Instructions to use Sculptor-AI/Ursa_Minor_Smashed 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 Sculptor-AI/Ursa_Minor_Smashed 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 Sculptor-AI/Ursa_Minor_Smashed:F32 # Run inference directly in the terminal: llama cli -hf Sculptor-AI/Ursa_Minor_Smashed:F32
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Sculptor-AI/Ursa_Minor_Smashed:F32 # Run inference directly in the terminal: llama cli -hf Sculptor-AI/Ursa_Minor_Smashed:F32
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 Sculptor-AI/Ursa_Minor_Smashed:F32 # Run inference directly in the terminal: ./llama-cli -hf Sculptor-AI/Ursa_Minor_Smashed:F32
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 Sculptor-AI/Ursa_Minor_Smashed:F32 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Sculptor-AI/Ursa_Minor_Smashed:F32
Use Docker
docker model run hf.co/Sculptor-AI/Ursa_Minor_Smashed:F32
- LM Studio
- Jan
- Ollama
How to use Sculptor-AI/Ursa_Minor_Smashed with Ollama:
ollama run hf.co/Sculptor-AI/Ursa_Minor_Smashed:F32
- Unsloth Desktop
- Docker Model Runner
How to use Sculptor-AI/Ursa_Minor_Smashed with Docker Model Runner:
docker model run hf.co/Sculptor-AI/Ursa_Minor_Smashed:F32
- Lemonade
How to use Sculptor-AI/Ursa_Minor_Smashed with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Sculptor-AI/Ursa_Minor_Smashed:F32
Run and chat with the model
lemonade run user.Ursa_Minor_Smashed-F32
List all available models
lemonade list
- Atomic Chat
| #!/usr/bin/env python3 | |
| """ | |
| CPU-optimized basic usage examples for Ursa Minor Smashed model | |
| """ | |
| import sys | |
| import os | |
| sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) | |
| import torch | |
| from inference_cpu import generate_direct, load_model_direct | |
| def run_basic_examples(): | |
| """Run basic CPU-optimized usage examples""" | |
| print("๐ Ursa Minor Smashed - CPU Basic Usage Examples") | |
| print("=" * 60) | |
| # Load model once for all examples | |
| print("Loading model on CPU...") | |
| model = load_model_direct("model_optimized.pt") | |
| print("โ Model loaded!\n") | |
| examples = [ | |
| { | |
| "name": "Creative Writing", | |
| "prompt": "In a world where artificial intelligence has", | |
| "params": {"max_new_tokens": 80, "temperature": 0.9, "top_k": 30} # Lower tokens for CPU | |
| }, | |
| { | |
| "name": "Code Generation", | |
| "prompt": "def fibonacci(n):", | |
| "params": {"max_new_tokens": 60, "temperature": 0.4, "top_k": 25} | |
| }, | |
| { | |
| "name": "Explanation", | |
| "prompt": "Explain how neural networks work:", | |
| "params": {"max_new_tokens": 100, "temperature": 0.7, "top_k": 30} | |
| }, | |
| { | |
| "name": "Story Continuation", | |
| "prompt": "The spaceship landed on the mysterious planet, and the crew discovered", | |
| "params": {"max_new_tokens": 90, "temperature": 0.8, "top_k": 30} | |
| }, | |
| { | |
| "name": "Technical Writing", | |
| "prompt": "The benefits of using efficient algorithms include", | |
| "params": {"max_new_tokens": 70, "temperature": 0.6, "top_k": 25} | |
| } | |
| ] | |
| for i, example in enumerate(examples, 1): | |
| print(f"๐ Example {i}: {example['name']}") | |
| print(f"๐ญ Prompt: {example['prompt']}") | |
| print("๐ Generating...") | |
| try: | |
| result = generate_direct( | |
| model, | |
| example['prompt'], | |
| **example['params'] | |
| ) | |
| print("โจ Result:") | |
| print("-" * 40) | |
| print(result) | |
| print("-" * 40) | |
| print() | |
| except Exception as e: | |
| print(f"โ Error: {e}") | |
| print() | |
| def run_interactive_mode(): | |
| """Run interactive mode for testing different parameters""" | |
| print("\n๐ฎ Interactive Mode") | |
| print("=" * 30) | |
| # Load model | |
| print("Loading model on CPU...") | |
| model = load_model_direct("model_optimized.pt") | |
| print("โ Model loaded!") | |
| print("\nCommands:") | |
| print("- Enter a prompt to generate text") | |
| print("- Type 'params' to change generation parameters") | |
| print("- Type 'quit' to exit") | |
| print() | |
| # Default parameters optimized for CPU | |
| params = { | |
| "max_new_tokens": 80, # Lower for CPU efficiency | |
| "temperature": 0.8, | |
| "top_k": 30, # Lower for CPU efficiency | |
| "top_p": 0.9, | |
| "repetition_penalty": 1.1 | |
| } | |
| while True: | |
| user_input = input("๐ฏ Prompt (or command): ").strip() | |
| if user_input.lower() == 'quit': | |
| print("๐ Goodbye!") | |
| break | |
| elif user_input.lower() == 'params': | |
| print("\nCurrent parameters:") | |
| for key, value in params.items(): | |
| print(f" {key}: {value}") | |
| print("\nEnter new values (press Enter to keep current):") | |
| for key in params: | |
| new_value = input(f" {key} [{params[key]}]: ").strip() | |
| if new_value: | |
| try: | |
| if key == "max_new_tokens" or key == "top_k": | |
| params[key] = int(new_value) | |
| else: | |
| params[key] = float(new_value) | |
| except ValueError: | |
| print(f"Invalid value for {key}, keeping current value") | |
| print() | |
| continue | |
| elif user_input == "": | |
| continue | |
| # Generate text | |
| try: | |
| print("๐ Generating...") | |
| result = generate_direct(model, user_input, **params) | |
| print("โจ Result:") | |
| print("-" * 40) | |
| print(result) | |
| print("-" * 40) | |
| print() | |
| except Exception as e: | |
| print(f"โ Error: {e}") | |
| print() | |
| def main(): | |
| """Main function""" | |
| print("Choose mode:") | |
| print("1. Run basic examples") | |
| print("2. Interactive mode") | |
| try: | |
| choice = input("Enter choice (1 or 2): ").strip() | |
| if choice == "1": | |
| run_basic_examples() | |
| elif choice == "2": | |
| run_interactive_mode() | |
| else: | |
| print("Invalid choice. Running basic examples...") | |
| run_basic_examples() | |
| except KeyboardInterrupt: | |
| print("\n๐ Goodbye!") | |
| if __name__ == "__main__": | |
| main() |