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/path-display.constants.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/path-display.constants.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/constants/path-display.constants.ts
-
curl -L -o path-display.constants.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/path-display.constants.ts
1.04 kB
| /** | |
| * Constants for synthetic working-directory messages. | |
| * | |
| * The synthetic cwd-change message is text the UI renders as a folder row | |
| * and the model sees as a turn reminder. The prefix and cleared marker keep | |
| * the human-readable wording; the file-link regexes parse the | |
| * `[file:///abs/path](display)` payload back out on the UI side. | |
| */ | |
| import { UrlProtocol } from '$lib/enums'; | |
| export const CWD_CHANGED_PREFIX = 'Set working directory to '; | |
| export const CWD_CLEARED_TEXT = 'Working directory cleared'; | |
| /** Trailing separator that marks a path as a directory. */ | |
| export const DIRECTORY_PATH_SUFFIX = '/'; | |
| export const HOME_TILDE = '~'; | |
| export const HOME_TILDE_PREFIX = '~/'; // tilde plus path separator | |
| /** Scheme prefix of the file link embedded in a synthetic cwd message. */ | |
| export const FILE_URI_PREFIX = `${UrlProtocol.FILE}//`; | |
| /** Matches the leading `[file:///abs/path](display)` link; not anchored to the end so trailing guidance may follow. */ | |
| export const CWD_LINK_REGEX = /^\[file:\/\/([\s\S]*?)\]\(([\s\S]*?)\)/; | |