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from human_eval.data import write_jsonl, read_problems
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

problems = read_problems()

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x7B-v0.1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "merged_thing",
    torch_dtype=torch.bfloat16,

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base_model: openlm-research/open_llama_3b_v2
base_model_config: openlm-research/open_llama_3b_v2
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
push_dataset_to_hub:
datasets:
  - path: data1.json

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@override
  Widget build(BuildContext context) {
    return Scaffold(
      body: Center(
      child: Column(
        mainAxisAlignment: MainAxisAlignment.center,
        children: <Widget>[ 
        imageURI == null
          ? Text('No image selected.')
          : Image.file(imageURI, width: 300, height: 200, fit: BoxFit.cover),

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class MyApp extends StatelessWidget {

  @override
  Widget build(BuildContext context) {
    return MaterialApp(
        home: Scaffold(
           appBar: AppBar(
             title: Text('TFLite Example'),
             backgroundColor: Colors.transparent
             ),

sahil280114

import 'package:flutter/material.dart';
import 'package:image_picker/image_picker.dart';
import 'dart:io';
import 'package:tflite/tflite.dart';

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extension UIColor {
    public convenience init?(hex: String) {
        let r, g, b, a: CGFloat

        if hex.hasPrefix("#") {
            let start = hex.index(hex.startIndex, offsetBy: 1)
            let hexColor = String(hex[start...])

            if hexColor.count == 8 {
                let scanner = Scanner(string: hexColor)

sahil280114

coreml_model = coremltools.converters.sklearn.convert(model)
coreml_model.author = 'Your Name'
coreml_model.license = 'MIT'
coreml_model.short_description = 'Sentiment polarity LinearSVC.'
coreml_model.input_description['input'] = 'Features extracted from the text.'
coreml_model.output_description['classLabel'] = 'The most likely polarity (positive or negative), for the given input.'
coreml_model.output_description['classProbability'] = 'The probabilities for each class label, for the given input.'
coreml_model.save('SentimentPolarity.mlmodel')

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features = np.vectorize(features)

X = features(reviews[:, 1])

y = reviews[:, 0]

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clf = Pipeline([("dct", DictVectorizer()), ("svc", LinearSVC())])
params = {
    "svc__C": [1e15, 1e13, 1e11, 1e9, 1e7, 1e5, 1e3, 1e1, 1e-1, 1e-3, 1e-5]
}
gs = GridSearchCV(clf, params, cv=10, verbose=2, n_jobs=-1)
gs.fit(X, y)
model = gs.best_estimator_

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def features(sentence):
    stop_words = stopwords.words('english') + list(punctuation)
    words = word_tokenize(sentence)
    words = [w.lower() for w in words]
    filtered = [w for w in words if w not in stop_words and not w.isdigit()]
    words = {}
    for word in filtered:
        if word in words:
            words[word] += 1.0
        else: