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 /ci-run.sh

Qapdex

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	#!/bin/bash
	set -euo pipefail
	this=$(realpath "$0"); readonly this
	cd "$(dirname "$this")"
	shellcheck "$this"

	if (( $# != 1 && $# != 2 )); then
	cat >&2 <<'EOF'
	usage:
	ci-run.sh <tmp_dir> [<cache_dir>]

	This script wraps ci/run.sh:
	* If <tmp_dir> is a ramdisk, you can reduce writes to your SSD. If <tmp_dir> is not a ramdisk, keep in mind that total writes will increase by the size of <cache_dir>.
	(openllama_3b_v2: quantized models are about 30GB)
	* Persistent model and data files are synced to and from <cache_dir>,
	excluding generated .gguf files.
	(openllama_3b_v2: persistent files are about 6.6GB)
	* <cache_dir> defaults to ~/.cache/llama.cpp
	EOF
	exit 1
	fi

	cd .. # => llama.cpp repo root

	tmp="$1"
	mkdir -p "$tmp"
	tmp=$(realpath "$tmp")
	echo >&2 "Using tmp=$tmp"

	cache="${2-$HOME/.cache/llama.cpp}"
	mkdir -p "$cache"
	cache=$(realpath "$cache")
	echo >&2 "Using cache=$cache"

	_sync() {
	local from="$1"; shift
	local to="$1"; shift

	echo >&2 "Syncing from $from to $to"
	mkdir -p "$from" "$to"
	rsync -a "$from" "$to" --delete-during "$@"
	}

	_sync "$(realpath .)/" "$tmp/llama.cpp"
	_sync "$cache/ci-mnt/models/" "$tmp/llama.cpp/ci-mnt/models/"

	cd "$tmp/llama.cpp"
	bash ci/run.sh ci-out ci-mnt

	_sync 'ci-mnt/models/' "$cache/ci-mnt/models/" --exclude='*.gguf' -P