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What is deepseek-llm?

deepseek-ai/deepseek-llm — explained in plain English

Analysis updated 2026-06-24

6,905MakefileAudience · researcherComplexity · 4/5LicenseSetup · hard

In one sentence

DeepSeek LLM is a set of open-weight AI language models in 7B and 67B sizes, trained on 2 trillion tokens and released for research and commercial use, covering coding, math, and both English and Chinese.

Mindmap

mindmap
  root((DeepSeek LLM))
    Models
      7B parameters
      67B parameters
      Base and chat
    Capabilities
      Coding tasks
      Mathematics
      Chinese and English
    Training
      2T tokens
      Intermediate checkpoints
    Usage
      Hugging Face
      Python Transformers
      Local inference
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What do people build with it?

USE CASE 1

Run a local 7B chat model for coding help or Q&A without sending data to any external service.

USE CASE 2

Fine-tune the 67B base model on your own dataset to build a specialized language model for your domain.

USE CASE 3

Study how large language model capabilities develop by downloading and evaluating intermediate training checkpoints.

USE CASE 4

Deploy the DeepSeek chat model to answer questions and write code in both English and Chinese.

What is it built with?

PythonPyTorchHugging Face TransformersCUDA

How does it compare?

deepseek-ai/deepseek-llmyuk7/archwslfoostan/crkbd
Stars6,9057,3827,413
LanguageMakefileMakefileMakefile
Setup difficultyhardmoderatehard
Complexity4/52/54/5
Audienceresearcherdevelopergeneral

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1h+

The 67B model requires multiple high-end GPUs, even the 7B model needs significant GPU VRAM to run at reasonable speed.

Code is MIT (free to use commercially), model weights have a separate license that also permits commercial use.

So what is it?

DeepSeek LLM is a collection of open-weight AI language models released by DeepSeek AI for research and commercial use. The models answer questions, write code, solve math problems, and hold conversations in both English and Chinese. They were trained from scratch on two trillion tokens of text, which is the raw data fed to the model during its learning process. Two sizes are available: a 7 billion parameter version that runs on modest hardware, and a 67 billion parameter version that rivals much larger models from other organizations. Each size comes in two forms: a base model trained on general text, and a chat model fine-tuned to follow instructions and hold conversations. The 67B chat model scores notably well on coding tasks and mathematics, including a Hungarian national high school exam it had not seen during training. The models are hosted on Hugging Face, a platform where AI models are shared and downloaded. You load them using a Python library called Transformers, pass in your text, and receive a response. The README includes short code examples showing how to load a model and generate output, as well as how to run a multi-turn conversation with the chat version. Evaluation results comparing DeepSeek LLM against other publicly available models are included in the repository, covering reasoning, reading comprehension, coding, and Chinese language benchmarks. The base models are released under an MIT license for the code, with a separate model license governing use of the weights themselves. Commercial use is permitted under the model license terms. The repository also hosts intermediate training checkpoints, which researchers can download from cloud storage to study how the model's capabilities developed at different points during training.

Copy-paste prompts

Prompt 1
Load the DeepSeek 7B chat model using Hugging Face Transformers in Python and run a multi-turn conversation about a coding problem.
Prompt 2
Write a Python script that loads the DeepSeek 67B base model and generates completions for a given prompt with temperature sampling.
Prompt 3
How do I download a specific intermediate training checkpoint of DeepSeek LLM from cloud storage and evaluate it on a math benchmark?
Prompt 4
Set up the DeepSeek 67B chat model for inference across two A100 GPUs using model parallelism with the Transformers library.
Prompt 5
Compare the DeepSeek 7B and 67B chat models on math word problems using a Python evaluation script and print the results side by side.

Frequently asked questions

What is deepseek-llm?

DeepSeek LLM is a set of open-weight AI language models in 7B and 67B sizes, trained on 2 trillion tokens and released for research and commercial use, covering coding, math, and both English and Chinese.

What language is deepseek-llm written in?

Mainly Makefile. The stack also includes Python, PyTorch, Hugging Face Transformers.

What license does deepseek-llm use?

Code is MIT (free to use commercially), model weights have a separate license that also permits commercial use.

How hard is deepseek-llm to set up?

Setup difficulty is rated hard, with roughly 1h+ to a first successful run.

Who is deepseek-llm for?

Mainly researcher.

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