Instructions to use dev-willbird1936/gguf-rwkv-trie-oob-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use dev-willbird1936/gguf-rwkv-trie-oob-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 dev-willbird1936/gguf-rwkv-trie-oob-poc # Run inference directly in the terminal: llama cli -hf dev-willbird1936/gguf-rwkv-trie-oob-poc
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dev-willbird1936/gguf-rwkv-trie-oob-poc # Run inference directly in the terminal: llama cli -hf dev-willbird1936/gguf-rwkv-trie-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 dev-willbird1936/gguf-rwkv-trie-oob-poc # Run inference directly in the terminal: ./llama-cli -hf dev-willbird1936/gguf-rwkv-trie-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 dev-willbird1936/gguf-rwkv-trie-oob-poc # Run inference directly in the terminal: ./build/bin/llama-cli -hf dev-willbird1936/gguf-rwkv-trie-oob-poc
Use Docker
docker model run hf.co/dev-willbird1936/gguf-rwkv-trie-oob-poc
- LM Studio
- Jan
- Ollama
How to use dev-willbird1936/gguf-rwkv-trie-oob-poc with Ollama:
ollama run hf.co/dev-willbird1936/gguf-rwkv-trie-oob-poc
- Unsloth Studio
How to use dev-willbird1936/gguf-rwkv-trie-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 dev-willbird1936/gguf-rwkv-trie-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 dev-willbird1936/gguf-rwkv-trie-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 dev-willbird1936/gguf-rwkv-trie-oob-poc to start chatting
- Docker Model Runner
How to use dev-willbird1936/gguf-rwkv-trie-oob-poc with Docker Model Runner:
docker model run hf.co/dev-willbird1936/gguf-rwkv-trie-oob-poc
- Lemonade
How to use dev-willbird1936/gguf-rwkv-trie-oob-poc with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dev-willbird1936/gguf-rwkv-trie-oob-poc
Run and chat with the model
lemonade run user.gguf-rwkv-trie-oob-poc-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Pull the model
# Download Lemonade from https://lemonade-server.ai/lemonade pull dev-willbird1936/gguf-rwkv-trie-oob-poc
Run and chat with the model
lemonade run user.gguf-rwkv-trie-oob-poc-{{QUANT_TAG}}List all available models
lemonade listYAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
llamacpp-05 PoC: RWKV tokenizer trie-walk heap out-of-bounds read
llamacpp05_rwkv_trie_oob.gguf is a minimal GGUF file (640 bytes) that selects the RWKV tokenizer (tokenizer.ggml.model = "rwkv") and defines a 3-token vocabulary where one token's escaped text decodes to bytes containing an embedded NUL (0x00). Building the tokenizer's naive_trie from that token plants a NUL edge inside the trie.
llama_tokenize's RWKV session tokenize loop advances the scan position with a pre-increment, text[++position], with no bounds check against text.size(). When the walk reaches the input's real end, the C-string terminator at text[size()] matches the trie's NUL edge and the loop does not stop: the next pre-increment reads text[size()+1], one byte past the heap-allocated std::string buffer.
Confirmed with AddressSanitizer: heap-buffer-overflow READ of size 1 at llama-vocab.cpp:1323, reached through the real, public llama_tokenize API, no internal-only code path involved.
Load this file with any llama.cpp build (or any consumer that calls llama_tokenize on text long enough to exceed the small-string optimization threshold) to trigger the read.
- Downloads last month
- -
We're not able to determine the quantization variants.
# Gated model: Login with a HF token with gated access permission hf auth login