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 tests/peg-parser/simple-tokenize.cpp from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 863 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tests/peg-parser/simple-tokenize.cpp
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tests/peg-parser/simple-tokenize.cpp
-
curl -L -o simple-tokenize.cpp https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tests/peg-parser/simple-tokenize.cpp
863 Bytes
| std::vector<std::string> simple_tokenize(const std::string & input) { | |
| std::vector<std::string> result; | |
| std::string current; | |
| for (size_t i = 0; i < input.size(); i++) { | |
| switch (input[i]) { | |
| case ' ': | |
| case '\n': | |
| case '\t': | |
| case '{': | |
| case '}': | |
| case ',': | |
| case '[': | |
| case '"': | |
| case ']': | |
| case '.': | |
| case '<': | |
| case '>': | |
| case '=': | |
| case '/': | |
| if (!current.empty()) { | |
| result.push_back(current); | |
| current.clear(); | |
| } | |
| default:; | |
| } | |
| current += input[i]; | |
| } | |
| if (!current.empty()) { | |
| result.push_back(current); | |
| } | |
| return result; | |
| } | |