Instructions to use mrcmilo/phi3-text2sql-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrcmilo/phi3-text2sql-lora with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mrcmilo/phi3-text2sql-lora",
	filename="phi-3-mini-4k-instruct.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mrcmilo/phi3-text2sql-lora with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mrcmilo/phi3-text2sql-lora:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mrcmilo/phi3-text2sql-lora:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mrcmilo/phi3-text2sql-lora:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mrcmilo/phi3-text2sql-lora: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 mrcmilo/phi3-text2sql-lora:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mrcmilo/phi3-text2sql-lora: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 mrcmilo/phi3-text2sql-lora:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mrcmilo/phi3-text2sql-lora:Q4_K_M

Use Docker

docker model run hf.co/mrcmilo/phi3-text2sql-lora:Q4_K_M

LM Studio
Jan
Ollama
How to use mrcmilo/phi3-text2sql-lora with Ollama:
```
ollama run hf.co/mrcmilo/phi3-text2sql-lora:Q4_K_M
```

Unsloth Studio new

How to use mrcmilo/phi3-text2sql-lora 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 mrcmilo/phi3-text2sql-lora 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 mrcmilo/phi3-text2sql-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mrcmilo/phi3-text2sql-lora to start chatting

Docker Model Runner
How to use mrcmilo/phi3-text2sql-lora with Docker Model Runner:
```
docker model run hf.co/mrcmilo/phi3-text2sql-lora:Q4_K_M
```

Lemonade

How to use mrcmilo/phi3-text2sql-lora with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mrcmilo/phi3-text2sql-lora:Q4_K_M

Run and chat with the model

lemonade run user.phi3-text2sql-lora-Q4_K_M

List all available models

lemonade list

mrcmilo commited on Jan 2

Commit

06259cd

verified ·

1 Parent(s): 30e743b

Update README.md

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README.md +2 -2

README.md CHANGED Viewed

@@ -24,7 +24,7 @@ This is a specialized **Text-to-SQL** model fine-tuned from the **Microsoft Phi-
 ## 🚀 Key Features
 - **Architecture:** Phi-3-mini (3.8B parameters)
 - **Quantization:** Q4_K_M GGUF (Optimized balance of speed and logic)
-- **Fast Training:** Fine-tuned using [Unsloth](https://github.com/unslothai/unsloth).
 - **Format:** GGUF (Ready for Ollama, LM Studio, and llama.cpp)
 ## 🛠 Usage Instructions
@@ -35,7 +35,7 @@ To deploy locally:
 1. Download the `.gguf` file.
 2. Create a file named `Modelfile` and paste the following:
 ```dockerfile
-FROM ./phi-3-mini-4k-instruct.Q4_K_M.gguf
 TEMPLATE """<|system|>
 You are a helpful assistant that writes SQL queries. Given a user question and a table schema, output only the SQL code.<|end|>

 ## 🚀 Key Features
 - **Architecture:** Phi-3-mini (3.8B parameters)
 - **Quantization:** Q4_K_M GGUF (Optimized balance of speed and logic)
+- **Training Technique:** Fine-tuned using Lora with [Unsloth](https://github.com/unslothai/unsloth).
 - **Format:** GGUF (Ready for Ollama, LM Studio, and llama.cpp)
 ## 🛠 Usage Instructions
 1. Download the `.gguf` file.
 2. Create a file named `Modelfile` and paste the following:
 ```dockerfile
+FROM ./phi3-sql-expert.Q4_K_M.gguf
 TEMPLATE """<|system|>
 You are a helpful assistant that writes SQL queries. Given a user question and a table schema, output only the SQL code.<|end|>