Deepseek V3

2016, High-Flyer experimented with a multi-factor price-volume based model to take stock positions, began testing in trading the following year and then more broadly adopted machine learning-based strategies. https://en.wikipedia.org/wiki/High-Flyer https://en.wikipedia.org/wiki/DeepSeek

Started using Deepseek queries when it was launched. Now, despite the massive impact and usage end January 2025; responses are still in most cases fast, relevant and detailed. For further check on open source stack usage asked below question and got a rather well elaborated answer after 7 Seconds.

https://chat.deepseek.com

Query:

Looking for a tutorial on Deepseek open source stack for personal research which I did with other LLM’s (notes in https://we0we.com/)

Deepseek reply:

DeepSeek is a relatively new open-source stack for working with large language models (LLMs), and it’s designed to make it easier for researchers and developers to experiment with, fine-tune, and deploy LLMs. If you’re looking for a tutorial or guide to get started with DeepSeek for personal research, here’s a step-by-step outline to help you explore the stack, especially if you’ve already worked with other LLMs like GPT, LLaMA, or others.

1. Understand DeepSeek

Before diving in, familiarize yourself with DeepSeek’s architecture and goals. DeepSeek is designed to be modular, open-source, and research-friendly, so it likely includes tools for:

Model training and fine-tuning
Data preprocessing
Inference and deployment
Experiment tracking

Check out the official documentation and GitHub repository for DeepSeek to understand its components:

DeepSeek GitHub Repository (if available)
Official documentation or blog posts (if any)

2. Set Up Your Environment

To work with DeepSeek, you’ll need a suitable environment. Here’s how to set it up:

Prerequisites

Python 3.8 or higher
CUDA (if using GPU)
PyTorch or TensorFlow (depending on DeepSeek’s backend)
Git (to clone the repository)

Installation

Clone the DeepSeek repository:

   git clone https://github.com/deepseek-ai/deepseek.git
   cd deepseek

Install dependencies:

   pip install -r requirements.txt

Verify the installation by running a simple test or example provided in the repository.

3. Explore the DeepSeek Stack

DeepSeek likely includes the following components:

Data Preprocessing Tools: For cleaning and preparing datasets.
Model Training Scripts: For fine-tuning pre-trained models.
Inference APIs: For generating text or other outputs.
Evaluation Metrics: For assessing model performance.

Key Commands

Data Preprocessing:

  python scripts/preprocess_data.py --input data/raw --output data/processed

Fine-Tuning:

  python scripts/train.py --model deepseek-base --dataset data/processed

Inference:

  python scripts/generate.py --model deepseek-finetuned --prompt "Your input text"

4. Fine-Tune a Model

If you’ve worked with other LLMs, you’ll find the fine-tuning process similar. Here’s a general workflow:

Prepare Your Dataset: Use the preprocessing tools to format your data.
Choose a Base Model: DeepSeek likely provides pre-trained models to start with.
Fine-Tune:

Adjust hyperparameters (learning rate, batch size, etc.).
Use the training script to fine-tune the model on your dataset.

Evaluate: Use the provided evaluation tools to check performance.

5. Experiment Tracking

DeepSeek might integrate with tools like Weights & Biases, TensorBoard, or MLflow for tracking experiments. Set this up to log your training runs, metrics, and results.

6. Compare with Other LLMs

Since you’ve worked with other LLMs, you can compare DeepSeek’s performance, ease of use, and features with those of GPT, LLaMA, or others. Document your findings on your research notes site (we0we.com).

7. Contribute to the Community

If DeepSeek is open-source, consider contributing to the project by:

Reporting issues
Improving documentation
Adding new features

Resources

DeepSeek GitHub: https://github.com/search?q=deepseek&type=repositories (if available)
PyTorch/TensorFlow Documentation: For understanding the backend.
Hugging Face Transformers: If DeepSeek integrates with it, this will be useful.

If you can’t find the official DeepSeek repository or documentation, let me know, and I can help you find alternatives or similar tools for your research!

Added 27 February the reference to Perplexity version of the DeepSeek-R1 model that has been post-trained to provide unbiased, accurate, and factual information.

Perplexity (Deep Research)