Instructions to use Bhuvandesai/phi3-text-to-sql-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bhuvandesai/phi3-text-to-sql-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Bhuvandesai/phi3-text-to-sql-gguf",
	filename="phi3-text-to-sql-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Bhuvandesai/phi3-text-to-sql-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M

Use Docker

docker model run hf.co/Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use Bhuvandesai/phi3-text-to-sql-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bhuvandesai/phi3-text-to-sql-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bhuvandesai/phi3-text-to-sql-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M

Ollama
How to use Bhuvandesai/phi3-text-to-sql-gguf with Ollama:
```
ollama run hf.co/Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M
```

Unsloth Studio

How to use Bhuvandesai/phi3-text-to-sql-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Bhuvandesai/phi3-text-to-sql-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Bhuvandesai/phi3-text-to-sql-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Bhuvandesai/phi3-text-to-sql-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use Bhuvandesai/phi3-text-to-sql-gguf with Docker Model Runner:
```
docker model run hf.co/Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M
```

Lemonade

How to use Bhuvandesai/phi3-text-to-sql-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Bhuvandesai/phi3-text-to-sql-gguf:Q4_K_M

Run and chat with the model

lemonade run user.phi3-text-to-sql-gguf-Q4_K_M

List all available models

lemonade list

Phi-3-mini Text-to-SQL — GGUF (quantized for CPU)

Quantized GGUF builds of the fine-tuned Phi-3-mini Text-to-SQL model (LoRA already merged into the base weights), for fast CPU inference with llama.cpp.

File	Size	Effective bits/weight	vs f16
`phi3-text-to-sql-Q4_K_M.gguf` ⭐ recommended	2.40 GB	5.01	−68.6% (3.2× smaller)
`phi3-text-to-sql-Q5_K_M.gguf`	2.76 GB	5.76	−64.0% (2.8× smaller)

Note: "Q4" K-quants average ~5 effective bits/weight (embeddings and some tensors stay higher-precision), so the file is larger than a literal 4-bit×params calculation.

Which one?

Use Q4_K_M. On this task it matched Q5_K_M on quality while being smaller and faster.

Benchmarks (measured)

CPU = Intel i7-13650HX, 14 threads, llama-bench, build 9637:

Model	Prompt processing (pp256)	Token generation (tg64)
Q4_K_M	91.4 tok/s	20.1 tok/s
Q5_K_M	59.6 tok/s	18.5 tok/s

Task quality (12 held-out questions, execution-match against a live SQLite DB):

Model	Execution-match	Valid SQL
Q4_K_M	75.0%	100%
Q5_K_M	75.0%	100%

4-bit quantization cost no measurable task accuracy vs 5-bit here.

Run it

# CLI
llama-cli -m phi3-text-to-sql-Q4_K_M.gguf -p "<|user|>\n<schema + question><|end|>\n<|assistant|>\n" -n 150 --temp 0

# Server (OpenAI-compatible)
llama-server -m phi3-text-to-sql-Q4_K_M.gguf -c 2048 -t 14 --port 8080

The model expects Phi-3 chat formatting; include the database schema in the user turn (see the adapter card for the exact prompt). It outputs raw SQLite.

License: MIT.

Downloads last month: 289

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

4-bit

5-bit

Model tree for Bhuvandesai/phi3-text-to-sql-gguf

Base model

microsoft/Phi-3-mini-4k-instruct

Adapter

Bhuvandesai/phi3-text-to-sql-adapter

Quantized

(1)

this model

Bhuvandesai
/

phi3-text-to-sql-gguf

Phi-3-mini Text-to-SQL — GGUF (quantized for CPU)

Which one?

Benchmarks (measured)

Run it

Model tree for Bhuvandesai/phi3-text-to-sql-gguf

Space using Bhuvandesai/phi3-text-to-sql-gguf 1