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: 1,236 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 | import { BUILD_CONFIG } from '../src/lib/constants/pwa.constants';
import { existsSync, writeFileSync } from 'node:fs';
import { resolve } from 'path';
import type { Plugin } from 'vite';
let processed = false;
const OUTPUT_DIR = process.env.LLAMA_UI_OUT_DIR ?? BUILD_CONFIG.OUTPUT_DIR;
/**
* Write build.json with the llama.cpp release build number.
*
* LLAMA_BUILD_NUMBER is passed from CMake -> npm -> vite via env var.
* Used for display of the current llama-server release (e.g. "b1234").
*/
export function buildInfoPlugin(): Plugin {
return {
apply: 'build',
closeBundle() {
setTimeout(() => {
try {
if (processed) return;
processed = true;
const buildNumber = process.env.LLAMA_BUILD_NUMBER || 'b0000';
const outDir = resolve(OUTPUT_DIR);
const indexPath = resolve(outDir, 'index.html');
if (!existsSync(indexPath)) return;
const buildJsonPath = resolve(outDir, 'build.json');
writeFileSync(buildJsonPath, JSON.stringify({ version: buildNumber }), 'utf-8');
console.log(`Created build.json (version: ${buildNumber})`);
} catch (error) {
console.error('Failed to write build.json:', error);
}
}, 100);
},
name: 'llamacpp:build-info'
};
}
|