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: 1,543 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 | /**
* Parsing helpers for `exec_shell_command` tool output.
*
* The server appends one final line to the response - an exit-code summary
* shaped as `[exit code: N]` (and optionally followed by `[exit due to timed
* out]`) - so the renderer can color that final line based on success/failure
* without parsing the entire output stream.
*/
export interface ExecShellExitStatus {
code: number;
timedOut: boolean;
/** Length-prefix slice for matching against the rendered lines list. */
rawText: string;
}
// Anchor to the absolute end so intermediate "[exit code: N]" string content
// (e.g. a shell echo) doesn't false-positive.
const EXIT_CODE_TAIL_REGEX = /\[exit code: (-?\d+)\](?: \[exit due to timed out\])?\s*$/;
export function parseExecShellCommandExitStatus(
toolResultString: string | undefined
): ExecShellExitStatus | undefined {
if (!toolResultString) return undefined;
const match = toolResultString.match(EXIT_CODE_TAIL_REGEX);
if (!match) return undefined;
return {
code: Number.parseInt(match[1], 10),
rawText: match[0],
timedOut: match[0].includes('exit due to timed out')
};
}
/**
* Returns true when the supplied rendered line equals (trimmed) the
* trailing exit-code text. Used by the renderer to drop the duplicated
* representation (since the trailing line is replaced by a status badge).
*/
export function isExitCodeSummaryLine(
lineText: string,
status: ExecShellExitStatus | undefined
): boolean {
if (!status) return false;
return lineText.trim() === status.rawText.trim();
}
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