Instructions to use salvepilo/llama-cpp-gemma3-divzero-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use salvepilo/llama-cpp-gemma3-divzero-poc with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="salvepilo/llama-cpp-gemma3-divzero-poc", filename="poc_gemma3_divzero.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 salvepilo/llama-cpp-gemma3-divzero-poc 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 salvepilo/llama-cpp-gemma3-divzero-poc # Run inference directly in the terminal: llama cli -hf salvepilo/llama-cpp-gemma3-divzero-poc
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf salvepilo/llama-cpp-gemma3-divzero-poc # Run inference directly in the terminal: llama cli -hf salvepilo/llama-cpp-gemma3-divzero-poc
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 salvepilo/llama-cpp-gemma3-divzero-poc # Run inference directly in the terminal: ./llama-cli -hf salvepilo/llama-cpp-gemma3-divzero-poc
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 salvepilo/llama-cpp-gemma3-divzero-poc # Run inference directly in the terminal: ./build/bin/llama-cli -hf salvepilo/llama-cpp-gemma3-divzero-poc
Use Docker
docker model run hf.co/salvepilo/llama-cpp-gemma3-divzero-poc
- LM Studio
- Jan
- Ollama
How to use salvepilo/llama-cpp-gemma3-divzero-poc with Ollama:
ollama run hf.co/salvepilo/llama-cpp-gemma3-divzero-poc
- Unsloth Studio
How to use salvepilo/llama-cpp-gemma3-divzero-poc 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 salvepilo/llama-cpp-gemma3-divzero-poc 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 salvepilo/llama-cpp-gemma3-divzero-poc to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for salvepilo/llama-cpp-gemma3-divzero-poc to start chatting
- Atomic Chat new
- Docker Model Runner
How to use salvepilo/llama-cpp-gemma3-divzero-poc with Docker Model Runner:
docker model run hf.co/salvepilo/llama-cpp-gemma3-divzero-poc
- Lemonade
How to use salvepilo/llama-cpp-gemma3-divzero-poc with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull salvepilo/llama-cpp-gemma3-divzero-poc
Run and chat with the model
lemonade run user.llama-cpp-gemma3-divzero-poc-{{QUANT_TAG}}List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf salvepilo/llama-cpp-gemma3-divzero-poc# Run inference directly in the terminal:
llama cli -hf salvepilo/llama-cpp-gemma3-divzero-pocUse 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 salvepilo/llama-cpp-gemma3-divzero-poc# Run inference directly in the terminal:
./llama-cli -hf salvepilo/llama-cpp-gemma3-divzero-pocBuild 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 salvepilo/llama-cpp-gemma3-divzero-poc# Run inference directly in the terminal:
./build/bin/llama-cli -hf salvepilo/llama-cpp-gemma3-divzero-pocUse Docker
docker model run hf.co/salvepilo/llama-cpp-gemma3-divzero-pocllama.cpp Gemma3 Integer Division-by-Zero PoC
Proof-of-concept for a division-by-zero vulnerability in llama.cpp's Gemma3 architecture loader.
Vulnerable file: src/models/gemma3.cpp:32
Files
poc_gemma3_divzero.ggufโ 235-byte malicious GGUF (no tensors, triggers SIGFPE on x86_64)reproducer.cppโ standalone C++ reproducer (no llama.cpp build needed)
Quick test
# Standalone (no llama.cpp needed)
g++ -o reproducer reproducer.cpp -fsanitize=undefined -fno-sanitize-recover=all
./reproducer
# Expected: runtime error: division by zero
# With llama.cpp
./llama-cli -m poc_gemma3_divzero.gguf -p 'hello'
# Expected on x86_64: Floating point exception (exit 136)
Root cause
When block_count=62 (โ LLM_TYPE_27B) and attention.head_count is absent from the GGUF,
n_head(0) returns 0 and the expression n_embd / n_head(0) is integer division-by-zero.
Reported via Huntr bug bounty program.
- Downloads last month
- 17
We're not able to determine the quantization variants.
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf salvepilo/llama-cpp-gemma3-divzero-poc# Run inference directly in the terminal: llama cli -hf salvepilo/llama-cpp-gemma3-divzero-poc