I want to press the power button on my computer, step away, and return to find the desktop screen waiting for me, tea in hand. Currently, I have to navigate through multiple screens before reaching this point, with the last one displaying my single user account. Can someone offer advice on how to streamline this process?

​I want to press the power button on my computer, step away, and return to find the desktop screen waiting for me, tea in hand. Currently, I have to navigate through multiple screens before reaching this point, with the last one displaying my single user account. Can someone offer advice on how to streamline this process?  Read More

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Hi,

I am trying to create a production schedule in MS-Project and as of now it appears to be structured in  manner can be implemented at shop floor. I am using Project-3 plan. as of now I am trying to manage this with print of schedules.

My question is, I want to share the scheduled plans with users on shop floor, give them access to complete the task and complete etc. So that actual tasks are in sync with MS-Project. 

There will be around 20 machines of which I would like the task updates get to entered. How I can accomplish this goal.

All support greatly appreciated.

​Hi, I am trying to create a production schedule in MS-Project and as of now it appears to be structured in  manner can be implemented at shop floor. I am using Project-3 plan. as of now I am trying to manage this with print of schedules. My question is, I want to share the scheduled plans with users on shop floor, give them access to complete the task and complete etc. So that actual tasks are in sync with MS-Project.  There will be around 20 machines of which I would like the task updates get to entered. How I can accomplish this goal. All support greatly appreciated.    Read More

​

I’m preparing some learning materials recently, and I need to download the teaching videos on YouTube to my Windows 11 computer for offline learning. But I don’t want to open a YouTube membership, after all, the price is too high and not cost-effective. I have tried some online downloading tools before, but the video clarity is low, and some tools are even full of ads, which particularly affects the experience. So now I want to ask everyone, is there any good method that can stably download YouTube videos to computer Windows 11, and the clarity can be guaranteed? I really need your recommendation, thank you!

​I’m preparing some learning materials recently, and I need to download the teaching videos on YouTube to my Windows 11 computer for offline learning. But I don’t want to open a YouTube membership, after all, the price is too high and not cost-effective. I have tried some online downloading tools before, but the video clarity is low, and some tools are even full of ads, which particularly affects the experience. So now I want to ask everyone, is there any good method that can stably download YouTube videos to computer Windows 11, and the clarity can be guaranteed? I really need your recommendation, thank you!  Read More

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Купив підписку в офіс для корисиування в access, а воно не працює і знову просить підписку купляв для особистого користування підписку

​Купив підписку в офіс для корисиування в access, а воно не працює і знову просить підписку купляв для особистого користування підписку   Read More

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就这样，她在2009年赛季中获得七项世界杯冠军，包括速降项目的全部五项赛事。她还连续三个赛季夺得世界杯总冠军。然而就在2010年2月12日开始的温哥华冬奥会开幕前夕，她在奥地利的一次训练中右腿胫骨受伤，整整一个星期不能训练。这让她的心理和身体都承受了巨大的压力和痛苦。高山滑雪的速度高达120公里每小时，相当于一辆汽车在高速公路上奔驰，这样的高速对运动员的胫骨冲击非常大，赛前曾一度传出这位夺冠热门要退赛的消息。而冬奥会赛场上这条从起点到终点落差770米、全长2939米，号称世界难度最大的赛道让那些怀揣着奥运梦想的运动员们望而却步。

​就这样，她在2009年赛季中获得七项世界杯冠军，包括速降项目的全部五项赛事。她还连续三个赛季夺得世界杯总冠军。然而就在2010年2月12日开始的温哥华冬奥会开幕前夕，她在奥地利的一次训练中右腿胫骨受伤，整整一个星期不能训练。这让她的心理和身体都承受了巨大的压力和痛苦。高山滑雪的速度高达120公里每小时，相当于一辆汽车在高速公路上奔驰，这样的高速对运动员的胫骨冲击非常大，赛前曾一度传出这位夺冠热门要退赛的消息。而冬奥会赛场上这条从起点到终点落差770米、全长2939米，号称世界难度最大的赛道让那些怀揣着奥运梦想的运动员们望而却步。  Read More

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一辆东风大卡车，在沪杭高速上追尾撞上了一辆厢式大货车。东风大卡车的整个车头凹了进去，司机扭曲在驾驶室里，他身上的鲜血像发动机里的机油一样往下流。东风大卡车后面，停着另一辆卡车，他们是朋友，他眼睁睁地看着朋友在驾驶室里呻吟、求救。

    消防队员赶来了，经过了三个小时，才把司机从驾驶室里抱出来，但司机已经死去多时了。司机的遗体被家人运走了。安葬后，司机的妻子带着五岁的儿子赶来问他一个问题。

​一辆东风大卡车，在沪杭高速上追尾撞上了一辆厢式大货车。东风大卡车的整个车头凹了进去，司机扭曲在驾驶室里，他身上的鲜血像发动机里的机油一样往下流。东风大卡车后面，停着另一辆卡车，他们是朋友，他眼睁睁地看着朋友在驾驶室里呻吟、求救。    消防队员赶来了，经过了三个小时，才把司机从驾驶室里抱出来，但司机已经死去多时了。司机的遗体被家人运走了。安葬后，司机的妻子带着五岁的儿子赶来问他一个问题。  Read More

​

　又一次考试后，孩子考了第十七名，而他的同桌还是第一名。回家后，儿子又问了同样的问题。她真想说，人的智力确实有三六九等，考第一的人，脑子就是比一般的人灵。然而这样的回答，难道是孩子真想知道的答案吗？她庆幸自己没说出口。

　　应该怎样回答儿子的问题呢？有几次，她真想重复那几句被上万个父母重复了上万次的话——你太贪玩了；你在学习上还不够勤奋；和别人比起来还不够努力……一次来搪塞儿子。然而，向象她儿子这样脑袋不够聪明，在班上成绩不甚突出的孩子，平时活得还不够辛苦吗？所以她没有那么做，她想为儿子的问题找到一个完美的答案。

​　又一次考试后，孩子考了第十七名，而他的同桌还是第一名。回家后，儿子又问了同样的问题。她真想说，人的智力确实有三六九等，考第一的人，脑子就是比一般的人灵。然而这样的回答，难道是孩子真想知道的答案吗？她庆幸自己没说出口。　　应该怎样回答儿子的问题呢？有几次，她真想重复那几句被上万个父母重复了上万次的话——你太贪玩了；你在学习上还不够勤奋；和别人比起来还不够努力……一次来搪塞儿子。然而，向象她儿子这样脑袋不够聪明，在班上成绩不甚突出的孩子，平时活得还不够辛苦吗？所以她没有那么做，她想为儿子的问题找到一个完美的答案。  Read More

​

需要遵循一定的步骤和策略，以确保能够准确、高效地找到目标人物。以下是一些建议，供您参考：
首先，了解华纳公司的基本情况是非常关键的。这包括公司的规模、行业地位、业务范围以及联系方式等。您可以通过华纳公司的官方网站、行业报告或相关新闻报道等途径获取这些信息。这有助于您更全面地了解公司的运营状况和总经理的职责范围。

​需要遵循一定的步骤和策略，以确保能够准确、高效地找到目标人物。以下是一些建议，供您参考：首先，了解华纳公司的基本情况是非常关键的。这包括公司的规模、行业地位、业务范围以及联系方式等。您可以通过华纳公司的官方网站、行业报告或相关新闻报道等途径获取这些信息。这有助于您更全面地了解公司的运营状况和总经理的职责范围。  Read More

​

Introduction

In the age of AI and machine learning, data is the key to training and fine-tuning models. However, gathering high-quality, diverse datasets can be challenging. Synthetic data generation offers a promising solution, but how do you ensure the data you’re creating is both valid and responsible?

This blog will explore the process of crafting responsible synthetic data, evaluating it, and using it for fine-tuning models. We’ll also dive into Azure AI’s RAFT distillation recipe, a novel approach to generating synthetic datasets using Meta’s Llama 3.1 model and UC Berkeley’s Gorilla project.

Understanding Synthetic Data for Fine-Tuning

What is synthetic data?

Synthetic data is artificially generated rather than collected from real-world events. It is used when gathering real data is expensive, time-consuming, or raises privacy concerns. For example, synthetic images, videos, or text can be generated to mimic real-world datasets.

Why synthetic data matters for fine-tuning:

Fine-tuning a machine learning model with real-world data is often limited by the availability of diverse, high-quality datasets. Synthetic data fills this gap by providing additional samples, augmenting the original dataset, or generating new, unseen scenarios. For instance, in AI models like GPT or image classification systems, fine-tuning with synthetic data helps models adapt to specialized tasks or environments.

Common use cases:

Natural Language Processing (NLP): Generating new text to help models better understand uncommon language structures.
Computer Vision: Creating synthetic images to train models for object detection, especially in rare or sensitive cases like medical imaging.
Robotics: Simulating environments for AI models to interact with, reducing the need for real-world testing.

What makes data “responsible”?

Synthetic data can exacerbate existing biases or create new ethical concerns. Creating responsible data ensures that datasets are fair, and representative, and do not introduce harmful consequences when used for fine-tuning AI models.

Key principles of responsible synthetic data include:

Fairness: Avoiding biases in race, gender, or other sensitive attributes.
Privacy: Ensuring that synthetic data does not leak sensitive information from real-world datasets.
Transparency: Ensuring that the origins and processing of the synthetic data are documented.

Quality aspects for validation:

Diversity: Does the data capture the range of possible real-world cases?
Relevance: Does the synthetic data match the domain and task for which it will be used?
Performance Impact: Does the use of synthetic data improve model performance without degrading fairness or accuracy?

 Validating Synthetic Data

Validation ensures that synthetic data meets the required quality and ethical standards before being used to fine-tune a model.

Techniques for validation:

Ground-truth comparison: If there’s real data available, compare the synthetic data with real-world datasets to see how closely they match.
Model-based validation: Fine-tune a model with both synthetic and real data, then test its performance on a validation dataset. If the synthetic data significantly improves the model’s accuracy or generalization capabilities, it’s considered valid.
Bias and fairness evaluation: Use fairness metrics (such as demographic parity or disparate impact) to check if the synthetic data introduces unintended biases. Several tools, like Microsoft Fair Learn or IBM’s AI Fairness 360, can help identify such issues.

Tools and methods for validation:

Azure Machine Learning offers built-in tools for data validation, including feature importance, explainability dashboards, and fairness assessments.
Open-source tools such as Google’s What-If Tool or IBM AI Fairness 360, can provide detailed reports on fairness and bias in your synthetic data.

The RAFT Distillation Recipe

The RAFT distillation recipe, available on GitHub, provides a method to generate high-quality synthetic datasets using Meta Llama 3.1 and UC Berkeley’s Gorilla project.

Introduction to RAFT

RAFT (Reinforcement Active Fine-Tuning) is a technique where a pre-trained model generates synthetic data, which is then used to fine-tune the same or a similar model. The goal is to create data that is relevant, diverse, and aligned with the task for which the model is being fine-tuned.

Meta Llama 3.1:

A powerful language model deployed on Azure AI. Using RAFT, Meta Llama generates synthetic data that can be used for NLP tasks, such as question answering, summarization, or classification.

UC Berkeley’s Gorilla Project:

The Gorilla project focuses on fine-tuning models for specific tasks using minimal data. By integrating the Gorilla project’s methods into RAFT, users can create a tailored dataset quickly and efficiently.

Steps from the RAFT distillation recipe:

Step 1: Deploy Meta Llama 3.1 on Azure AI using the provided instructions in the GitHub repo.
Step 2: Use RAFT distillation to generate synthetic datasets. This involves having the model generate relevant text or data samples based on input prompts.
Step 3: Evaluate the generated synthetic dataset using metrics such as relevance, diversity, and performance.
Step 4: Fine-tune the model using the generated synthetic data to improve performance on specific tasks.

The blog can include code snippets from the repo to show users how to set up RAFT on Azure, generate synthetic datasets, and fine-tune models.

To create a JSONL (JSON Lines) file for training models in Azure Machine Learning, follow these step-by-step instructions:

What is a JSONL File?

A JSONL file is a format where each line is a valid JSON object. It’s commonly used for machine learning tasks like fine-tuning models because it allows you to store structured data in a readable format.

Step-by-Step Guide to Creating a JSONL File

Step 1: Prepare Your Data

Identify the data you need for fine-tuning. For instance, if you’re fine-tuning a text model, your data may consist of input and output text pairs.
Each line in the file should be a JSON object. A typical structure might look like this:

Step 2: Use a Text Editor or Python Script

You can create a JSONL file using a text editor like Notepad or VS Code, or generate it programmatically with a script (e.g., in Python).

Method 1: Using a Text Editor

Open a plain text editor (like Notepad++ or Visual Studio Code).
Write each line as a valid JSON object, e.g.

 Save the file with a .jsonl extension (e.g., training_data.jsonl).

Method 2: Using Python Script You can also use Python to generate a JSONL file, especially if you have a large dataset.

Example Python code:

Step 3: Validate the JSON Format

Ensure that:

Each line in your file is a valid JSON object.
There are no commas between objects (unlike a JSON array).
Make sure that every object is properly enclosed in {}.

Step 4: Upload to Azure ML

Once your JSON file is ready:

Upload the file to your Azure Machine Learning workspace. You can do this from the Azure portal or via an SDK command.
Use the file for training or evaluation in the Azure ML pipeline, depending on your task (e.g., fine-tuning).

Step 5: Test the File

To verify the file, you can use a simple Python script to load and print the contents:

Example: JSONL File for Fine-Tuning (with 3 lines)

Summary of Steps:

Prepare your data in a structured JSON format.
Write each line as a separate JSON object in a text editor or using a Python script.
Save the file with a .jsonl extension.
Validate the format to ensure each line is a valid JSON object.
Upload to Azure Machine Learning for model training or fine-tuning.

By following these steps, you’ll have a valid JSONL file ready to be used in Azure Machine Learning for tasks such as model fine-tuning.

Resources

Azure Machine Learning documentation: https://learn.microsoft.com/azure/machine-learning/?view=azureml-api-2&viewFallbackFrom=azureml-api-2%3Fstudentamb_263805
Azure AI: https://azure.microsoft.com/solutions/ai/?studentamb_263805

​Microsoft Tech Community – Latest Blogs –Read More 

如果您需要联系公司或了解更具体的信息，建议直接访问该公司的官方网站或通过官方渠道进行咨询。不过，需要注意的是，根据我之前所提供的信息，

​如果您需要联系公司或了解更具体的信息，建议直接访问该公司的官方网站或通过官方渠道进行咨询。不过，需要注意的是，根据我之前所提供的信息，  Read More

​