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 src/llama-memory.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.53 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/src/llama-memory.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/src/llama-memory.cpp
-
curl -L -o llama-memory.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/src/llama-memory.cpp
1.53 kB
| llama_memory_status llama_memory_status_combine(llama_memory_status s0, llama_memory_status s1) { | |
| bool has_update = false; | |
| switch (s0) { | |
| case LLAMA_MEMORY_STATUS_SUCCESS: | |
| { | |
| has_update = true; | |
| break; | |
| } | |
| case LLAMA_MEMORY_STATUS_NO_UPDATE: | |
| { | |
| break; | |
| } | |
| case LLAMA_MEMORY_STATUS_FAILED_PREPARE: | |
| case LLAMA_MEMORY_STATUS_FAILED_COMPUTE: | |
| { | |
| return s0; | |
| } | |
| } | |
| switch (s1) { | |
| case LLAMA_MEMORY_STATUS_SUCCESS: | |
| { | |
| has_update = true; | |
| break; | |
| } | |
| case LLAMA_MEMORY_STATUS_NO_UPDATE: | |
| { | |
| break; | |
| } | |
| case LLAMA_MEMORY_STATUS_FAILED_PREPARE: | |
| case LLAMA_MEMORY_STATUS_FAILED_COMPUTE: | |
| { | |
| return s1; | |
| } | |
| } | |
| // if either status has an update, then the combined status has an update | |
| return has_update ? LLAMA_MEMORY_STATUS_SUCCESS : LLAMA_MEMORY_STATUS_NO_UPDATE; | |
| } | |
| bool llama_memory_status_is_fail(llama_memory_status status) { | |
| switch (status) { | |
| case LLAMA_MEMORY_STATUS_SUCCESS: | |
| case LLAMA_MEMORY_STATUS_NO_UPDATE: | |
| { | |
| return false; | |
| } | |
| case LLAMA_MEMORY_STATUS_FAILED_PREPARE: | |
| case LLAMA_MEMORY_STATUS_FAILED_COMPUTE: | |
| { | |
| return true; | |
| } | |
| } | |
| return false; | |
| } | |