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: 1,646 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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | // An `@` starts a mention only when preceded by start-of-string or one of
// these; identifier chars are not delimiters, so a mid-word `@` does not.
const TOKEN_BOUNDARY_CHARS = new Set([
' ',
'\t',
'\n',
'\r',
'(',
')',
'[',
']',
',',
';',
':',
'"',
"'"
]);
/**
* Find the most-recent `@`-mention token whose extent includes `cursor`;
* the query covers the whole `@...` token regardless of caret position.
*/
export function findMentionToken(
value: string,
cursor: number
): { start: number; end: number; query: string } | null {
if (cursor <= 0 || cursor > value.length) return null;
let atIndex = -1;
for (let i = cursor - 1; i >= 0; i--) {
const ch = value[i];
if (ch === '@') {
const prev = i > 0 ? value[i - 1] : '';
if (i === 0 || TOKEN_BOUNDARY_CHARS.has(prev)) {
atIndex = i;
}
break;
}
if (TOKEN_BOUNDARY_CHARS.has(ch)) break;
}
if (atIndex === -1) return null;
let end = atIndex + 1;
while (end < value.length && !TOKEN_BOUNDARY_CHARS.has(value[end])) {
end++;
}
return {
end,
query: value.slice(atIndex + 1, end),
start: atIndex
};
}
/**
* Stable signature of a mention token for use as a "dismissed" marker:
* while the picker is closed and this exact token is still intact, the
* picker does not silently re-open on in-token edits.
*/
export interface MentionDismissSnapshot {
start: number;
query: string;
}
export function takeMentionDismissSnapshot(
value: string,
cursor: number
): MentionDismissSnapshot | null {
const token = findMentionToken(value, cursor);
if (!token) return null;
return { query: token.query, start: token.start };
}
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