Instructions to use RahulPi/qwen2.5-1.5B-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RahulPi/qwen2.5-1.5B-sql with Transformers:

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

pipe = pipeline("text-generation", model="RahulPi/qwen2.5-1.5B-sql")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RahulPi/qwen2.5-1.5B-sql")
model = AutoModelForCausalLM.from_pretrained("RahulPi/qwen2.5-1.5B-sql", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RahulPi/qwen2.5-1.5B-sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RahulPi/qwen2.5-1.5B-sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RahulPi/qwen2.5-1.5B-sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RahulPi/qwen2.5-1.5B-sql

SGLang

How to use RahulPi/qwen2.5-1.5B-sql 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 "RahulPi/qwen2.5-1.5B-sql" \
    --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": "RahulPi/qwen2.5-1.5B-sql",
		"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 "RahulPi/qwen2.5-1.5B-sql" \
        --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": "RahulPi/qwen2.5-1.5B-sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RahulPi/qwen2.5-1.5B-sql with Docker Model Runner:
```
docker model run hf.co/RahulPi/qwen2.5-1.5B-sql
```

Qwen2.5-1.5B SQL Assistant

A fine-tuned text-to-SQL model designed to translate natural language questions into valid SQL queries given a database context and table schema.

Model Details

Model Description

This model is a fine-tuned, merged version of Qwen/Qwen2.5-1.5B-Instruct. It was trained using QLoRA with Hugging Face's SFTTrainer on a structured SQL context dataset and subsequently merged back into full 16-bit precision (fp16) for direct inference.

Developed by: RahulPi
Model type: Causal Language Model
Language(s): English
License: Apache 2.0
Finetuned from model: Qwen/Qwen2.5-1.5B-Instruct

Uses

Direct Use

Generating SQL queries based on database context and user prompts.

Prompt Format

The model was trained using the following template format:

System: You are a strict SQL assistant. Output ONLY valid SQL queries.
User: Schema: <context/schema> Question: <natural_language_question>
Assistant: <sql_query>

How to Get Started with the Model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "RahulPi/qwen2.5-1.5B-sql"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

context = "CREATE TABLE head (born_state VARCHAR, age INTEGER)"
question = "How many heads were born in California and are older than 50?"

prompt = (
    f"System: You are a strict SQL assistant. Output ONLY valid SQL queries.\n"
    f"User: Schema: {context} Question: {question}\n"
    f"Assistant:"
)

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

Training Details

Training Data

Trained on a 1,000-row subset of b-mc2/sql-create-context, combining table schemas and questions mapped directly to correct SQL target queries.

Training Procedure

Framework: TRL SFTTrainer with PEFT (QLoRA)
Base Precision: Loaded in 4-bit (NormalFloat4) during training, merged into fp16 for distribution
LoRA Target Modules: q_proj, k_proj, v_proj, o_proj
LoRA Parameters: r = 16, alpha = 32, Dropout = 0.05

Training Hyperparameters

Learning Rate: 2e-4
Scheduler: Cosine
Batch Size: 4 per device
Gradient Accumulation Steps: 4 (Effective batch size: 16)
Optimizer: paged_adamw_32bit
Max Sequence Length: 2048
Epochs: 3 (189 global steps)

Results

Final Training Loss: 0.7238
Mean Token Accuracy: 85.97%
Training Runtime: ~~1707 seconds (~~28.5 minutes on NVIDIA T4)

Downloads last month: 211

Safetensors

Model size

2B params

Tensor type

F16

Model tree for RahulPi/qwen2.5-1.5B-sql

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct