Instructions to use Rj18/text-to-sql-tinyllama-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rj18/text-to-sql-tinyllama-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "Rj18/text-to-sql-tinyllama-lora")

Transformers

How to use Rj18/text-to-sql-tinyllama-lora with Transformers:

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

pipe = pipeline("text-generation", model="Rj18/text-to-sql-tinyllama-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Rj18/text-to-sql-tinyllama-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rj18/text-to-sql-tinyllama-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rj18/text-to-sql-tinyllama-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rj18/text-to-sql-tinyllama-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rj18/text-to-sql-tinyllama-lora

SGLang

How to use Rj18/text-to-sql-tinyllama-lora 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 "Rj18/text-to-sql-tinyllama-lora" \
    --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": "Rj18/text-to-sql-tinyllama-lora",
		"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 "Rj18/text-to-sql-tinyllama-lora" \
        --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": "Rj18/text-to-sql-tinyllama-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rj18/text-to-sql-tinyllama-lora with Docker Model Runner:
```
docker model run hf.co/Rj18/text-to-sql-tinyllama-lora
```

Text-to-SQL TinyLlama LoRA Adapter

A fine-tuned LoRA adapter that converts natural language questions into SQL queries. Built on top of TinyLlama-1.1B-Chat-v1.0 using Supervised Fine-Tuning (SFT) on the Spider benchmark dataset.

Model Details

Model Description

This is a LoRA (Low-Rank Adaptation) adapter fine-tuned to generate SQL queries from natural language questions. Only 0.10% of the base model's parameters were trained, making it extremely lightweight (4.5 MB) while still achieving strong results.

Developed by: Rj18
Model type: Causal Language Model (LoRA Adapter)
Language(s): English
License: MIT
Fine-tuned from: TinyLlama/TinyLlama-1.1B-Chat-v1.0

Model Sources

Repository: https://github.com/18-RAJAT/Interactive-Production-text2sql-Pipeline

How to Use

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

Load base model and tokenizer

base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" adapter = "Rj18/text-to-sql-tinyllama-lora"

tokenizer = AutoTokenizer.from_pretrained(adapter) model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype=torch.float16) model = PeftModel.from_pretrained(model, adapter) model.eval()

Generate SQL

question = "How many employees are in each department?" prompt = f"[INST] Generate SQL for the following question.\nQuestion: {question} [/INST]\n"

inputs = tokenizer(prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.1)

sql = tokenizer.decode(outputs[0], skip_special_tokens=True) print(sql)

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