Instructions to use Qapdex/SLM750-Edge-1.58-bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

SLM750-Edge-1.58-bit / BitNet /3rdparty /llama.cpp /scripts /hf.sh

Qapdex

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	#!/bin/bash
	#
	# Shortcut for downloading HF models
	#
	# Usage:
	# ./llama-cli -m $(./scripts/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
	# ./llama-cli -m $(./scripts/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
	# ./llama-cli -m $(./scripts/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
	#

	# all logs go to stderr
	function log {
	echo "$@" 1>&2
	}

	function usage {
	log "Usage: $0 [[--url] <url>] [--repo <repo>] [--file <file>] [--outdir <dir> [-h\|--help]"
	exit 1
	}

	# check for curl or wget
	function has_cmd {
	if ! [ -x "$(command -v $1)" ]; then
	return 1
	fi
	}

	if has_cmd wget; then
	cmd="wget -q --show-progress -c -O %s/%s %s"
	elif has_cmd curl; then
	cmd="curl -C - -f --output-dir %s -o %s -L %s"
	else
	log "[E] curl or wget not found"
	exit 1
	fi

	url=""
	repo=""
	file=""
	outdir="."

	# parse args
	while [[ $# -gt 0 ]]; do
	case "$1" in
	--url)
	url="$2"
	shift 2
	;;
	--repo)
	repo="$2"
	shift 2
	;;
	--file)
	file="$2"
	shift 2
	;;
	--outdir)
	outdir="$2"
	shift 2
	;;
	-h\|--help)
	usage
	;;
	*)
	url="$1"
	shift
	;;
	esac
	done

	if [ -n "$repo" ] && [ -n "$file" ]; then
	url="https://huggingface.co/$repo/resolve/main/$file"
	fi

	if [ -z "$url" ]; then
	log "[E] missing --url"
	usage
	fi

	# check if the URL is a HuggingFace model, and if so, try to download it
	is_url=false

	if [[ ${#url} -gt 22 ]]; then
	if [[ ${url:0:22} == "https://huggingface.co" ]]; then
	is_url=true
	fi
	fi

	if [ "$is_url" = false ]; then
	log "[E] invalid URL, must start with https://huggingface.co"
	exit 0
	fi

	# replace "blob/main" with "resolve/main"
	url=${url/blob\/main/resolve\/main}

	basename=$(basename $url)

	log "[+] attempting to download $basename"

	if [ -n "$cmd" ]; then
	cmd=$(printf "$cmd" "$outdir" "$basename" "$url")
	log "[+] $cmd"
	if $cmd; then
	echo $outdir/$basename
	exit 0
	fi
	fi

	log "[-] failed to download"

	exit 1