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

66d2627

verified ·

1 Parent(s): 06259cd

Update README.md

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README.md +30 -4

README.md CHANGED Viewed

@@ -21,15 +21,15 @@ pipeline_tag: text-generation
 This is a specialized **Text-to-SQL** model fine-tuned from the **Microsoft Phi-3-mini-4k-instruct** architecture. It has been optimized using **Unsloth** to provide high-accuracy SQL generation while remaining lightweight enough to run on consumer hardware.
-## 🚀 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. Ollama (Recommended)
 To deploy locally:
 1. Download the `.gguf` file.
@@ -46,4 +46,30 @@ You are a helpful assistant that writes SQL queries. Given a user question and a
 PARAMETER stop "<|end|>"
 PARAMETER temperature 0.1
-PARAMETER num_ctx 2048

 This is a specialized **Text-to-SQL** model fine-tuned from the **Microsoft Phi-3-mini-4k-instruct** architecture. It has been optimized using **Unsloth** to provide high-accuracy SQL generation while remaining lightweight enough to run on consumer hardware.
+## 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
+### Ollama (Recommended)
 To deploy locally:
 1. Download the `.gguf` file.
 PARAMETER stop "<|end|>"
 PARAMETER temperature 0.1
+PARAMETER num_ctx 2048
+```
+3. Run ollama create sql-expert -f Modelfile
+4. Run ollama run sql-expert
+## Evaluation Data
+The model was fine-tuned on the sql-create-context dataset, focusing on:
+Mapping natural language to complex SELECT, WHERE, and JOIN statements.
+Understanding table schemas provided in the prompt.
+Maintaining strict SQL syntax.
+## Recommended Settings
+Temperature: 0.0 or 0.1 (SQL requires deterministic output).
+Stop Tokens: Ensure <|end|> is set as a stop sequence to prevent "infinite looping" generation.
+Context Window: 2048 or 4096 tokens.
+Model Developer: mrcmilo
+Base Model: Phi-3-mini-4k-instruct