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: 1,395 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 | /**
* Browser executor for the `get_datetime` tool. It runs in the browser, so it
* reports the user's own clock and time zone instead of the server's UTC time -
* a chat about "tomorrow" means the user's tomorrow, not the host's.
*
* @see buildGetDatetimeToolDefinition in constants/get-datetime.ts - tool schema sent to the LLM
*/
import type { ToolExecutionResult } from '$lib/types';
function pad(value: number): string {
return String(value).padStart(2, '0');
}
/** ISO 8601 in local time, e.g. `2026-08-17T14:05:09+02:00` */
function localIsoString(date: Date): string {
// getTimezoneOffset() counts minutes behind UTC, ISO 8601 counts them ahead
const offset = -date.getTimezoneOffset();
const sign = offset < 0 ? '-' : '+';
const absOffset = Math.abs(offset);
const day = `${date.getFullYear()}-${pad(date.getMonth() + 1)}-${pad(date.getDate())}`;
const time = `${pad(date.getHours())}:${pad(date.getMinutes())}:${pad(date.getSeconds())}`;
return `${day}T${time}${sign}${pad(Math.floor(absOffset / 60))}:${pad(absOffset % 60)}`;
}
/** The `result` field keeps the shape the `get_datetime` renderer already reads. */
export function executeGetDatetimeTool(): ToolExecutionResult {
const now = new Date();
return {
content: JSON.stringify({
result: localIsoString(now),
timezone: Intl.DateTimeFormat().resolvedOptions().timeZone
}),
isError: false
};
}
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