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
| // swift-tools-version:5.5 | |
| import PackageDescription | |
| var sources = [ | |
| "src/llama.cpp", | |
| "src/llama-vocab.cpp", | |
| "src/llama-grammar.cpp", | |
| "src/llama-sampling.cpp", | |
| "src/unicode.cpp", | |
| "src/unicode-data.cpp", | |
| "ggml/src/ggml.c", | |
| "ggml/src/ggml-alloc.c", | |
| "ggml/src/ggml-backend.cpp", | |
| "ggml/src/ggml-quants.c", | |
| "ggml/src/ggml-aarch64.c", | |
| "spm-sources/ggml-bitnet-lut.cpp", | |
| "spm-sources/ggml-bitnet-mad.cpp", | |
| ] | |
| var resources: [Resource] = [] | |
| var linkerSettings: [LinkerSetting] = [] | |
| var cSettings: [CSetting] = [ | |
| .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), | |
| .unsafeFlags(["-fno-objc-arc"]), | |
| .define("GGML_BITNET_ARM_TL1") | |
| // NOTE: NEW_LAPACK will required iOS version 16.4+ | |
| // We should consider add this in the future when we drop support for iOS 14 | |
| // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc) | |
| // .define("ACCELERATE_NEW_LAPACK"), | |
| // .define("ACCELERATE_LAPACK_ILP64") | |
| ] | |
| #if canImport(Darwin) | |
| sources.append("ggml/src/ggml-metal.m") | |
| resources.append(.process("ggml/src/ggml-metal.metal")) | |
| linkerSettings.append(.linkedFramework("Accelerate")) | |
| cSettings.append( | |
| contentsOf: [ | |
| .define("GGML_USE_ACCELERATE"), | |
| .define("GGML_USE_METAL") | |
| ] | |
| ) | |
| #endif | |
| #if os(Linux) | |
| cSettings.append(.define("_GNU_SOURCE")) | |
| #endif | |
| let package = Package( | |
| name: "llama", | |
| platforms: [ | |
| .macOS(.v12), | |
| .iOS(.v14), | |
| .watchOS(.v4), | |
| .tvOS(.v14) | |
| ], | |
| products: [ | |
| .library(name: "llama", targets: ["llama"]), | |
| ], | |
| targets: [ | |
| .target( | |
| name: "llama", | |
| path: ".", | |
| exclude: [ | |
| "cmake", | |
| "examples", | |
| "scripts", | |
| "models", | |
| "tests", | |
| "CMakeLists.txt", | |
| "Makefile" | |
| ], | |
| sources: sources, | |
| resources: resources, | |
| publicHeadersPath: "spm-headers", | |
| cSettings: cSettings, | |
| linkerSettings: linkerSettings | |
| ) | |
| ], | |
| cxxLanguageStandard: .cxx11 | |
| ) | |