Text Generation
GGUF
English
llama.cpp
bitnet
ternary
1.58-bit
quantized
q4_k_m
edge
efficient-inference
cpu
tool-calling
Instructions to use Qapdex/SLM750-Edge-1.58-bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Qapdex/SLM750-Edge-1.58-bit with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Qapdex/SLM750-Edge-1.58-bit", filename="quantized_q4km.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Qapdex/SLM750-Edge-1.58-bit 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 Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT # Run inference directly in the terminal: llama cli -hf Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT # Run inference directly in the terminal: llama cli -hf Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
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 Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT # Run inference directly in the terminal: ./llama-cli -hf Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
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 Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT # Run inference directly in the terminal: ./build/bin/llama-cli -hf Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
Use Docker
docker model run hf.co/Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
- LM Studio
- Jan
- vLLM
How to use Qapdex/SLM750-Edge-1.58-bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qapdex/SLM750-Edge-1.58-bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qapdex/SLM750-Edge-1.58-bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
- Ollama
How to use Qapdex/SLM750-Edge-1.58-bit with Ollama:
ollama run hf.co/Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
- Unsloth Studio
How to use Qapdex/SLM750-Edge-1.58-bit 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 Qapdex/SLM750-Edge-1.58-bit 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 Qapdex/SLM750-Edge-1.58-bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Qapdex/SLM750-Edge-1.58-bit to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Qapdex/SLM750-Edge-1.58-bit with Docker Model Runner:
docker model run hf.co/Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
- Lemonade
How to use Qapdex/SLM750-Edge-1.58-bit with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
Run and chat with the model
lemonade run user.SLM750-Edge-1.58-bit-Q4_K_M_QUANT
List all available models
lemonade list
| import os | |
| import sys | |
| import signal | |
| import platform | |
| import argparse | |
| import subprocess | |
| def run_command(command, shell=False): | |
| """Run a system command and ensure it succeeds.""" | |
| try: | |
| subprocess.run(command, shell=shell, check=True) | |
| except subprocess.CalledProcessError as e: | |
| print(f"Error occurred while running command: {e}") | |
| sys.exit(1) | |
| def run_inference(): | |
| build_dir = "build" | |
| if platform.system() == "Windows": | |
| main_path = os.path.join(build_dir, "bin", "Release", "llama-cli.exe") | |
| if not os.path.exists(main_path): | |
| main_path = os.path.join(build_dir, "bin", "llama-cli") | |
| else: | |
| main_path = os.path.join(build_dir, "bin", "llama-cli") | |
| command = [ | |
| f'{main_path}', | |
| '-m', args.model, | |
| '-n', str(args.n_predict), | |
| '-t', str(args.threads), | |
| '-p', args.prompt, | |
| '-ngl', '0', | |
| '-c', str(args.ctx_size), | |
| '--temp', str(args.temperature), | |
| "-b", "1", | |
| ] | |
| if args.conversation: | |
| command.append("-cnv") | |
| run_command(command) | |
| def signal_handler(sig, frame): | |
| print("Ctrl+C pressed, exiting...") | |
| sys.exit(0) | |
| if __name__ == "__main__": | |
| signal.signal(signal.SIGINT, signal_handler) | |
| # Usage: python run_inference.py -p "Microsoft Corporation is an American multinational corporation and technology company headquartered in Redmond, Washington." | |
| parser = argparse.ArgumentParser(description='Run inference') | |
| parser.add_argument("-m", "--model", type=str, help="Path to model file", required=False, default="models/bitnet_b1_58-3B/ggml-model-i2_s.gguf") | |
| parser.add_argument("-n", "--n-predict", type=int, help="Number of tokens to predict when generating text", required=False, default=128) | |
| parser.add_argument("-p", "--prompt", type=str, help="Prompt to generate text from", required=True) | |
| parser.add_argument("-t", "--threads", type=int, help="Number of threads to use", required=False, default=2) | |
| parser.add_argument("-c", "--ctx-size", type=int, help="Size of the prompt context", required=False, default=2048) | |
| parser.add_argument("-temp", "--temperature", type=float, help="Temperature, a hyperparameter that controls the randomness of the generated text", required=False, default=0.8) | |
| parser.add_argument("-cnv", "--conversation", action='store_true', help="Whether to enable chat mode or not (for instruct models.)") | |
| parser.add_argument('--no-stream', action='store_true', help='Ganze Antwort auf einmal ausgeben') | |
| args = parser.parse_args() | |
| run_inference() | |