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: 1,334 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 | 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;
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