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: 2,122 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 43 44 45 46 47 48 49 50 51 | // Constants for the markdown code-block renderer: language/fence handling and CSS classes.
/** Parsing and escaping helpers for the markdown code-block renderer. */
export const CODE_BLOCK = {
AMPERSAND_REGEX: /&/g,
/** Language fallback used when no language is specified. */
DEFAULT_LANGUAGE: 'text',
/** Matches opening/closing markdown code fences. */
FENCE_PATTERN: /^```|\n```/g,
GT_REGEX: />/g,
/** Matches the language specifier at the start of a code fence. */
LANG_PATTERN: /^(\w*)\n?/,
LT_REGEX: /</g,
// Matches the `text:` prefix that file-type identifiers use to denote a
// plain-text language (e.g. `text:typescript`). Used by tool-call renderers
// to recover the underlying highlight.js language.
TEXT_LANGUAGE_PREFIX_REGEX: /^text:/,
// Whitespace-only empty lines (between start of string and first non-empty line).
// Used by trimCodePadding to drop leading/trailing phantom blank rows from LLM
// payload wrappers without touching internal blank lines.
TRIM_LEADING_PADDING_REGEX: /^(?:[ \t]*\n)+/,
TRIM_TRAILING_PADDING_REGEX: /(?:\n[ \t]*)+$/
} as const;
// Matches either Unix or Windows path separators so `String.split(REGEX)` can
// recover the trailing file-name segment from either `/foo/bar.txt` or
// `C:\foo\bar.txt`. Used wherever a parameter accepts a user-supplied path.
export const FILE_PATH_SEPARATOR_REGEX = /[\\/]/;
// Separates a file name from its extension, e.g. the '.' in `cover.png`.
export const FILE_EXTENSION_SEPARATOR = '.';
// Matches the `text:` prefix that file-type identifiers use to denote a
// plain-text language (e.g. `text:typescript`). Used by tool-call renderers
// to recover the underlying highlight.js language.
export const TEXT_LANGUAGE_PREFIX_REGEX = /^text:/;
/** CSS classes applied by the markdown code-block renderer. */
export const CODE_BLOCK_CLASS = {
ACTIONS: 'code-block-actions',
COPY_BTN: 'copy-code-btn',
HEADER: 'code-block-header',
LANGUAGE: 'code-language',
PREVIEW_BTN: 'preview-code-btn',
RELATIVE: 'relative',
SCROLL_CONTAINER: 'code-block-scroll-container',
WRAPPER: 'code-block-wrapper'
} as const;
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