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/auto-scroll.constants.ts from VladHong/llama-cpp-K2-FAST2: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/auto-scroll.constants.ts
- Command line
-
hf download hf://VladHong/llama-cpp-K2-FAST2/tools/ui/src/lib/constants/auto-scroll.constants.ts
-
curl -L -o auto-scroll.constants.ts https://huggingface.co/VladHong/llama-cpp-K2-FAST2/resolve/main/tools/ui/src/lib/constants/auto-scroll.constants.ts
1.33 kB
| export const AUTO_SCROLL_INTERVAL = 100; | |
| // Conversation landing: the page keeps growing after the first bottom pin | |
| // without DOM mutations (content-visibility size realizations, syntax | |
| // highlight passes), so the pin repeats every frame until the height holds | |
| // for this many consecutive frames, bounded by the time cap below. | |
| export const LANDING_STABLE_FRAMES = 10; | |
| export const LANDING_SETTLE_MAX_MS = 1000; | |
| // Chat main view: tight threshold because scroll-here events come from | |
| // discrete assistant-message appends. | |
| export const AUTO_SCROLL_AT_BOTTOM_THRESHOLD = 10; | |
| // Reasoning block: stickier because reasoning fires many small | |
| // incremental DOM writes that easily drift a few pixels off bottom. | |
| export const REASONING_SCROLL_AT_BOTTOM_THRESHOLD_PX = 64; | |
| // Syntax-highlighted code: stickier than the chat main view because line | |
| // wrap reflows while the highlight.js pass settles can drift a few pixels | |
| // off bottom. | |
| export const SYNTAX_CODE_SCROLL_AT_BOTTOM_THRESHOLD_PX = 32; | |
| // Streaming tool output (e.g. exec_shell_command): shell commands produce | |
| // lots of small line writes and the exit-code line appended at the tail | |
| // past the last user-visible frame is what triggers DOM drift, so use a | |
| // threshold generous enough to capture that tail flush. | |
| export const TOOL_RUNTIME_SCROLL_AT_BOTTOM_THRESHOLD_PX = 64; | |