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/eval-callback/eval-callback.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 2.14 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/eval-callback/eval-callback.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/examples/eval-callback/eval-callback.cpp
-
curl -L -o eval-callback.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/examples/eval-callback/eval-callback.cpp
2.14 kB
| static bool run(llama_context * ctx, const common_params & params) { | |
| const llama_model * model = llama_get_model(ctx); | |
| const llama_vocab * vocab = llama_model_get_vocab(model); | |
| const bool add_bos = llama_vocab_get_add_bos(vocab); | |
| std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos, true); | |
| if (tokens.empty()) { | |
| LOG_ERR("%s : there are not input tokens to process - (try to provide a prompt with '-p')\n", __func__); | |
| return false; | |
| } | |
| LOG_INF("number of input tokens = %zu\n", tokens.size()); | |
| for (size_t i = 0; i < tokens.size(); ++i) { | |
| LOG_INF(" %d\n", tokens[i]); | |
| } | |
| if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) { | |
| LOG_ERR("%s : failed to eval\n", __func__); | |
| return false; | |
| } | |
| return true; | |
| } | |
| int main(int argc, char ** argv) { | |
| std::setlocale(LC_NUMERIC, "C"); | |
| common_debug_cb_user_data cb_data; | |
| common_params params; | |
| common_init(); | |
| if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { | |
| return 1; | |
| } | |
| llama_backend_init(); | |
| llama_numa_init(params.numa); | |
| // pass the callback to the backend scheduler | |
| // it will be executed for each node during the graph computation | |
| params.cb_eval = common_debug_cb_eval; | |
| params.cb_eval_user_data = &cb_data; | |
| params.warmup = false; | |
| // init | |
| auto llama_init = common_init_from_params(params); | |
| auto * model = llama_init->model(); | |
| auto * ctx = llama_init->context(); | |
| if (model == nullptr || ctx == nullptr) { | |
| LOG_ERR("%s : failed to init\n", __func__); | |
| return 1; | |
| } | |
| // print system information | |
| { | |
| LOG_INF("\n"); | |
| LOG_INF("%s\n", common_params_get_system_info(params).c_str()); | |
| LOG_INF("\n"); | |
| } | |
| bool OK = run(ctx, params); | |
| if (!OK) { | |
| return 1; | |
| } | |
| LOG("\n"); | |
| llama_perf_context_print(ctx); | |
| llama_backend_free(); | |
| return 0; | |
| } | |