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 / README.md

saadkhi

Update README.md

d983985 verified 7 months ago

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	---
	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))