DeepSeek Coder 33B Instruct

Slow response time but excels in handling extensive data analysis tasks.

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DeepSeek Coder 33B Instruct is a superior language model designed for code generation and completion. It delivers top-tier results on various benchmarks and is fine-tuned with a mix of English and Chinese data. Its advanced fill-in-the-blank task feature enhances its ability to provide accurate and contextually relevant code suggestions.

Context window(in thousands)16384

Use cases for DeepSeek Coder 33B Instruct

  1. Market trend prediction: Analyzes large datasets to identify potential market trends and user behavior patterns.
  2. E-learning personalization: The DeepSeek Coder 33B Instruct can analyze student data to tailor personalized learning experiences.
  3. Climate change analysis: Processes extensive datasets from climate research to identify and predict trends.
Arena EloN/A
MT BenchN/A

DeepSeek Coder 33B Instruct is not currently ranked on the Chatbot Arena Leaderboard.

GPT-4 Omni


GPT-4 1106 Preview


GPT-4 0125 Preview


Llama 3 Instruct (70B)


GPT-4 0314


Throughput(output tokens per second)18
Latency(seconds to first tokens chunk received)0.2
Total Response Time(seconds to output 100 tokens)7.7

This model has a slow response time and low latency, which may limit its effectiveness for time-sensitive tasks.

What's Twitter saying?

  • Innovative Code LLM Offering Both Instructions and Fill-in-the-Middle: Codestral is gaining attention for being the first code LLM to handle both instructions and fill-in-the-middle tasks. It outperforms DeepSeek Coder 33B, which is a current state-of-the-art open-source code LLM and 50% larger. (Source: @maximelabonne)
  • 2024 Trends in Multimodal and Synthetic Data: GPT4-V excels at image-to-code tasks, but most open-source VLMs struggle. To improve this, a new dataset called WebSight was created using Mistral-7B-v0.1 and DeepSeek Coder 33B Instruct. (Source: @LeoTronchon)
  • Overview of Open Access LLMs Trained in China: This overview covers 8 open access LLMs trained in China, including Qwen, Yi, and DeepSeek. It highlights each model's parameters, context length, and unique features. (Source: @osanseviero)

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What is DeepSeek Coder and what can it do?

DeepSeek Coder is a suite of code language models with capabilities ranging from project-level code completion to infilling tasks. It is trained on 2T tokens, composed of 87% code and 13% natural language in both English and Chinese, and comes in various sizes up to 33B parameters. This model achieves state-of-the-art performance on multiple programming languages and benchmarks.

Can DeepSeek Coder be used for commercial purposes?

Yes, DeepSeek Coder supports commercial use under its licensing agreement. It is licensed under the MIT License for the code repository, with the usage of models being subject to the Model License. Review the LICENSE-MODEL for more details.

What programming languages does DeepSeek Coder support?

While specific languages supported are not listed, DeepSeek Coder is trained on a vast dataset comprising 87% code from multiple sources, suggesting broad language support. Its state-of-the-art performance across various benchmarks indicates strong capabilities in the most common programming languages.

How can I get support or ask questions about DeepSeek Coder?

If you have questions or need support with DeepSeek Coder, you're encouraged to raise an issue on the Hugging Face repository or contact the DeepSeek team directly at [email protected].

Is the model too large for serverless applications?

Yes, the 33B parameter model is too large for loading in a serverless Inference API. However, it can be launched on dedicated Inference Endpoints (like Telnyx) for scalable use. This ensures that users with high computational demands can still leverage the model's capabilities efficiently.

How can I stay updated on DeepSeek Coder's developments?

To stay informed about updates and developments related to DeepSeek Coder, follow the project on its Hugging Face repository or join the community through platforms like Discord or Wechat for discussions and announcements.