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

Libraries

How to use saadkhi/SQL_Chat_finetuned_model with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Phi-3-mini-4k-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "saadkhi/SQL_Chat_finetuned_model")

Transformers

How to use saadkhi/SQL_Chat_finetuned_model with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("saadkhi/SQL_Chat_finetuned_model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use saadkhi/SQL_Chat_finetuned_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/saadkhi/SQL_Chat_finetuned_model

SGLang

How to use saadkhi/SQL_Chat_finetuned_model 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 "saadkhi/SQL_Chat_finetuned_model" \
    --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": "saadkhi/SQL_Chat_finetuned_model",
		"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 "saadkhi/SQL_Chat_finetuned_model" \
        --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": "saadkhi/SQL_Chat_finetuned_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

SQL_Chat_finetuned_model

File size: 2,612 Bytes

---
language:
  - en
license: apache-2.0
library_name: peft
tags:
  - text-generation
  - sql
  - chat
  - peft
  - lora
  - transformers
  - phi-3
  - instruction-tuning
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
pipeline_tag: text-generation
inference: false
---

# 🧠 SQL Chat – Phi-3-mini SQL Assistant

**Model ID:** `saadkhi/SQL_Chat_finetuned_model`  
**Base model:** `unsloth/Phi-3-mini-4k-instruct-bnb-4bit`  
**Model type:** LoRA (merged)  
**Task:** Natural Language → SQL query generation + conversational SQL assistance  
**Language:** English  
**License:** Apache 2.0

This model is a fine-tuned version of **Phi-3-mini-4k-instruct** (4-bit quantized) specialized in understanding natural language questions about databases and generating correct, clean SQL queries.

## ✨ Key Features

- Very good balance between size, speed and SQL generation quality  
- Works well with common database dialects (PostgreSQL, MySQL, SQLite, SQL Server, etc.)  
- Can explain queries, suggest improvements and handle follow-up questions  
- Fast inference even on consumer hardware (especially with 4-bit quantization)

## 🎯 Intended Use & Capabilities

**Best for:**
- Converting natural language questions → SQL queries  
- Helping beginners learn SQL through explanations  
- Quick prototyping of SQL queries in development  
- Building SQL chat interfaces / tools / assistants  
- Educational purposes

**Limitations / Not recommended for:**
- Extremely complex analytical/business intelligence queries  
- Real-time query optimization advice  
- Very database-specific or proprietary SQL extensions  
- Production systems without human review (always validate generated SQL!)

## 🛠️ Quick Start (merged LoRA version)

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "saadkhi/SQL_Chat_finetuned_model"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Simple prompt style (chat template is recommended)
prompt = """Show all customers who placed more than 5 orders in 2024"""

messages = [{"role": "user", "content": prompt}]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=180,
    do_sample=False,
    temperature=0.0,
    pad_token_id=tokenizer.eos_token_id
)

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