rshemet

import 'package:cactus/cactus.dart';

final vlm = await CactusVLM.init(
  modelUrl: 'huggingface/gguf/vision-model/link', // URL to VLM GGUF
  mmprojUrl: 'huggingface/gguf/mmproj/link',   // URL to mmproj GGUF
);

final messages = [
  ChatMessage(role: 'user', content: 'Describe what you see in this image.')
];

rshemet

import 'package:cactus/cactus.dart';

// pass in conversation history
final messages = [
  ChatMessage(role: 'system', content: 'You are a helpful assistant.'),
  ChatMessage(role: 'user', content: 'What is the capital of France?')
];

// run inference (lm is your previously initialized model)
final response = await lm.completion(

rshemet

import 'package:cactus/cactus.dart';

final lm = await CactusLM.init(
  modelUrl: 'https://huggingface.co/Cactus-Compute/Qwen3-600m-Instruct-GGUF/resolve/main/Qwen3-0.6B-Q8_0.gguf',
  contextSize: 2048, // The context size for the model.
);

rshemet

import { CactusVLM } from 'cactus-react-native';

// Initialize the Vision Language Model.
const { vlm, error } = await CactusVLM.init({
    model: '/path/to/vision-model.gguf',
    mmproj: '/path/to/mmproj.gguf', // The projector file for the vision model.
});

if (error) {
  console.error('Failed to initialize VLM:', error);

rshemet

import { CactusLM } from 'cactus-react-native';

// Initialize the model. The localPath is the path to your previously downloaded model.
const { lm, error } = await CactusLM.init({
  model: localPath,
  n_ctx: 2048, // context window for your model
});

if (error) {
  console.error('Failed to initialize model:', error);

rshemet

import { CactusLM } from 'cactus-react-native';

// pass in conversation history
const messages = [
  { role: 'system', content: 'You are a helpful assistant.' },
  { role: 'user', content: 'What is the capital of France?' }
];

// define inference parameters
const params = { 

rshemet

import RNFS from 'react-native-fs';

const modelUrl = 'https://huggingface.co/Cactus-Compute/Qwen3-600m-Instruct-GGUF/resolve/main/Qwen3-0.6B-Q8_0.gguf';
const localPath = `${RNFS.DocumentDirectoryPath}/${modelUrl.split('/').pop()}`;

// Download the model and log progress
await RNFS.downloadFile({
  fromUrl: modelUrl,
  toFile: localPath,
  progress: res => console.log(`${(res.bytesWritten / res.contentLength * 100).toFixed(2)}%`),

rshemet

npm install cactus-react-native && npx pod-install

rshemet

if __name__ == '__main__':
    x = np.arange(50)[:, np.newaxis]
    y = np.array([i + np.random.rand() for i in range(50)])[:, np.newaxis]
    ols_test = Model(x, y)
    ols_test.fit(intercept=True)
    ols_test.predict()
    ols_test.plot_predictions()

rshemet

    def plot_predictions(self):
        plt.scatter(self.x, self.y, c='orange', label = 'Observed values')
        plt.plot(self.x, self.y_hat, label = 'Fitted values')
        plt.legend()
        title_label = 'Fitted OLS Regression: intercept={}, slope={}'\
            .format(np.round(self.betas[0][0],2), np.round(self.betas[1][0],2))
        plt.title(label=title_label)
        plt.show()