Instructions to use Bhavika67/text2sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bhavika67/text2sql with Transformers:

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

pipe = pipeline("text-generation", model="Bhavika67/text2sql", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bhavika67/text2sql", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Bhavika67/text2sql", trust_remote_code=True)
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 Bhavika67/text2sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bhavika67/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": "Bhavika67/text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Bhavika67/text2sql

SGLang

How to use Bhavika67/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 "Bhavika67/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": "Bhavika67/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 "Bhavika67/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": "Bhavika67/text2sql",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Bhavika67/text2sql with Docker Model Runner:
```
docker model run hf.co/Bhavika67/text2sql
```

text2sql

File size: 861 Bytes

d096bd5
4cecb95
 
d096bd5
 
 
 
 
 
 
4cecb95
 
 
d096bd5
 
 
 
 
 
 
 
 
 
 
 
 
4cecb95
d096bd5
 
 
 
 
 
 
 
4cecb95

---
language:
- en
tags:
- text2sql
- causal-lm
- transformers
- safetensors
license: mit
pipeline_tag: text-generation
base_model:
- microsoft/Phi-3-mini-4k-instruct
library_name: transformers
---



# Phi-3 Text-to-SQL Model

This is a fine-tuned **Microsoft Phi-3** model specialized for **Text-to-SQL** generation.

## Example

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "bhavika67/text2sql"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype="auto", device_map="auto")

question = "List all customers who ordered products over $500 last month."
inputs = tokenizer(question, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)

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