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
File size: 1,098 Bytes
c59fd93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | export const API_MODELS = {
LIST: '/v1/models',
LOAD: '/models/load',
SSE: '/models/sse',
UNLOAD: '/models/unload'
};
// chat completion routes, the control route drives realtime inference (e.g. end reasoning)
export const API_CHAT = {
COMPLETIONS: './v1/chat/completions',
CONTROL: './v1/chat/completions/control'
};
// slot introspection, requires the --slots flag on the server
export const API_SLOTS = {
LIST: './slots'
};
export const API_TOOLS = {
EXECUTE: '/tools',
LIST: '/tools'
};
// resumable stream routes, the conv::model identity travels as the conv_id query param
// because model names can contain slashes that a path segment cannot carry
// resume retry cadence while the owning model is still loading (server answers 503)
export const STREAM_RESUME_RETRY_MS = 2000;
export const API_STREAM = {
BASE: './v1/stream',
LOOKUP: './v1/streams/lookup'
};
// query params for the resumable stream routes
export const STREAM_QUERY_PARAMS = {
CONV_ID: 'conv_id',
FROM: 'from'
} as const;
/** CORS proxy endpoint path */
export const CORS_PROXY_ENDPOINT = '/cors-proxy';
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