Instructions to use remiai3/RemiAI_Framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use remiai3/RemiAI_Framework with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="remiai3/RemiAI_Framework", filename="engine/model.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use remiai3/RemiAI_Framework with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf remiai3/RemiAI_Framework # Run inference directly in the terminal: llama-cli -hf remiai3/RemiAI_Framework
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf remiai3/RemiAI_Framework # Run inference directly in the terminal: llama-cli -hf remiai3/RemiAI_Framework
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf remiai3/RemiAI_Framework # Run inference directly in the terminal: ./llama-cli -hf remiai3/RemiAI_Framework
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf remiai3/RemiAI_Framework # Run inference directly in the terminal: ./build/bin/llama-cli -hf remiai3/RemiAI_Framework
Use Docker
docker model run hf.co/remiai3/RemiAI_Framework
- LM Studio
- Jan
- vLLM
How to use remiai3/RemiAI_Framework with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "remiai3/RemiAI_Framework" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "remiai3/RemiAI_Framework", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/remiai3/RemiAI_Framework
- Ollama
How to use remiai3/RemiAI_Framework with Ollama:
ollama run hf.co/remiai3/RemiAI_Framework
- Unsloth Studio new
How to use remiai3/RemiAI_Framework with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for remiai3/RemiAI_Framework to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for remiai3/RemiAI_Framework to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for remiai3/RemiAI_Framework to start chatting
- Docker Model Runner
How to use remiai3/RemiAI_Framework with Docker Model Runner:
docker model run hf.co/remiai3/RemiAI_Framework
- Lemonade
How to use remiai3/RemiAI_Framework with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull remiai3/RemiAI_Framework
Run and chat with the model
lemonade run user.RemiAI_Framework-{{QUANT_TAG}}List all available models
lemonade list
File size: 4,899 Bytes
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const path = require('path');
const { spawn, exec } = require('child_process');
const fs = require('fs');
const os = require('os');
const si = require('systeminformation');
let mainWindow;
let apiProcess;
// Empty function to prevent crashes since logging is disabled
function logToDesktop(message) { }
function createWindow() {
mainWindow = new BrowserWindow({
width: 1300,
height: 900,
backgroundColor: '#ffffff',
icon: path.join(__dirname, 'remiai.ico'),
webPreferences: {
nodeIntegration: true,
contextIsolation: false,
webviewTag: true // RESTORED: This allows your browsing feature to work
},
autoHideMenuBar: true,
title: "RemiAI - bujji"
});
mainWindow.loadFile('index.html');
// --- FEATURE: TASK REMINDER SYSTEM ---
mainWindow.webContents.on('did-finish-load', () => {
mainWindow.webContents.send('check-tasks');
});
powerMonitor.on('resume', () => {
if (mainWindow) mainWindow.webContents.send('check-tasks');
});
powerMonitor.on('unlock-screen', () => {
if (mainWindow) mainWindow.webContents.send('check-tasks');
});
// ALLOW INTERNAL NAVIGATION
mainWindow.webContents.setWindowOpenHandler(({ url }) => {
return { action: 'allow' };
});
mainWindow.on('closed', function () { mainWindow = null; });
}
// --- AI ENGINE BACKEND LOGIC (STRICT CPU ONLY) ---
async function selectEngine() {
try {
const cpuFlags = await si.cpuFlags();
const flagsStr = JSON.stringify(cpuFlags).toLowerCase();
const basePath = app.isPackaged ? process.resourcesPath : __dirname;
const engineBaseDir = path.join(basePath, 'engine');
const hasFolder = (f) => fs.existsSync(path.join(engineBaseDir, f));
// 1. Check for AVX2 (Priority for speed)
if (flagsStr.includes('avx2') && hasFolder('cpu_avx2')) {
return 'cpu_avx2';
}
// 2. Fallback to standard AVX
return 'cpu_avx';
} catch (e) {
return 'cpu_avx';
}
}
async function startNativeBackend() {
killProcess();
await new Promise(resolve => setTimeout(resolve, 500));
const engineSubfolder = await selectEngine();
const basePath = app.isPackaged ? process.resourcesPath : __dirname;
const workingDir = path.join(basePath, 'engine', engineSubfolder);
let exeName = fs.existsSync(path.join(workingDir, 'bujji_engine.exe'))
? 'bujji_engine.exe'
: 'llama-server.exe';
const exePath = path.join(workingDir, exeName);
// --- CHECK FOR GIT LFS POINTERS ---
try {
const stats = fs.statSync(exePath);
if (stats.size < 5000) { // Real engine is >3MB. Pointers are ~130 bytes.
dialog.showErrorBox(
"RemiAI Engine Missing",
`The engine executable is a Git LFS pointer (size: ${stats.size} bytes), not the actual file.\n\nPlease install Git LFS and run: 'git lfs pull'\nOr redownload the 'engine' folder contents correctly.`
);
return; // Stop execution
}
} catch (err) {
logToDesktop("Error checking engine file: " + err.message);
}
// --- SPEED & CPU CONTROL ---
// Your i5 has 8 logical threads.
// Setting this to 4 uses 50% of your CPU capacity, ensuring fast 3-4s responses.
const optimizedThreads = 4;
const args = [
'-m', '../model.gguf',
'-c', '2048', // Keeps memory usage low and response snappy
'--batch-size', '512', // Increases prompt processing speed
'--port', '5000',
'-t', optimizedThreads.toString(),
'--n-gpu-layers', '0', // Forced CPU only
'--no-mmap' // Pre-loads model into RAM for zero lag
];
try {
apiProcess = spawn(exePath, args, {
cwd: workingDir,
windowsHide: true,
stdio: ['ignore', 'pipe', 'pipe']
});
apiProcess.stderr.on('data', (data) => logToDesktop(data.toString()));
} catch (e) {
logToDesktop(`Catch Error: ${e.message}`);
}
}
function killProcess() {
try {
exec('taskkill /IM bujji_engine.exe /F /T');
exec('taskkill /IM llama-server.exe /F /T');
} catch (e) { }
}
ipcMain.on('restart-brain', () => {
startNativeBackend();
if (mainWindow) mainWindow.webContents.send('brain-restarted');
});
ipcMain.on('reload-window', () => {
if (mainWindow) mainWindow.reload();
});
// --- APP LIFECYCLE ---
app.whenReady().then(() => {
startNativeBackend();
createWindow();
});
app.on('window-all-closed', () => {
killProcess();
if (process.platform !== 'darwin') app.quit();
});
app.on('will-quit', () => { killProcess(); }); |