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/utils/command-token.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 961 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/command-token.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/utils/command-token.ts
-
curl -L -o command-token.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/utils/command-token.ts
961 Bytes
| /** | |
| * Slash-command token detection for the chat form. Valid only at offset 0. | |
| */ | |
| export function findCommandToken( | |
| value: string | |
| ): { name: string; args: string; end: number } | null { | |
| if (!value.startsWith('/')) return null; | |
| const rest = value.slice(1); | |
| const spaceIdx = rest.search(/\s/); | |
| const name = spaceIdx === -1 ? rest : rest.slice(0, spaceIdx); | |
| const args = spaceIdx === -1 ? '' : rest.slice(spaceIdx + 1); | |
| return { args, end: value.length, name }; | |
| } | |
| /** | |
| * Stable signature of a slash-command token for use as a "dismissed" | |
| * marker: while the picker is closed and this exact token is still intact, | |
| * the picker does not re-open on in-token edits. | |
| */ | |
| export interface CommandDismissSnapshot { | |
| name: string; | |
| args: string; | |
| } | |
| export function takeCommandDismissSnapshot(value: string): CommandDismissSnapshot | null { | |
| const token = findCommandToken(value); | |
| if (!token) return null; | |
| return { args: token.args, name: token.name }; | |
| } | |