Instructions to use thawndev/llama-cpp-systemic-eog-oob-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thawndev/llama-cpp-systemic-eog-oob-poc with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="thawndev/llama-cpp-systemic-eog-oob-poc",
	filename="poc_bert_defaults_oob.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 thawndev/llama-cpp-systemic-eog-oob-poc with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thawndev/llama-cpp-systemic-eog-oob-poc
# Run inference directly in the terminal:
llama-cli -hf thawndev/llama-cpp-systemic-eog-oob-poc

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf thawndev/llama-cpp-systemic-eog-oob-poc
# Run inference directly in the terminal:
llama-cli -hf thawndev/llama-cpp-systemic-eog-oob-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 thawndev/llama-cpp-systemic-eog-oob-poc
# Run inference directly in the terminal:
./llama-cli -hf thawndev/llama-cpp-systemic-eog-oob-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 thawndev/llama-cpp-systemic-eog-oob-poc
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thawndev/llama-cpp-systemic-eog-oob-poc

Use Docker

docker model run hf.co/thawndev/llama-cpp-systemic-eog-oob-poc

LM Studio
Jan
Ollama
How to use thawndev/llama-cpp-systemic-eog-oob-poc with Ollama:
```
ollama run hf.co/thawndev/llama-cpp-systemic-eog-oob-poc
```

Unsloth Studio

How to use thawndev/llama-cpp-systemic-eog-oob-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 thawndev/llama-cpp-systemic-eog-oob-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 thawndev/llama-cpp-systemic-eog-oob-poc to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for thawndev/llama-cpp-systemic-eog-oob-poc to start chatting

Docker Model Runner
How to use thawndev/llama-cpp-systemic-eog-oob-poc with Docker Model Runner:
```
docker model run hf.co/thawndev/llama-cpp-systemic-eog-oob-poc
```

Lemonade

How to use thawndev/llama-cpp-systemic-eog-oob-poc with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thawndev/llama-cpp-systemic-eog-oob-poc

Run and chat with the model

lemonade run user.llama-cpp-systemic-eog-oob-poc-{{QUANT_TAG}}

List all available models

lemonade list

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YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Systemic OOB memory corruption via hardcoded default special token IDs across multiple tokenizer models in llama.cpp

Affected software: ggerganov/llama.cpp Bug class: memory corruption (out-of-bounds read/write via operator[]) Impact: Out-of-bounds memory access in the EOG (end-of-generation) token processing loop at llama-vocab.cpp:2646, affecting SPM/llama, T5, and GPT-2 tokenizer models. In release builds without STL assertions, operator[] silently reads/writes adjacent heap memory. Format: gguf

This repository contains a proof-of-concept file for a vulnerability report. Access is gated — request via HuggingFace and grant to protectai-bot only.

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GGUF

Model size

5 params

Architecture

bert

Hardware compatibility

We're not able to determine the quantization variants.

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