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: 828 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 | export const IMAGE_NOT_ERROR_BOUND_SELECTOR = 'img:not([data-error-bound])';
/** Data attributes for the markdown renderer DOM contract. */
export const MARKDOWN_DATA_ATTRS = {
BLOCK_ID: 'data-block-id',
CODE_ID: 'data-code-id',
ERROR_BOUND: 'data-error-bound',
ERROR_HANDLED: 'data-error-handled',
LISTENER_BOUND: 'data-listener-bound',
ORIGINAL_SRC: 'data-original-src'
} as const;
/** Markdown structural markers used by `looksLikeMarkdown`. Inline / line-level. */
export const MARKDOWN = {
ATX_HEADING_REGEX: /^#{1,6}\s+\S/,
BLOCKQUOTE_REGEX: /^>\s+\S/,
BOLD_REGEX: /\*\*[^*\n]+\*\*|__[^_\n]+__/,
CODE_FENCE_REGEX: /^(```|~~~)/m,
LINK_REGEX: /\[[^\]\n]+\]\([^)\s]+\)/,
LIST_BULLET_REGEX: /^\s*[-*+]\s+\S/,
LIST_NUMBERED_REGEX: /^\s*\d+[.)]\s+\S/,
TABLE_SEPARATOR_REGEX: /^\s*\|?[\s:|-]+\|?\s*$/
} as const;
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