Instructions to use iamkushagratomar/Qwen2.5-0.5B-text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iamkushagratomar/Qwen2.5-0.5B-text2sql with Transformers:

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

pipe = pipeline("text-generation", model="iamkushagratomar/Qwen2.5-0.5B-text2sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iamkushagratomar/Qwen2.5-0.5B-text2sql")
model = AutoModelForCausalLM.from_pretrained("iamkushagratomar/Qwen2.5-0.5B-text2sql", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use iamkushagratomar/Qwen2.5-0.5B-text2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iamkushagratomar/Qwen2.5-0.5B-text2sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iamkushagratomar/Qwen2.5-0.5B-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/iamkushagratomar/Qwen2.5-0.5B-text2sql

SGLang

How to use iamkushagratomar/Qwen2.5-0.5B-text2sql 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 "iamkushagratomar/Qwen2.5-0.5B-text2sql" \
    --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": "iamkushagratomar/Qwen2.5-0.5B-text2sql",
		"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 "iamkushagratomar/Qwen2.5-0.5B-text2sql" \
        --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": "iamkushagratomar/Qwen2.5-0.5B-text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use iamkushagratomar/Qwen2.5-0.5B-text2sql with Docker Model Runner:
```
docker model run hf.co/iamkushagratomar/Qwen2.5-0.5B-text2sql
```

Qwen2.5-0.5B Text-to-SQL Model

This model is a fine-tuned version of Qwen2.5-0.5B for the text-to-SQL task on the b-mc2/sql-create-context dataset. It converts natural language questions + database schemas into SQL queries.

🔧 How to Use

Install dependencies:
```
pip install transformers torch
```

Load the model:

 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
 
 model_name = "iamkushagratomar/Qwen2.5-0.5B-text2sql"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")

Use the model (with your question + schema)::

def generate_sql(question, schema):
  # Format input as: "System: [message]\n\nUser: [question + schema]"
  system_message = "You are a SQL expert. Convert natural language questions into SQL queries."
  user_message = f"### Question: \n{question}\n\n### Schema: \n{schema}"
  
  full_input = f"System: {system_message}\n\nUser: {user_message}"
  
  inputs = tokenizer(full_input, return_tensors="pt").to(model.device)
  outputs = model.generate(
      input_ids=inputs.input_ids,
      max_new_tokens=100,
      temperature=0.2,
      do_sample=True,
      use_cache=True
  )
  return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Example usage
question = "Find the total revenue from orders placed in the last 30 days."
schema = "CREATE TABLE orders (order_id INT, order_date DATE, order_total DECIMAL, customer_id INT);"
sql_query = generate_sql(question, schema)
print(sql_query)