Instructions to use mrcmilo/phi3-text2sql-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mrcmilo/phi3-text2sql-lora 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 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 serve -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
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
- Atomic Chat
| tags: | |
| - gguf | |
| - llama.cpp | |
| - unsloth | |
| license: mit | |
| datasets: | |
| - b-mc2/sql-create-context | |
| language: | |
| - en | |
| metrics: | |
| - accuracy | |
| base_model: | |
| - microsoft/Phi-3-mini-4k-instruct | |
| 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 & Q5_K_M | |
| - **Training Technique:** Fine-tuned using Lora with [Unsloth](https://github.com/unslothai/unsloth). | |
| - **Format:** GGUF (Ready for Ollama, LM Studio, and llama.cpp) | |
| - **phi-3-mini-4k-instruct.Q4_K_M.gguf** | |
| - **phi-3-mini-4k-instruct.Q5_K.gguf** | |
| ## Usage Instructions | |
| ### Ollama (Recommended) | |
| To deploy locally: | |
| 1. Download the `.gguf` file (Q4 or Q5). | |
| 2. Create the Modelfile with the following instructions | |
| ```Dockerfile | |
| FROM ./phi-3-mini-4k-instruct.Q4_K_M.gguf | |
| TEMPLATE """<s><|user|> | |
| Schema: {{ .System }} | |
| Question: {{ .Prompt }}<|end|> | |
| <|assistant|> | |
| """ | |
| # Parameters for SQL stability | |
| PARAMETER stop "<|end|>" | |
| PARAMETER stop "<s>" | |
| PARAMETER stop "</s>" | |
| PARAMETER temperature 0.0 | |
| ``` | |
| 3. Run ```ollama create phi3-sql-expert -f Modelfile``` | |
| 5. Run ```ollama run phi3-sql-expert "schema: CREATE TABLE table_name_7 (nba_draft VARCHAR, school VARCHAR) question: What was the NBA draft status for Northeast High School?"``` | |
| 6. The answer should be ```SELECT nba_draft FROM table_name_7 WHERE school = "Northeast"``` | |
| ## Evaluation Data | |
| The model was fine-tuned on the [sql-create-context dataset](https://huggingface.co/datasets/b-mc2/sql-create-context), focusing on: | |
| - Mapping natural language to SQL queries with SELECT, WHERE, and JOIN statements. | |
| - Understanding table schemas provided in the prompt. | |
| - Maintaining strict SQL syntax in the response. | |
| ## 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 tokens. | |
| **Model Developer**: [msquared](https://github.com/mrcmilano) | |
| Base Model: [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) |