Instructions to use Aliguinga01/rule_violation2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2:F16
# Run inference directly in the terminal:
llama cli -hf Aliguinga01/rule_violation2:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Aliguinga01/rule_violation2:F16
# Run inference directly in the terminal:
llama cli -hf Aliguinga01/rule_violation2:F16

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 Aliguinga01/rule_violation2:F16
# Run inference directly in the terminal:
./llama-cli -hf Aliguinga01/rule_violation2:F16

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 Aliguinga01/rule_violation2:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Aliguinga01/rule_violation2:F16

Use Docker

docker model run hf.co/Aliguinga01/rule_violation2:F16

LM Studio
Jan
Ollama
How to use Aliguinga01/rule_violation2 with Ollama:
```
ollama run hf.co/Aliguinga01/rule_violation2:F16
```

Unsloth Studio

How to use Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2 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 Aliguinga01/rule_violation2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Aliguinga01/rule_violation2 to start chatting

Docker Model Runner
How to use Aliguinga01/rule_violation2 with Docker Model Runner:
```
docker model run hf.co/Aliguinga01/rule_violation2:F16
```

Lemonade

How to use Aliguinga01/rule_violation2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Aliguinga01/rule_violation2:F16

Run and chat with the model

lemonade run user.rule_violation2-F16

List all available models

lemonade list

Atomic Chat

rule_violation2 / llama.cpp /docs /backend /OPENCL.md

Aliguinga01

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	# llama.cpp for OpenCL

	- [Background](#background)
	- [OS](#os)
	- [Hardware](#hardware)
	- [DataType Supports](#datatype-supports)
	- [Model Preparation](#model-preparation)
	- [CMake Options](#cmake-options)
	- [Android](#android)
	- [Windows 11 Arm64](#windows-11-arm64)
	- [Known Issue](#known-issues)
	- [TODO](#todo)

	## Background

	OpenCL (Open Computing Language) is an open, royalty-free standard for cross-platform, parallel programming of diverse accelerators found in supercomputers, cloud servers, personal computers, mobile devices and embedded platforms. OpenCL specifies a programming language (based on C99) for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the compute devices. Similar to CUDA, OpenCL has been widely used to program GPUs and is supported by most GPU vendors.

	### Llama.cpp + OpenCL

	The llama.cpp OpenCL backend is designed to enable llama.cpp on Qualcomm Adreno GPU firstly via OpenCL. Thanks to the portabilty of OpenCL, the OpenCL backend can also run on certain Intel GPUs although the performance is not optimal.

	## OS

	\| OS \| Status \| Verified \|
	\|---------\|---------\|------------------------------------------------\|
	\| Android \| Support \| Snapdragon 8 Gen 3, Snapdragon 8 Elite \|
	\| Windows \| Support \| Windows 11 Arm64 with Snapdragon X Elite \|
	\| Linux \| Support \| Ubuntu 22.04 WSL2 with Intel 12700H \|

	## Hardware

	### Adreno GPU

	Verified devices

	\| Adreno GPU \| Status \|
	\|:------------------------------------:\|:-------:\|
	\| Adreno 750 (Snapdragon 8 Gen 3) \| Support \|
	\| Adreno 830 (Snapdragon 8 Elite) \| Support \|
	\| Adreno X85 (Snapdragon X Elite) \| Support \|

	## DataType Supports

	\| DataType \| Status \|
	\|:----------------------:\|:--------------------------:\|
	\| Q4_0 \| Support \|
	\| Q6_K \| Support, but not optimized \|

	## Model Preparation

	You can refer to the general [Prepare and Quantize](README.md#prepare-and-quantize) guide for model prepration.

	Currently we support `Q4_0` quantization and have optimize for it. To achieve best performance on Adreno GPU, add `--pure` to `llama-quantize`. For example,

	```sh
	./llama-quantize --pure ggml-model-qwen2.5-3b-f16.gguf ggml-model-qwen-3b-Q4_0.gguf Q4_0
	```

	Since `Q6_K` is also supported, `Q4_0` quantization without `--pure` will also work. However, the performance will be worse compared to pure `Q4_0` quantization.

	## CMake Options

	The OpenCL backend has the following CMake options that control the behavior of the backend.

	\| CMake options \| Default value \| Description \|
	\|:---------------------------------:\|:--------------:\|:------------------------------------------\|
	\| `GGML_OPENCL_EMBED_KERNELS` \| `ON` \| Embed OpenCL kernels into the executable. \|
	\| `GGML_OPENCL_USE_ADRENO_KERNELS` \| `ON` \| Use kernels optimized for Adreno. \|

	## Android

	Ubuntu 22.04 is used for targeting Android. Make sure the following tools are accessible from command line,

	* Git
	* CMake 3.29
	* Ninja
	* Python3

	### I. Setup Environment

	1. Install NDK

	```sh
	cd ~
	wget https://dl.google.com/android/repository/commandlinetools-linux-8512546_latest.zip && \
	unzip commandlinetools-linux-8512546_latest.zip && \
	mkdir -p ~/android-sdk/cmdline-tools && \
	mv cmdline-tools latest && \
	mv latest ~/android-sdk/cmdline-tools/ && \
	rm -rf commandlinetools-linux-8512546_latest.zip

	yes \| ~/android-sdk/cmdline-tools/latest/bin/sdkmanager "ndk;26.3.11579264"
	```

	2. Install OpenCL Headers and Library

	```sh
	mkdir -p ~/dev/llm
	cd ~/dev/llm

	git clone https://github.com/KhronosGroup/OpenCL-Headers && \
	cd OpenCL-Headers && \
	cp -r CL ~/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include

	cd ~/dev/llm

	git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && \
	cd OpenCL-ICD-Loader && \
	mkdir build_ndk26 && cd build_ndk26 && \
	cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \
	-DCMAKE_TOOLCHAIN_FILE=$HOME/android-sdk/ndk/26.3.11579264/build/cmake/android.toolchain.cmake \
	-DOPENCL_ICD_LOADER_HEADERS_DIR=$HOME/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \
	-DANDROID_ABI=arm64-v8a \
	-DANDROID_PLATFORM=24 \
	-DANDROID_STL=c++_shared && \
	ninja && \
	cp libOpenCL.so ~/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
	```

	### II. Build llama.cpp

	```sh
	cd ~/dev/llm

	git clone https://github.com/ggml-org/llama.cpp && \
	cd llama.cpp && \
	mkdir build-android && cd build-android

	cmake .. -G Ninja \
	-DCMAKE_TOOLCHAIN_FILE=$HOME/android-sdk/ndk/26.3.11579264/build/cmake/android.toolchain.cmake \
	-DANDROID_ABI=arm64-v8a \
	-DANDROID_PLATFORM=android-28 \
	-DBUILD_SHARED_LIBS=OFF \
	-DGGML_OPENCL=ON

	ninja
	```

	## Windows 11 Arm64

	A Snapdragon X Elite device with Windows 11 Arm64 is used. Make sure the following tools are accessible from command line,

	* Git
	* CMake 3.29
	* Clang 19
	* Ninja
	* Visual Studio 2022
	* Powershell 7

	Visual Studio provides necessary headers and libraries although it is not directly used for building.
	Alternatively, Visual Studio Build Tools can be installed instead of the full Visual Studio.

	Powershell 7 is used for the following commands.
	If an older version of Powershell is used, these commands may not work as they are.

	### I. Setup Environment

	1. Install OpenCL Headers and Library

	```powershell
	mkdir -p ~/dev/llm

	cd ~/dev/llm
	git clone https://github.com/KhronosGroup/OpenCL-Headers && cd OpenCL-Headers
	mkdir build && cd build
	cmake .. -G Ninja `
	-DBUILD_TESTING=OFF `
	-DOPENCL_HEADERS_BUILD_TESTING=OFF `
	-DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
	-DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl"
	cmake --build . --target install

	cd ~/dev/llm
	git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && cd OpenCL-ICD-Loader
	mkdir build && cd build
	cmake .. -G Ninja `
	-DCMAKE_BUILD_TYPE=Release `
	-DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" `
	-DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl"
	cmake --build . --target install
	```

	### II. Build llama.cpp

	```powershell

	mkdir -p ~/dev/llm
	cd ~/dev/llm

	git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
	mkdir build && cd build

	cmake .. -G Ninja `
	-DCMAKE_TOOLCHAIN_FILE="$HOME/dev/llm/llama.cpp/cmake/arm64-windows-llvm.cmake" `
	-DCMAKE_BUILD_TYPE=Release `
	-DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" `
	-DBUILD_SHARED_LIBS=OFF `
	-DGGML_OPENCL=ON
	ninja
	```

	## Known Issues

	- Currently OpenCL backend does not work on Adreno 6xx GPUs.

	## TODO

	- Optimization for Q6_K
	- Support and optimization for Q4_K