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: 1,625 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 | import { build } from 'esbuild';
import { dirname, resolve } from 'path';
import { fileURLToPath } from 'url';
import type { Plugin } from 'vite';
const __dirname = dirname(fileURLToPath(import.meta.url));
const VENDORS_DIR = resolve(__dirname, '../src/lib/vendors');
const VIRTUAL_ID = 'virtual:nerdamer';
const RESOLVED_ID = '\0' + VIRTUAL_ID;
/**
* Bundle the vendored nerdamer-prime source into a minified IIFE string,
* exposed as the `virtual:nerdamer` module. Flags mirror the upstream
* build (esbuild --bundle --minify --format=iife --global-name=nerdamer),
* so only human readable source lives in the repo and minification is a
* build artifact. Vendored under src/lib/vendors/, upstream snapshot:
* https://github.com/together-science/nerdamer-prime/commit/1936145f8af306ec0d883b9bfd7730aedd175c24
*/
export function nerdamerPlugin(): Plugin {
let bundled: string | null = null;
return {
async load(id) {
if (id !== RESOLVED_ID) return undefined;
if (bundled === null) {
const result = await build({
alias: {
'big-integer': resolve(VENDORS_DIR, 'big-integer/BigInteger.js'),
'decimal.js': resolve(VENDORS_DIR, 'decimal.js/decimal.js')
},
bundle: true,
entryPoints: [resolve(VENDORS_DIR, 'nerdamer-prime/all.js')],
format: 'iife',
globalName: 'nerdamer',
logLevel: 'silent',
minify: true,
write: false
});
bundled = result.outputFiles[0].text;
}
return `export default ${JSON.stringify(bundled)};`;
},
name: 'llamacpp:nerdamer',
resolveId(id) {
return id === VIRTUAL_ID ? RESOLVED_ID : undefined;
}
};
}
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