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.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.13 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/common/ngram-mod.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/common/ngram-mod.cpp
-
curl -L -o ngram-mod.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/common/ngram-mod.cpp
1.13 kB
| // | |
| // common_ngram_mod | |
| // | |
| common_ngram_mod::common_ngram_mod(uint16_t n, size_t size) : n(n), used(0) { | |
| entries.resize(size); | |
| reset(); | |
| } | |
| size_t common_ngram_mod::idx(const entry_t * tokens) const { | |
| size_t res = 0; | |
| for (size_t i = 0; i < n; ++i) { | |
| res = res*6364136223846793005ULL + tokens[i]; | |
| } | |
| res = res % entries.size(); | |
| return res; | |
| } | |
| void common_ngram_mod::add(const entry_t * tokens) { | |
| const size_t i = idx(tokens); | |
| if (entries[i] == EMPTY) { | |
| used++; | |
| } | |
| entries[i] = tokens[n]; | |
| } | |
| common_ngram_mod::entry_t common_ngram_mod::get(const entry_t * tokens) const { | |
| const size_t i = idx(tokens); | |
| return entries[i]; | |
| } | |
| void common_ngram_mod::reset() { | |
| std::fill(entries.begin(), entries.end(), EMPTY); | |
| used = 0; | |
| } | |
| size_t common_ngram_mod::get_n() const { | |
| return n; | |
| } | |
| size_t common_ngram_mod::get_used() const { | |
| return used; | |
| } | |
| size_t common_ngram_mod::size() const { | |
| return entries.size(); | |
| } | |
| size_t common_ngram_mod::size_bytes() const { | |
| return entries.size() * sizeof(entries[0]); | |
| } | |