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 /ci /README.md

Aliguinga01

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CI

This CI implements heavy-duty workflows that run on self-hosted runners. Typically the purpose of these workflows is to cover hardware configurations that are not available from Github-hosted runners and/or require more computational resource than normally available.

It is a good practice, before publishing changes to execute the full CI locally on your machine. For example:

mkdir tmp

# CPU-only build
bash ./ci/run.sh ./tmp/results ./tmp/mnt

# with CUDA support
GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# with SYCL support
source /opt/intel/oneapi/setvars.sh
GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# with MUSA support
GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt

# etc.

Adding self-hosted runners

Add a self-hosted ggml-ci workflow to [[.github/workflows/build.yml]] with an appropriate label
Request a runner token from ggml-org (for example, via a comment in the PR or email)
Set-up a machine using the received token (docs)
Optionally update ci/run.sh to build and run on the target platform by gating the implementation with a GG_BUILD_... env