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 tools/ui/src/lib/constants/api-endpoints.constants.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.1 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/api-endpoints.constants.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/constants/api-endpoints.constants.ts
-
curl -L -o api-endpoints.constants.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/api-endpoints.constants.ts
1.1 kB
| 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'; | |