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 examples/lookup/lookup-create.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/lookup/lookup-create.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/lookup/lookup-create.cpp
-
curl -L -o lookup-create.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/lookup/lookup-create.cpp
1.2 kB
| int main(int argc, char ** argv){ | |
| std::setlocale(LC_NUMERIC, "C"); | |
| common_params params; | |
| common_init(); | |
| if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) { | |
| return 1; | |
| } | |
| // init llama.cpp | |
| llama_backend_init(); | |
| llama_numa_init(params.numa); | |
| // load the model | |
| auto llama_init = common_init_from_params(params); | |
| auto * model = llama_init->model(); | |
| auto * ctx = llama_init->context(); | |
| GGML_ASSERT(model != nullptr); | |
| // tokenize the prompt | |
| std::vector<llama_token> inp; | |
| inp = common_tokenize(ctx, params.prompt, true, true); | |
| fprintf(stderr, "%s: tokenization done\n", __func__); | |
| common_ngram_cache ngram_cache; | |
| common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true); | |
| fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.speculative.ngram_cache.lookup_cache_static.c_str()); | |
| common_ngram_cache_save(ngram_cache, params.speculative.ngram_cache.lookup_cache_static); | |
| return 0; | |
| } | |