DeepSeek-API 🤗🤗 - Unofficial Reverse Engineering 🚀

Table of Contents

• Introduction
• Repository Status Update
• Quick Start
• Installation
• DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
  • 1. Introduction
  • 2. News
  • 3. Model Downloads
  • 4. Evaluation Results
  • 5. Model Architecture
  • 6. Chat Website
• Contributing
• License
• Get in Touch

Crafted with ❤️ by Devs Do Code (Sree)

Disclaimer: This project is not officially associated with DeepSeek AI. It is an independent reverse engineering effort to explore the DeepSeek Chat Website.

🚀 Repository Status Update:

🛑 Important Notice: This repository is no longer maintained by the owner Devs Do Code (Sree). Any contribution in this repository is heartily welcomed 💝💝

🚀 Quick Start

1. Clone the Repository:

   git clone https://github.com/SreejanPersonal/DeepSeek-API-Unofficial.git

2. Access the DeepSeek Playground:

   • Navigate to the DeepSeek Playground and sign in with your account.
   • This platform allows you to interact with the available models and observe API requests.

3. Access Developer Tools:

   • Open the Developer Tools in your browser with Ctrl + Shift + I (Windows/Linux) or Cmd + Option + I (Mac).
   • Select the Network tab to monitor network activity.

4. Initiate a New Conversation:

   • Choose any available model (e.g., Coder v2 or Chat v2) on the DeepSeek Playground to start a conversation.
   • Enter a query in the chat interface, such as Hi, Introduce yourself.

5. Locate the completions Request:

   • In the Network tab, find the API request labeled completions.
   • Click on this request to inspect its details.

6. Obtain the JWT Token:

   • In the Request Headers section, locate the Authorization entry.
   • Copy the JWT Token value (it appears as a long string without the Bearer prefix). This token serves as your API key and must be kept confidential.
   • Example format: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzc29faWQiOiI3OTg3ZTZmYS1kZDUzLTRlMzQtYjkxNC1lNWUzZWVlM2IwYjMiLCJpc19vYXV0aCI6MSwib2F1dGhfcHJvdmlkZXIiOiJ......

🛠️ Installation

After cloning the repository and obtaining your JWT Token, follow these steps to set up and run the project:

1. Navigate to the Project Directory:

   cd DeepSeek-API-Unofficial

2. Create a .env File:

   • Inside the project directory, create a .env file.
   • Add your JWT Token to this file. You can use the provided .env.example file as a reference.

   cp .env.example .env

   • Open the .env file and insert your token:

   DEEPSEEK=your_jwt_token_here
   

3. Install Required Dependencies:

   • Ensure you have pip installed and run:

   pip install -r requirements.txt

4. Run the Application:

   • Execute the main script to start interacting with the DeepSeek API:

   python main.py

By following these steps, you will set up the environment and be able to interact with the DeepSeek models using the unofficial API.

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

1. Introduction

Today, we’re introducing DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times.

We pretrained DeepSeek-V2 on a diverse and high-quality corpus comprising 8.1 trillion tokens. This comprehensive pretraining was followed by a process of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unleash the model's capabilities. The evaluation results validate the effectiveness of our approach as DeepSeek-V2 achieves remarkable performance on both standard benchmarks and open-ended generation evaluation.

2. News

• 2024.05.16: We released the DeepSeek-V2-Lite.
• 2024.05.06: We released the DeepSeek-V2.

3. Model Downloads

Model	#Total Params	#Activated Params	Context Length	Download
DeepSeek-V2-Lite	16B	2.4B	32k	🤗 HuggingFace
DeepSeek-V2-Lite-Chat (SFT)	16B	2.4B	32k	🤗 HuggingFace
DeepSeek-V2	236B	21B	128k	🤗 HuggingFace
DeepSeek-V2-Chat (RL)	236B	21B	128k	🤗 HuggingFace

Due to the constraints of HuggingFace, the open-source code currently experiences slower performance than our internal codebase when running on GPUs with Huggingface. To facilitate the efficient execution of our model, we offer a dedicated vllm solution that optimizes performance for running our model effectively.

4. Evaluation Results

Base Model

Standard Benchmark (Models larger than 67B)

Benchmark	Domain	LLaMA3 70B	Mixtral 8x22B	DeepSeek-V1 (Dense-67B)	DeepSeek-V2 (MoE-236B)
MMLU	English	78.9	77.6	71.3	78.5
BBH	English	81.0	78.9	68.7	78.9
C-Eval	Chinese	67.5	58.6	66.1	81.7
CMMLU	Chinese	69.3	60.0	70.8	84.0
HumanEval	Code	48.2	53.1	45.1	48.8
MBPP	Code	68.6	64.2	57.4	66.6
GSM8K	Math	83.0	80.3	63.4	79.2
Math	Math	42.2	42.5	18.7	43.6

Standard Benchmark (Models smaller than 16B)

Benchmark	Domain	DeepSeek 7B (Dense)	DeepSeekMoE 16B	DeepSeek-V2-Lite (MoE-16B)
Architecture	-	MHA+Dense	MHA+MoE	MLA+MoE
MMLU	English	48.2	45.0	58.3
BBH	English	39.5	38.9	44.1
C-Eval	Chinese	45.0	40.6	60.3
CMMLU	Chinese	47.2	42.5	64.3
HumanEval	Code	26.2	26.8	29.9
MBPP	Code	39.0	39.2	43.2
GSM8K	Math	17.4	18.8	41.1
Math	Math	3.3	4.3	17.1

For more evaluation details, such as few-shot settings and prompts, please check our paper.

Context Window

Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V2 performs well across all context window lengths up to 128K.

Chat Model

Standard Benchmark (Models larger than 67B)

Benchmark	Domain	QWen1.5 72B Chat	Mixtral 8x22B	LLaMA3 70B Instruct	DeepSeek-V1 Chat (SFT)	DeepSeek-V2 Chat (SFT)	DeepSeek-V2 Chat (RL)
MMLU	English	76.2	77.8	80.3	71.1	78.4	77.8
BBH	English	65.9	78.4	80.1	71.7	81.3	79.7
C-Eval	Chinese	82.2	60.0	67.9	65.2	80.9	78.0
CMMLU	Chinese	82.9	61.0	70.7	67.8	82.4	81.6
HumanEval	Code	68.9	75.0	76.2	73.8	76.8	81.1
MBPP	Code	52.2	64.4	69.8	61.4	70.4	72.0
LiveCodeBench (0901-0401)	Code	18.8	25.0	30.5	18.3	28.7	32.5
GSM8K	Math	81.9	87.9	93.2	84.1	90.8	92.2
Math	Math	40.6	49.8	48.5	32.6	52.7	53.9

Standard Benchmark (Models smaller than 16B)

Benchmark	Domain	DeepSeek 7B Chat (SFT)	DeepSeekMoE 16B Chat (SFT)	DeepSeek-V2-Lite 16B Chat (SFT)
MMLU	English	49.7	47.2	55.7
BBH	English	43.1	42.2	48.1
C-Eval	Chinese	44.7	40.0	60.1
CMMLU	Chinese	51.2	49.3	62.5
HumanEval	Code	45.1	45.7	57.3
MBPP	Code	39.0	46.2	45.8
GSM8K	Math	62.6	62.2	72.0
Math	Math	14.7	15.2	27.9

English Open Ended Generation Evaluation

We evaluate our model on AlpacaEval 2.0 and MTBench, showing the competitive performance of DeepSeek-V2-Chat-RL on English conversation generation.

Chinese Open Ended Generation Evaluation

Alignbench (https://arxiv.org/abs/2311.18743)

模型	开源/闭源	总分	中文推理	中文语言
gpt-4-1106-preview	闭源	8.01	7.73	8.29
DeepSeek-V2 Chat (RL)	开源	7.91	7.45	8.36
erniebot-4.0-202404 (文心一言)	闭源	7.89	7.61	8.17
DeepSeek-V2 Chat (SFT)	开源	7.74	7.30	8.17
gpt-4-0613	闭源	7.53	7.47	7.59
erniebot-4.0-202312 (文心一言)	闭源	7.36	6.84	7.88
moonshot-v1-32k-202404 (月之暗面)	闭源	7.22	6.42	8.02
Qwen1.5-72B-Chat (通义千问)	开源	7.19	6.45	7.93
DeepSeek-67B-Chat	开源	6.43	5.75	7.11
Yi-34B-Chat (零一万物)	开源	6.12	4.86	7.38
gpt-3.5-turbo-0613	闭源	6.08	5.35	6.71
DeepSeek-V2-Lite 16B Chat	开源	6.01	4.71	7.32

Coding Benchmarks

We evaluate our model on LiveCodeBench (0901-0401), a benchmark designed for live coding challenges. As illustrated, DeepSeek-V2 demonstrates considerable proficiency in LiveCodeBench, achieving a Pass@1 score that surpasses several other sophisticated models. This performance highlights the model's effectiveness in tackling live coding tasks.

5. Model Architecture

DeepSeek-V2 adopts innovative architectures to guarantee economical training and efficient inference：

• For attention, we design MLA (Multi-head Latent Attention), which utilizes low-rank key-value union compression to eliminate the bottleneck of inference-time key-value cache, thus supporting efficient inference.
• For Feed-Forward Networks (FFNs), we adopt DeepSeekMoE architecture, a high-performance MoE architecture that enables training stronger models at lower costs.

6. Chat Website

You can chat with the DeepSeek-V2 on DeepSeek's official website: chat.deepseek.com

🤝 Contributing

Your contributions are welcome! Please refer to our CONTRIBUTING.md for contribution guidelines.

📜 License

This project is licensed under the MIT License. Full license text is available in the LICENSE file.

📬 Get in Touch

For inquiries or assistance, please open an issue or reach out through our social channels:

We appreciate your interest in DeepSeek-API-Unofficial