Instructions to use ritam-05/qwen2.5-7b-sql-specialist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ritam-05/qwen2.5-7b-sql-specialist with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "ritam-05/qwen2.5-7b-sql-specialist")

Transformers

How to use ritam-05/qwen2.5-7b-sql-specialist with Transformers:

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

pipe = pipeline("text-generation", model="ritam-05/qwen2.5-7b-sql-specialist")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ritam-05/qwen2.5-7b-sql-specialist", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ritam-05/qwen2.5-7b-sql-specialist with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ritam-05/qwen2.5-7b-sql-specialist"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ritam-05/qwen2.5-7b-sql-specialist",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ritam-05/qwen2.5-7b-sql-specialist

SGLang

How to use ritam-05/qwen2.5-7b-sql-specialist 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 "ritam-05/qwen2.5-7b-sql-specialist" \
    --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": "ritam-05/qwen2.5-7b-sql-specialist",
		"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 "ritam-05/qwen2.5-7b-sql-specialist" \
        --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": "ritam-05/qwen2.5-7b-sql-specialist",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ritam-05/qwen2.5-7b-sql-specialist with Docker Model Runner:
```
docker model run hf.co/ritam-05/qwen2.5-7b-sql-specialist
```

Qwen2.5-7B SQL Specialist (QLoRA Adapter)

This is a fine-tuned LoRA adapter for Qwen/Qwen2.5-7B-Instruct, specifically trained to act as a Senior SQL Database Specialist. It accurately translates natural language questions into executable SQL queries based on provided database schemas.

Model Details

Base Model: Qwen/Qwen2.5-7B-Instruct
Fine-tuning Method: QLoRA (4-bit quantization) via PEFT
Task: Text-to-SQL generation
Language: English / SQL

Intended Use

This model is designed to take a database schema and a user question, and output only the executable SQL query enclosed in a markdown code block. It is instructed to avoid conversational filler.

Training Details

Hardware: Trained on an NVIDIA T4 GPU (Kaggle).
Hyperparameters:
- Batch size: 1 (with gradient accumulation steps = 16)
- Learning rate: 2e-4 (Cosine schedule)
- Mixed precision: fp16
- LoRA Rank (r): 16
- LoRA Alpha: 32

How to Get Started

To use this model, load the base model in 4-bit and attach this LoRA adapter:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

base_model_id = "Qwen/Qwen2.5-7B-Instruct"
adapter_id = "ritam-05/qwen2.5-7b-sql-specialist" 

# Load base model in 4-bit
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16
)
base_model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(adapter_id)

# Attach adapter
model = PeftModel.from_pretrained(base_model, adapter_id)

# Test prompt
schema = "CREATE TABLE employees (emp_id INT, name VARCHAR, department VARCHAR, salary INT);"
question = "What is the average salary in the Engineering department?"
messages = [
    {"role": "system", "content": "You are a Senior SQL Database Specialist. Output only the executable SQL query enclosed within a markdown code block."},
    {"role": "user", "content": f"Database Schema:\n{schema}\n\nQuestion:\n{question}"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.1)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

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