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 ggml/src/ggml-cpu/traits.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.23 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-cpu/traits.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/ggml/src/ggml-cpu/traits.cpp
-
curl -L -o traits.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/ggml/src/ggml-cpu/traits.cpp
1.23 kB
| namespace ggml::cpu { | |
| tensor_traits::~tensor_traits() {} | |
| extra_buffer_type::~extra_buffer_type() {} | |
| } // namespace ggml::cpu | |
| bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) { | |
| for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) { | |
| if (extra && extra->context) { | |
| auto buf_extra = (ggml::cpu::extra_buffer_type *) extra->context; | |
| auto tensor_traits = buf_extra->get_tensor_traits(op); | |
| if (tensor_traits && tensor_traits->compute_forward(params, op)) { | |
| return true; | |
| } | |
| } | |
| } | |
| return false; | |
| } | |
| bool ggml_cpu_extra_work_size(int n_threads, const struct ggml_tensor * op, size_t * size) { | |
| for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) { | |
| if (extra && extra->context) { | |
| auto buf_extra = (ggml::cpu::extra_buffer_type *) extra->context; | |
| auto tensor_traits = buf_extra->get_tensor_traits(op); | |
| if (tensor_traits && tensor_traits->work_size(n_threads, op, *size)) { | |
| return true; | |
| } | |
| } | |
| } | |
| return false; | |
| } | |