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/markdown.constants.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 828 Bytes
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/markdown.constants.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/constants/markdown.constants.ts
-
curl -L -o markdown.constants.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/markdown.constants.ts
828 Bytes
| 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; | |