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 /benchmark.sh

Qapdex

Rename BitNet/files.sh/benchmark.sh to BitNet/benchmark.sh

63ba696 verified 14 days ago

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	#!/usr/bin/env bash
	MODEL="${1:-models/quantized_q4km.gguf}"
	PROMPT="${2:-Hallo BitNet}"
	TOKENS="${3:-64}"
	THREADS=$(nproc)
	VK_ICD_FILENAMES="$PREFIX/share/vulkan/icd.d/freedreno_icd.aarch64.json"
	KMP_AFFINITY="disabled"
	EXECUTABLE_DISABLE_MTE="1"

	echo "Model: $MODEL"
	echo "Threads: $THREADS"
	echo "Prompt: $PROMPT"
	echo "Max new tokens: $TOKENS"


	START_LOAD=$(date +%s)
	export LD_LIBRARY_PATH=/data/data/com.termux/files/usr/lib/python3.13/site-packages/lib:/data/data/com.termux/files/usr/lib:$LD_LIBRARY_PATH
	python3 run_inference.py --model "$MODEL" --backend llama.cpp --threads $THREADS --n_ctx 512 --max_new_tokens 1 --prompt "$PROMPT" >/dev/null 2>&1
	END_LOAD=$(date +%s)
	LOAD_SEC=$((END_LOAD-START_LOAD))

	echo "Model load + first token time: ${LOAD_SEC}s (approx)"

	# Measure generation time for TOKENS
	START_GEN=$(date +%s.%N)
	python3 run_inference.py --model "$MODEL" --backend llama.cpp --threads $THREADS --n_ctx 512 --max_new_tokens $TOKENS --prompt "$PROMPT" --no-stream >/dev/null 2>&1
	END_GEN=$(date +%s.%N)
	GEN_SEC=$(echo "$END_GEN - $START_GEN" \| bc -l)
	TOK_PER_SEC=$(echo "$TOKENS / $GEN_SEC" \| bc -l)

	printf "Generated %s tokens in %.2f s → %.2f tokens/s\n" "$TOKENS" "$GEN_SEC" "$TOK_PER_SEC"