Instructions to use VladHong/llama-cpp-K2-FAST2 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 VladHong/llama-cpp-K2-FAST2 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 VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: llama cli -hf VladHong/llama-cpp-K2-FAST2
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf VladHong/llama-cpp-K2-FAST2 # Run inference directly in the terminal: ./build/bin/llama cli -hf VladHong/llama-cpp-K2-FAST2
Use Docker
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Llama new
- LM Studio
- Jan
- Ollama
How to use VladHong/llama-cpp-K2-FAST2 with Ollama:
ollama run hf.co/VladHong/llama-cpp-K2-FAST2
- Unsloth Desktop
- Docker Model Runner
How to use VladHong/llama-cpp-K2-FAST2 with Docker Model Runner:
docker model run hf.co/VladHong/llama-cpp-K2-FAST2
- Lemonade
How to use VladHong/llama-cpp-K2-FAST2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull VladHong/llama-cpp-K2-FAST2
Run and chat with the model
lemonade run user.llama-cpp-K2-FAST2-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Download common/ngram-mod.h from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 728 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/common/ngram-mod.h
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/common/ngram-mod.h
-
curl -L -o ngram-mod.h https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/common/ngram-mod.h
728 Bytes
| // | |
| // common_ngram_mod | |
| // ref: https://github.com/ggml-org/llama.cpp/pull/19164 | |
| // | |
| // basic n-gram hasher | |
| struct common_ngram_mod { | |
| using entry_t = int32_t; | |
| static constexpr entry_t EMPTY = -1; | |
| common_ngram_mod(uint16_t n, size_t size); | |
| size_t idx(const entry_t * tokens) const; | |
| void add(const entry_t * tokens); | |
| entry_t get(const entry_t * tokens) const; // return -1 if not found | |
| void reset(); | |
| size_t get_n() const; | |
| size_t get_used() const; | |
| size_t size() const; | |
| size_t size_bytes() const; | |
| private: | |
| size_t n; // ngram size to hash | |
| size_t used; | |
| std::vector<entry_t> entries; | |
| }; | |