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 
