Bring the power of Gemini 2.0 to help you build applications faster and easier than ever before.

Key Features

- Coding assistance powered by Gemini 2.0.

- Contextualized responses to your prompts to help guide you on what you're trying to do with your code.

- Source citations regarding which documentation and code samples the assistant used to generate its responses.

- Automated code reviews in GitHub with Gemini Code Assist for GitHub.

- Also available in Firebase, Android Studio, VS Code, IntelliJ, Google Cloud Databases, BigQuery, Apigee, and more.

Highlights

Through a natural language chat interface, you can quickly chat with Gemini Code Assist to get answers to cloud questions, or receive guidance on best practices.

Gemeni AI answers:

Okay, I understand you want to use DeepSeek AI within your Flutter application. Since there isn't a specific official DeepSeek AI SDK for Flutter or Dart, you'll need to interact with the DeepSeek AI API using HTTP requests. Here's a breakdown of how to do it, along with some important considerations:

Core Approach: Using HTTP Requests

1. DeepSeek AI API: You'll be interacting with DeepSeek AI through their API.

How is the Gemini AI look like on Android Studio Meerkat?

How to use DeepSeek AI with flutter?

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Introducing DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding.

Introducing DeepSeek LLM (Large Language Model), an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.

1. Superior General Capabilities: DeepSeek LLM 67B Base outperforms Llama2 70B Base in areas such as reasoning, coding, math, and Chinese comprehension.

When using expert parallelism (EP), different experts are assigned to different GPUs. Because the load of different experts may vary depending on the current workload, it is important to keep the load of different GPUs balanced. As described in the DeepSeek-V3 paper, we adopt a redundant experts strategy that duplicates heavy-loaded experts. Then, we heuristically pack the duplicated experts to GPUs to ensure load balancing across different GPUs.

DeepSeekMoE 16B is a Mixture-of-Experts (MoE) language model with 16.4B parameters. It employs an innovative MoE architecture, which involves two principal strategies: fine-grained expert segmentation and shared experts isolation. It is trained from scratch on 2T English and Chinese tokens, and exhibits comparable performance with DeekSeek 7B and LLaMA2 7B, with only about 40% of computations.