ChatGPT-4, GigaChat Pro, GigaChat Lite, YaGPT Pro and Llama 3 7B

Hello friends! Today we would like to discuss five popular LLMs (Large Language Models) that our team has worked with: ChatGPT-4, GigaChat Pro, GigaChat Lite, YaGPT Pro and Llama 3 7B. Each of these models has its own characteristics, advantages and limitations. In this article, we will describe as much as possible the details that will help you better understand the nuances of working with each of them, and we will tell you for which tasks each of the models is best suited.

1. ChatGPT-4

Technical details:

Architecture: A Transformer-based model with billions of parameters (the exact number is not disclosed).
Size: According to preliminary estimates, the number of parameters may exceed 100 billion.
Education: The model was trained on a huge volume of text in multiple languages using RLHF (reinforcement learning from human feedback) techniques, which improves the quality and adaptability of responses.

Pros:

Wide range of tasks: ChatGPT-4 is versatile and supports a variety of use cases, from text creation to programming assistance and data analysis.
Text generation quality: High quality texts, logic and creativity. The model takes into account context and is capable of creating coherent narratives.
Contextual memory: Capable of accounting for up to 8,000 context tokens, allowing you to support long conversations.

Cons:

Performance: The model requires significant computational resources and generation time, which can be a disadvantage in real-time systems.
High cost: Using a model, especially for data-intensive problems, can be expensive.

Ideal tasks: Article writing, content creation, programming support, data analysis, multitasking.

2. GigaChat Pro

Technical details:

Architecture: Also based on the Transformer architecture, but optimized for performance.
Size: The number of parameters is not disclosed, but the model is smaller than ChatGPT-4, which improves its performance.
Education: Model training included specialized datasets for technical tasks such as programming and data analysis.

Pros:

High performance: The model generates responses faster, making it suitable for interactive applications.
Optimization: GigaChat Pro is optimized for specific tasks such as programming and technical analysis.
Flexible Integration: The model is easily integrated into various systems thanks to API support and developer tools.

Cons:

The text quality is lower: Texts are less coherent and may be less creative than ChatGPT-4.
Narrow specialization: Although the model is good at technical tasks, it may be inferior at more general and creative tasks.

Ideal tasks: Quick solution of technical problems, programming, integration into applications, creation of chatbots.

3. GigaChat Lite

Technical details:

Architecture: A simplified version of GigaChat Pro, based on the same principles of transformers.
Size: The model is smaller and lighter than the Pro version, with fewer parameters.
Education: Narrower and simpler datasets are used, which reduces the cost of training and operating the model.

Pros:

Economical: Significantly cheaper to use compared to more powerful models.
Fast generation: Due to its smaller size, the model generates answers faster.
Lightweight: Consumes less computing resources, which makes it suitable for use on devices with limited power.

Cons:

Limited functionality: Limited support for complex tasks and contexts.
Low text quality: Texts may be of lower quality and require additional editing.

Ideal tasks: Simple tasks that require fast text generation, work with limited resources, cost-effective solutions.

4. YaGPT Pro

Technical details:

Architecture: Cloud architecture based on transformers, optimized for the Russian language.
Size: The number of parameters is close to large models in order to maintain high quality generation in Russian.
Education: The training was conducted on datasets with an emphasis on Russian-language texts and cultural features.

Pros:

Specialization in Russian: Better work with Russian-language content thanks to training on relevant texts.
Customization flexibility: Users can customize generation parameters and adapt the model to specific tasks.
Efficiency: The model is optimized for fast response and efficient use of resources.

Cons:

Limited support for other languages: The model is not as effective in other languages, especially English.
Average text quality: While the texts are good for Russian, they may not be as coherent or creative as other models.

Ideal tasks: Projects aimed at Russian-speaking audiences, content taking into account cultural and linguistic characteristics, tasks with flexible settings.

5. Llama 3 7B

Technical details:

Architecture: Transformer-based model optimized for small data volumes.
Size: 7 billion parameters – significantly less than other models, which makes it more lightweight.
Education: The model was trained on open datasets using self-supervised learning techniques, which increases its adaptability.

Pros:

Lightweight: The model requires less computing resources and runs faster at limited capacity.
Open Source: Allows you to easily customize and adapt the model to your needs.
Good text quality: For a model with so many parameters, Llama 3 7B demonstrates decent text quality.

Cons:

Limited Features: Due to the small number of parameters, the model cannot cope with such complex tasks as ChatGPT-4.
Low adaptability: The model is less adaptable to new tasks and contexts, which may limit its use in complex projects.

Ideal tasks: Prototyping, resource-constrained tasks, open source projects, early stage AI product development.

Conclusion

Each of these models has its own strengths and weaknesses, making them suitable for different tasks and scenarios. ChatGPT-4 is a versatile tool with high text quality, ideal for complex projects. GigaChat Pro And Lite offer performance and cost-effectiveness especially useful for technical and real-time applications. YaGPT Pro – an excellent choice for Russian-language projects where cultural and linguistic nuances are important. Llama 3 7B stands out for its ease and customization, which makes it attractive for projects with limited resources and developers who prefer to work with open source code.

The choice of model depends on the specifics of your project, available resources and priorities, be it text quality, performance, or customization flexibility.

Our choice

Our team is actively developing an internal product – foxtailbox.ru. This is a service for automated assessment of the skills of IT specialists. In it, we also use LLM to generate testing questions and evaluate answers.

Perfect for our needs Llama 3 7B model, since it provides the optimal balance of text quality and computing resources.

Despite the relatively small size of 7 billion parameters, the model demonstrates good text generation quality and customization flexibility thanks to its open source code.

This allows us to adapt it to our specific tasks, such as automatically generating questions and evaluating answers, without significant infrastructure costs. The model's lightweight nature also makes it ideal for use at limited capacity, which is important for prototyping and developing AI-based products.

We hope the review was useful. If you have your own thoughts or experience with these models, please share them in the comments. It will be interesting to discuss!

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