Open source LLM: DeepSeek AI for Developers

Discover how Deep Sea trains efficient language models by distillation from large LLMs, using a mixture of experts to route queries, train on targeted Q&A data, and speed up inference.

DeepSeek v3 is an open source language model with 671 billion parameters that uses model distillation to enhance reasoning and relies on a mixture of experts with longer context windows.

Examine the Deep-Sea R1 flagship reasoning model, open source, built with chain of thought, reinforcement learning, distillation, and mixture of experts, and its benchmarks against rivals.

Compare DeepSeek v3 and R1, showing v3 as an efficient standard LLM with fast responses and low cost, while R1 emphasizes reasoning with higher accuracy.

This lecture explains that AI open source differs from traditional software, noting Deep Seek released models and reports but not training code or data, and discusses OSI's transparency standard.

Explore how Leipzig models face Chinese regulations and built-in biases at training and application levels, including leet-speak bypass methods and real-world bias examples in hiring and facial recognition.

Explore Deepsea R1 distilled models, extracting R1 reasoning to fine-tune smaller open-source models like Llama and Queen, enabling effective performance on limited hardware such as Raspberry Pi.

Janus-Pro is a 7-billion-parameter multimodal model that generates and analyzes images, available as open-source and free to download. It demonstrates solid multimodal understanding but limited image realism in text-to-image tasks.

Explore the features of the Deepsee browser chat and the Deep Sea app, getting an overview of the user interface, capabilities, and the privacy policy to understand data sharing.

Explore the browser-based deep sea chat interface, delete or rename chats, switch to the deep sea v3 model, enable search, and attach files with text extraction and image OCR.

Examine the privacy policy, noting data collection including keystroke patterns used to improve models and location-based rights; sign-in requires consent, and you should remove personally identifiable information.

Discover how to run open-source ai models offline, navigate hardware needs for models up to 700GB, and use distilled R1 variants with tools to interact locally.

Install and run the llama cli tool across macOS, Linux, and Windows to test hardware and interact with the Deepsi R1 model at 8 billion parameters locally.

Explore LM studio, a graphical user interface for downloading and running models locally on Mac, Windows, and Linux. Browse Deepsea models, assess GPU offload, and load models interactively.

Explore the deep sea api models, use postman to test integration, and leverage OpenAI-like apis to add smart features to apps with models like deep v3 and r1.

Obtain and manage an api key to access Deepsee models via the api. Create named keys, copy them securely, and revoke unused keys to keep access controlled.

Learn how to perform your first DeepSeek API call using Postman, authenticate with an API key, and explore available models like Deepsea Chat and Deepsea Reasoner.

Learn to create a chat completion using the dipstick api in Postman, sending a prompt and model to generate contextual responses, and inspect the content property and the messages object.

Create a chat completion with a reasoning model using the dipstick API in Postman, compare chat and reasoning models, and learn how to separate content from internal reasoning in responses.

Understand how tokens encode text for ai models like GPT four, including prompt, completion, and total tokens, and how tokenization and embeddings convert words into numerical representations affecting pricing.

Explain how the messages object trains the ai by tracking conversation with system, user, and assistant roles, preserving context in stateless chat; include full history for chat completions.

Control model behavior at inference by adjusting the temperature parameter; low values favor factual, deterministic results, while higher values increase creativity but can cause incoherence and hallucinations.

Assess how the DeepSeek privacy policy applies to web, chat, and API usage, including data tracked and stored. Emphasize GDPR implications and urge developers to align their app privacy policy.

Monitor the dipstick api status page for real-time updates on api availability, delays, and errors; review historical downtime, and contact support when issues arise.

Interact with locally hosted models via the Olama API using Postman on localhost, listing models and generating completions and chat responses.

Explore building apps on IPsec models via API or self-hosted options. Build a chatbot that chats with documents using Colab notebooks or a local Python and VS Code setup.

Launch Google Colab to develop Python in the cloud, running notebooks in Google Drive, using cells to execute code, save copies, and manage secrets like API keys.

Install Python 3, clone repository, run hello world script, and create and activate a virtual environment to install dependencies via pip from requirements.txt and set a .env api key.

Install dependencies in Google Colab, initialize the Deepsee API client with the base URL and API key, then run code to query the chat and reasoning models.

Learn to monitor Google Colab resource usage with the RAM and disk indicator, and manage sessions to terminate idle notebooks, freeing memory and avoiding limits.

Use the Allama Python library to run a locally hosted model, pool a model with a command, and chat via Python to display the final response.

Learn how to integrate a Deep Sea model with link chain to build AI applications, manage conversations, and run local executions using different libraries and APIs.

Build a cool looking interface with Gradio to create interactive web apps for ai projects, including a chat bot with prompts, history, streaming responses, and shareable or local URLs.

Learn to manage security and costs in the deep sea platform by invalidating api tokens, enabling announcements for updates, and providing feedback to shape future topics.