Instructions to use AM-Core/gguf-divzero-loader-crash-poc-clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AM-Core/gguf-divzero-loader-crash-poc-clean with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AM-Core/gguf-divzero-loader-crash-poc-clean", filename="poc_gguf_divzero_alignment_loader_crash.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use AM-Core/gguf-divzero-loader-crash-poc-clean with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AM-Core/gguf-divzero-loader-crash-poc-clean # Run inference directly in the terminal: llama-cli -hf AM-Core/gguf-divzero-loader-crash-poc-clean
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AM-Core/gguf-divzero-loader-crash-poc-clean # Run inference directly in the terminal: llama-cli -hf AM-Core/gguf-divzero-loader-crash-poc-clean
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 AM-Core/gguf-divzero-loader-crash-poc-clean # Run inference directly in the terminal: ./llama-cli -hf AM-Core/gguf-divzero-loader-crash-poc-clean
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 AM-Core/gguf-divzero-loader-crash-poc-clean # Run inference directly in the terminal: ./build/bin/llama-cli -hf AM-Core/gguf-divzero-loader-crash-poc-clean
Use Docker
docker model run hf.co/AM-Core/gguf-divzero-loader-crash-poc-clean
- LM Studio
- Jan
- Ollama
How to use AM-Core/gguf-divzero-loader-crash-poc-clean with Ollama:
ollama run hf.co/AM-Core/gguf-divzero-loader-crash-poc-clean
- Unsloth Studio new
How to use AM-Core/gguf-divzero-loader-crash-poc-clean 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 AM-Core/gguf-divzero-loader-crash-poc-clean 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 AM-Core/gguf-divzero-loader-crash-poc-clean to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AM-Core/gguf-divzero-loader-crash-poc-clean to start chatting
- Docker Model Runner
How to use AM-Core/gguf-divzero-loader-crash-poc-clean with Docker Model Runner:
docker model run hf.co/AM-Core/gguf-divzero-loader-crash-poc-clean
- Lemonade
How to use AM-Core/gguf-divzero-loader-crash-poc-clean with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AM-Core/gguf-divzero-loader-crash-poc-clean
Run and chat with the model
lemonade run user.gguf-divzero-loader-crash-poc-clean-{{QUANT_TAG}}List all available models
lemonade list
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
GGUF division-by-zero loader crash PoC
This repository contains a malformed GGUF file that triggers sanitizer-backed undefined behavior during llama.cpp model loading.
PoC file: poc_gguf_divzero_alignment_loader_crash.gguf
SHA256: a0df9cbeba1fb33b95fbd8b4dd7b403a5da25c4c9ee1649674f9344f55c1f593
Size: 2387040 bytes
Reproduce: LLAMA_CLI=/path/to/llama-cli bash reproduce.sh
Expected:
- runtime error: division by zero
- AddressSanitizer: FPE
- abnormal process termination
Boundary: This is not RCE. This is malformed GGUF model-file-triggered undefined behavior and loader crash.
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We're not able to determine the quantization variants.
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AM-Core/gguf-divzero-loader-crash-poc-clean", filename="poc_gguf_divzero_alignment_loader_crash.gguf", )