gemma-3-4b-it-gguf

arunb74/gemma-3-4b-it-gguf

gemma-3-4b-it-gguf is a machine learning model from arunb74. Use it for the machine learning task on the model card, and read the license before you ship it in a product.

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At a glance

Access: Public
Created: Sep 17, 2026
Updated: Sep 17, 2026
SHA: 19bd4b64

Model card

From the Hugging Face model README

llama.cpp

<b>LLM inference in C/C++</b>

ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API

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Quick start

A few options to get llama.cpp installed on your machine:

Visit https://llama.app and follow the instructions
Run with Docker - see our Docker documentation
Download pre-built binaries from the releases page
Build from source by cloning this repository - check out our build guide

Once installed:

# Download and run a model directly from Hugging Face
llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF

# Launch OpenAI-compatible API server
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF

<table align="center"> <tr> <td align="center" width=50%> <img width="1310" height="888" alt="VLM session with `llama cli`" src="https://github.com/user-attachments/assets/88726b48-1713-48aa-a525-95a02e78afc4" /> <i>VLM session with <b>llama cli</b></i> </td> <td align="center"> <img width="1392" height="958" alt="Built-in web UI against `llama serve` running Qwen 3.6" src="https://github.com/user-attachments/assets/b402f972-2e32-4def-8771-8d849f08cf2e" /> <i>Built-in web UI against <b>llama serve</b></i> </td> </tr> <table>

Description

The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud.

Plain C/C++ implementation without any dependencies
Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
AVX, AVX2, AVX512 and AMX support for x86 architectures
RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
Vulkan and SYCL backend support
CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

The llama.cpp project is build on top of the ggml library.

Supported backends

Backend	Target devices
BLAS	All
BLIS	All
CANN	Ascend NPU
CUDA	Nvidia GPU
HIP	AMD GPU
Hexagon	Snapdragon
IBM zDNN	IBM Z & LinuxONE
MUSA	Moore Threads GPU
Metal	Apple Silicon
OpenCL	Adreno GPU
OpenVINO [In Progress]	Intel CPUs, GPUs, and NPUs
RPC	All
SYCL	Intel GPU
VirtGPU	VirtGPU APIR
Vulkan	GPU
WebGPU	All
ZenDNN	AMD CPU

Documentation

Tools

Development

Contributing

Contributors can open PRs
Collaborators will be invited based on contributions
Maintainers can push to branches in the llama.cpp repo and merge PRs into the master branch
Any help with managing issues, PRs and projects is very appreciated!
Read the CONTRIBUTING.md for more information

Acknowledgements

yhirose/cpp-httplib - Single-header HTTP server, used by llama-server - MIT license
nothings/stb - Single-header image format decoder, used by multimodal subsystem - Public domain
nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
mackron/miniaudio - Single-header audio format decoder, used by multimodal subsystem - Public domain
sheredom/subprocess.h - Single-header process launching solution for C and C++ - Public domain

Created: Sep 17, 2026
Updated: Sep 17, 2026