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Motorola’s new Moto Buds Plus offer Bose quality ANC and tuning for a budget price

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Motorola has launched two new wireless earbuds that bring Bose’s audio tuning and best-in-class active noise cancellation for very competitive prices. It’s calling the new earbuds: the Moto Buds and Moto Buds Plus. 

The Moto Buds Plus are the more attention-grabbing earbuds of the two, as they sport some impressive features that you’d find in the best wireless earbuds for a budget price of just £129 (roughly $160 and AU$250 but we have yet to get pricing for other regions). There’s hi-res audio support along with active noise cancellation and Dolby Atmos with Head Tracking tech to provide dynamic directional audio when listening to compatible continents.  

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How to fine tuning Mixtral open source AI model

How to fine tuning Mixtral open source AI model

In the rapidly evolving world of artificial intelligence (AI), a new AI model has emerged that is capturing the attention of developers and researchers alike. Known as Mixtral, this open-source AI model is making waves with its unique approach to machine learning. Mixtral is built on the mixture of experts (MoE) model, which is similar to the technology used in OpenAI’s GPT-4. This guide will explore how Mixtral works, its applications, and how it can be fine-tuned and integrated with other AI tools to enhance machine learning projects.

Mixtral 8x7B, a high-quality sparse mixture of experts model (SMoE) with open weights. Licensed under Apache 2.0. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference.

At the heart of Mixtral is the MoE model, which is a departure from traditional neural networks. Instead of using a single network, Mixtral employs a collection of ‘expert’ networks, each specialized in handling different types of data. A gating mechanism is responsible for directing the input to the most suitable expert, which optimizes the model’s performance. This allows for faster and more accurate processing of information, making Mixtral a valuable tool for those looking to improve their AI systems.

One of the key features of Mixtral is its use of the Transformer architecture, which is known for its effectiveness with sequential data. What sets Mixtral apart is the incorporation of MoE layers within the Transformer framework. These layers function as experts, enabling the model to address complex tasks by leveraging the strengths of each layer. This innovative design allows Mixtral to handle intricate problems with greater precision.

How to fine tuning Mixtral

For those looking to implement Mixtral, RunPod offers a user-friendly template that simplifies the process of performing inference. This template makes it easier to call functions and manage parallel requests, which streamlines the user experience. This means that developers can focus on the more creative aspects of their projects, rather than getting bogged down with technical details. Check out the fine tuning tutorial kindly created by Trelis Research  to learn more about how you can find tune Mixtral and more.

Here are some other articles you may find of interest on the subject of Mixtral and Mistral AI :

Customizing Mixtral to meet specific needs is a process known as fine-tuning. This involves adjusting the model’s parameters to better fit the data you’re working with. A critical part of this process is the modification of attention layers, which help the model focus on the most relevant parts of the input. Fine-tuning is an essential step for those who want to maximize the effectiveness of their Mixtral model.

Looking ahead, the future seems bright for MoE models like Mixtral. There is an expectation that these models will be integrated into a variety of mainstream AI packages and tools. This integration will enable a broader range of developers to take advantage of the benefits that MoE models offer. For example, MoE models can manage large sets of parameters with greater efficiency, as seen in the Mixtral 8X 7B instruct model.

The technical aspects of Mixtral, such as the router and gating mechanism, play a crucial role in the model’s efficiency. These components determine which expert should handle each piece of input, ensuring that computational resources are used optimally. This strategic balance between the size of the model and its efficiency is a defining characteristic of the MoE approach. Mixtral has the following capabilities.

  • It gracefully handles a context of 32k tokens.
  • It handles English, French, Italian, German and Spanish.
  • It shows strong performance in code generation.
  • It can be finetuned into an instruction-following model that achieves a score of 8.3 on MT-Bench.

Another important feature of Mixtral is the ability to create an API for scalable inference. This API can handle multiple requests at once, which is essential for applications that require quick responses or need to process large amounts of data simultaneously. The scalability of Mixtral’s API makes it a powerful tool for those looking to expand their AI solutions.

Once you have fine-tuned your Mixtral model, it’s important to preserve it for future use. Saving and uploading the model to platforms like Hugging Face allows you to share your work with the AI community and access it whenever needed. This not only benefits your own projects but also contributes to the collective knowledge and resources available to AI developers.

Mixtral’s open-source AI model represents a significant advancement in the field of machine learning. By utilizing the MoE architecture, users can achieve superior results with enhanced computational efficiency. Whether you’re an experienced AI professional or just starting out, Mixtral offers a robust set of tools ready to tackle complex machine learning challenges. With its powerful capabilities and ease of integration, Mixtral is poised to become a go-to resource for those looking to push the boundaries of what AI can do.

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7 Midjourney tips and tricks for style tuning

7 Midjourney tips and tricks for the best AI art

Recently Midjourney released a new feature that allows users to quickly and easily create unique styles which they can use on new AI art creations. The Midjourney style tuning process allows you to easily create new artwork offering anywhere between 32 to 256 variations.

These Midjourney styles can then be saved to be used at a later date to recreate similar imagery or shared with others. Once you’ve found the perfect style that vibes with your vision, getting your masterpiece out of the tool and into the world is a breeze. Just grab the job ID from the image link, and you’re all set to sprinkle your digital art across any project you like.

With Midjourney, you can add a seed number to your prompt, and just like that, you’re not just creating an image—you’re crafting a one-of-a-kind piece that’s as unique as your fingerprint. This seed number is like a secret sauce that ensures no one else can replicate your artwork. It’s all yours, and that’s pretty special.

Mixing styles is where things get really fun. Think of style codes as the spices in your creative kitchen. Blend different codes together, and you create a unique flavor that makes your art stand out. And if you’re feeling adventurous, throw in a “– -style random” command to add a dash of surprise to your work. Who knows what amazing styles you’ll discover?

Midjourney style tuning

Check out the excellent Midjourney user guide below created by Future Tech Pilot which features a number of useful ideas on how you can streamline your Midjourney workflow and create fantastic AI artwork.

  • Midjourney’s style tuning feature allows users to create a wide range of styles, from 32 up to 256 options, for their creative projects.
  • Users can access and download individual images from the style tuning grid by using the job ID found in the image link.
  • The seed number provided with the prompt does not guarantee the recreation of the same set of images, indicating that the images generated are unique and cannot be exactly replicated.
  • Style codes can be combined to create unique art by linking multiple codes with a hyphen, resulting in a blend of the chosen styles.
  • Users can generate completely random style codes by using the command “- -style random,” which can be further customized by linking several random styles or specifying the length and percentage of selections from the tuning quiz.
  •  The style tuning feature includes a “repeat” option that allows users to generate multiple iterations of a style, with a maximum limit of 40 repeats.
  • Users can recover the tuning test that created a specific style code by entering the code into a designated URL, but this does not work with randomly generated codes.
  • A community-created style decoder website can decode random style codes to reveal the instructions on how they were made, providing insight into the creation process.
  • The “sticky style” setting enables users to apply their last used style code to all future prompts automatically, saving time for those who wish to consistently use a particular style.
  • When creating a tuning quiz, users can use multi-prompts to exclude specific elements (e.g., colors) by assigning negative values, as the “no” parameter is not recognized within tuning.

Perfection doesn’t come easy, but with the “repeat” feature, you’re on your way there. This handy tool lets you generate up to 40 variations of a style, giving you a whole array of subtle tweaks to choose from.

Ever wonder where a certain style came from? With the style code recovery function, you can trace the steps back to the original tuning test of any style code. But remember, this doesn’t work for those random styles. For those, you might need a little help from a community-developed style decoder to crack the code of your mysterious creations. Here are some other articles you may find of interest on the subject of AI art.

AI artwork consistency

Consistency can be just as important as creativity. That’s where “sticky style” comes in. This feature locks in your chosen style code for all your prompts, making it a breeze to maintain a cohesive look across your artwork. It’s like having a signature style that everyone recognizes as uniquely you.

When you’re fine-tuning your art, control is key. With multi-prompts in tuning quizzes, you can get super specific by excluding elements you don’t want, just by assigning negative values. This level of detail ensures that your final piece is exactly as you envisioned, without any unwanted surprises.

Collaboration is a big part of the creative process, and Midjourney makes it easy. Turn on the “embeds and link previews” option in your settings, and you’ll be able to share your tuning quiz links directly in chat. This keeps everyone on the same page and makes teamwork a whole lot smoother.

So there you have it. Midjourney’s style tuning isn’t just a tool; it’s a gateway to a world where your digital art can truly shine. By getting to grips with its features, from picking the perfect style to crafting images that are unmistakably yours, you’ll be able to create art that doesn’t just turn heads—it speaks with your voice. Dive in, experiment, and watch as your AI digital artistry reaches heights you never thought possible.

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How to create a Midjourney Style Tuning workflow

How to create a Midjourney Style Tuning workflow

If you are interested in learning more about the new Style features added to the Midjourney AI art generator this month. This guide will take you through the basics of creating a workflow to start using the new Style Tuning in Midjourney in your AI art creation. The first step in this process is to consider the aspect ratio when tuning a prompt. The aspect ratio, a term that refers to the dimensions of visual content, plays a significant role in shaping the overall aesthetics of your output. It’s a balance between the width and height of your visual content, and adjusting it can significantly alter the visual appeal. Understanding the impact of aspect ratio on the aesthetics of your project is key to achieving the desired visual effect.

What is Midjourney Style Tuning?

  • The Style Tuner lets you make your own Midjourney style
  • Even though we call it a “style” it’s a bit more than that, it’s really controlling the “personality” of the model and it influences everything from colors to character details
  • You can optimize specifically around a single prompt or setting (including non-square aspect ratios, image + text prompts and raw mode)

Default or RAW mode?

Next, you’ll need to choose between the default mode and raw mode. The default mode is the standard setting for operations, offering a pre-set configuration that’s generally suitable for a wide range of tasks. On the other hand, raw mode provides an unprocessed setting, giving you more flexibility and customization options. Both modes have their unique advantages, but for those who are new to the platform, it’s advisable to start with the default mode.

Adding Midjourney Styles to your workflow

Other Midjourney articles, guides and news you may find of interest and help you refine your AI art creation process:

The third step involves selecting a style from the 32 available options. These styles, also known as ‘style directions’, provide visual guidance for your project. After selecting a style, you can test it by copying the entire prompt and pasting it into Midjourney. This step allows you to preview how your chosen style will look in practice, giving you a tangible sense of the aesthetic direction of your project.

Then, you’ll need to decide whether to delete the words in the prompt that created the style. This decision is largely a matter of personal preference, and it’s recommended to experiment with both options to determine which one best suits your project’s needs.

The fifth step involves enabling ‘fast hours’, a speed optimization feature that can significantly speed up the processing time of your project. To activate this feature, you’ll need to place squiggly brackets around the words used to create the style code. This simple action can greatly enhance the efficiency of your project’s processing time.

Creating a portfolio of Midjourney styles to use and share

Once you’ve found a style that aligns with your aesthetic vision, you can save it as a shortcut. This quick access method allows you to easily apply the same style to future projects, saving you valuable time and effort in the long run.

If the style code doesn’t produce the desired effect, you can try adjusting the stylized values. These values represent the degree of visual influence each style has on your project. By fine-tuning these values, you can further refine the aesthetics of your output, ensuring that it aligns perfectly with your vision.

Introducing some ‘chaos’ into your project can add variety to the generated grid. Chaos, in this context, refers to the randomness in output, which can introduce an element of unpredictability and uniqueness to your project, making it more dynamic and engaging.

Finally, if you’re not entirely satisfied with the results, you have the option to change the style from default to raw after the fact. This flexibility allows you to experiment with different settings and configurations, giving you greater control over the final output.

Style tuning in Midjourney is a complex but rewarding process. By understanding and effectively utilizing the various elements involved, you can create visually stunning projects that truly stand out. If you found this guide helpful, remember to subscribe and hit the like button, and feel free to share your own tips and experiences in the comments.

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How to automate fine tuning ChatGPT 3.5 Turbo

How to automate fine tuning ChatGPT 3.5 Turbo

The advent of AI and machine learning has transform the wide variety of different areas, including the field of natural language processing. One of the most significant advancements in this area is the development and release of ChatGPT 3.5 Turbo, a language model developed by OpenAI. In this guide will delve into the process of automating the fine-tuning of GPT 3.5 Turbo for function calling using Python, with a particular focus on the use of the Llama Index.

OpenAI has announced the availability of fine-tuning for its GPT-3.5 Turbo model back in August 2023, with support for GPT-4 expected to be released this fall. This new feature allows developers to customize language models to better suit their specific needs, offering enhanced performance and functionality. Notably, early tests have shown that a fine-tuned version of GPT-3.5 Turbo can match or even outperform the base GPT-4 model in specialized tasks. In terms of data privacy, OpenAI ensures that all data sent to and from the fine-tuning API remains the property of the customer. This means that the data is not used by OpenAI or any other organization to train other models.

One of the key advantages of fine-tuning is improved steerability. Developers can make the model follow specific instructions more effectively. For example, the model can be fine-tuned to always respond in a particular language, such as German, when prompted to do so. Another benefit is the consistency in output formatting, which is essential for applications that require a specific response format, like code completion or generating API calls. Developers can fine-tune the model to reliably generate high-quality JSON snippets based on user prompts.

How to automate fine tuning ChatGPT

The automation of fine-tuning GPT 3.5 Turbo involves a series of steps, starting with the generation of data classes and examples. This process is tailored to the user’s specific use case, ensuring that the resulting function description and fine-tuned model are fit for purpose. The generation of data classes and examples is facilitated by a Python file, which forms the first part of a six-file sequence.

Fine-tuning also allows for greater customization in terms of the tone of the model’s output, enabling it to better align with a business’s unique brand identity. In addition to these performance improvements, fine-tuning also brings efficiency gains. For instance, businesses can reduce the size of their prompts without losing out on performance. The fine-tuned GPT-3.5 Turbo models can handle up to 4k tokens, which is double the capacity of previous fine-tuned models. This increased capacity has the potential to significantly speed up API calls and reduce costs.

Other articles you may find of interest on the subject of ChatGPT 3.5 Turbo :

The second file in the sequence leverages the Llama Index, a powerful tool that automates several processes. The Llama Index generates a fine-tuning dataset based on the list produced by the first file. This dataset is crucial for the subsequent fine-tuning of the GPT 3.5 Turbo model. The next step in the sequence extracts the function definition from the generated examples. This step is vital for making calls to the fine-tuned model. Without the function definition, the model would not be able to process queries effectively.

The process then again utilizes the Llama Index, this time to fine-tune the GPT 3.5 Turbo model using the generated dataset. The fine-tuning process can be monitored from the Python development environment or from the OpenAI Playground, providing users with flexibility and control over the process.

Fine tuning ChatGPT 3.5 Turbo

Once the model has been fine-tuned, it can be used to make regular calls to GPT-4, provided the function definition is included in the call. This capability allows the model to be used in a wide range of applications, from answering complex queries to generating human-like text.

The code files for this project are available on the presenter’s Patreon page, providing users with the resources they need to automate the fine-tuning of GPT 3.5 Turbo for their specific use cases. The presenter’s website also offers a wealth of information, with a comprehensive library of videos that can be browsed and searched for additional guidance.

Fine-tuning is most effective when integrated with other techniques such as prompt engineering, information retrieval, and function calling. OpenAI has also indicated that it will extend support for fine-tuning with function calling and a 16k-token version of GPT-3.5 Turbo later this fall. Overall, the fine-tuning update for GPT-3.5 Turbo offers a versatile and robust set of features for developers seeking to tailor the model for specialized tasks. With the upcoming capability to fine-tune GPT-4 models, the scope for creating highly customized and efficient language models is set to expand even further.

The automation of fine-tuning GPT 3.5 Turbo for function calling using Python and the Llama Index is a complex but achievable process. By generating data classes and examples tailored to the user’s use case, leveraging the Llama Index to automate processes, and carefully extracting function definitions, users can create a fine-tuned model capable of making regular calls to GPT-4. This process, while intricate, offers significant benefits, enabling users to harness the power of GPT 3.5 Turbo for a wide range of applications.

Further articles you may find of interest on fine tuning large language models :

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