- Libraries
- llama-cpp-python
How to use wanshenl/pgsql-performance with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="wanshenl/pgsql-performance",
filename="unsloth.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 wanshenl/pgsql-performance with llama.cpp:
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wanshenl/pgsql-performance:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf wanshenl/pgsql-performance:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wanshenl/pgsql-performance:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf wanshenl/pgsql-performance: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 wanshenl/pgsql-performance:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf wanshenl/pgsql-performance: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 wanshenl/pgsql-performance:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wanshenl/pgsql-performance:Q4_K_M
Use Docker
docker model run hf.co/wanshenl/pgsql-performance:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use wanshenl/pgsql-performance with Ollama:
ollama run hf.co/wanshenl/pgsql-performance:Q4_K_M
- Unsloth Studio new
How to use wanshenl/pgsql-performance 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 wanshenl/pgsql-performance 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 wanshenl/pgsql-performance to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for wanshenl/pgsql-performance to start chatting
- Pi new
How to use wanshenl/pgsql-performance with Pi:
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf wanshenl/pgsql-performance:Q4_K_M
Configure the model in Pi
# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"llama-cpp": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "pgsql-performance"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- Docker Model Runner
How to use wanshenl/pgsql-performance with Docker Model Runner:
docker model run hf.co/wanshenl/pgsql-performance:Q4_K_M
- Lemonade
How to use wanshenl/pgsql-performance with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull wanshenl/pgsql-performance:Q4_K_M
Run and chat with the model
lemonade run user.pgsql-performance-Q4_K_M
List all available models
lemonade list
Pull the model
# Download Lemonade from https://lemonade-server.ai/