Instructions to use vindows/qwen2.5-7b-text-to-sql-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vindows/qwen2.5-7b-text-to-sql-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="vindows/qwen2.5-7b-text-to-sql-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vindows/qwen2.5-7b-text-to-sql-merged")
model = AutoModelForCausalLM.from_pretrained("vindows/qwen2.5-7b-text-to-sql-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use vindows/qwen2.5-7b-text-to-sql-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vindows/qwen2.5-7b-text-to-sql-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vindows/qwen2.5-7b-text-to-sql-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vindows/qwen2.5-7b-text-to-sql-merged

SGLang

How to use vindows/qwen2.5-7b-text-to-sql-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "vindows/qwen2.5-7b-text-to-sql-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vindows/qwen2.5-7b-text-to-sql-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "vindows/qwen2.5-7b-text-to-sql-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vindows/qwen2.5-7b-text-to-sql-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vindows/qwen2.5-7b-text-to-sql-merged with Docker Model Runner:
```
docker model run hf.co/vindows/qwen2.5-7b-text-to-sql-merged
```

Qwen2.5-7B Merged Model for Text-to-SQL

This is a fully merged model (base + LoRA) ready for direct use. No need to load adapters separately!

Quick Links

🔧 LoRA Adapter Only: vindows/qwen2.5-7b-text-to-sql
🖥️ GGUF for CPU: vindows/qwen2.5-7b-text-to-sql-gguf

Performance

Spider Benchmark (200 examples)

Metric	Score
Exact Match	0.00%
Normalized Match	0.50%
Component Accuracy	92.60%
Average Similarity	25.47%

Training Metrics

Metric	Base	Fine-tuned	Improvement
Loss	2.1301	0.4098	80.76% ⬆️
Perplexity	8.4155	1.5064	82.10% ⬆️

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "vindows/qwen2.5-7b-text-to-sql-merged",
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "vindows/qwen2.5-7b-text-to-sql-merged",
    trust_remote_code=True
)

# Generate SQL from natural language
prompt = """Convert the following natural language question to SQL:

Database: concert_singer
Question: How many singers do we have?

SQL:"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.1, do_sample=False)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Extract SQL (remove prompt and extra text)
sql = result.split("SQL:")[-1].strip().split('\n\n')[0]
print(sql)