,000 examples)
Training Hardware	Nvidia L4 GPU (24GB VRAM) via Colab / Modal
Epochs	3
Effective Batch Size	16
Precision	BFloat16

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "yesabhishek/qwen35-text2sql", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("yesabhishek/qwen35-text2sql")

prompt = """<|im_start|>user
Given the following SQL schema:
CREATE TABLE employees (id INT, name VARCHAR, department VARCHAR, salary DECIMAL)

Generate a SQL query for: What is the average salary by department?<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Domains Covered

Domain	Examples	Key SQL Features
E-Commerce	15	JOINs, aggregations, window functions
HR	12	Subqueries, HAVING, date arithmetic
Healthcare	10	GROUP BY, CASE WHEN, completion rates
Education	10	Capacity calculations, grade distributions
Finance	10	EXISTS, defaults, transaction analysis
Logistics	8	Inventory management, delivery metrics
Social Media	8	Engagement metrics, trending analysis
Real Estate	8	Price analytics, market time calculations

Limitations

Optimized for PostgreSQL syntax; may need adaptation for MySQL/SQLite
Best with schemas structurally similar to training domains
May struggle with very complex queries involving 5+ JOINs or recursive CTEs
Not tested on non-English natural language inputs
Thinking mode (<think> tags) is disabled in this fine-tune for clean SQL output