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Models/DeepSeek Math 7B
DeepSeekOpen Weights✓ Verified Spec & Code

DeepSeek Math 7B

MoE LLM • Released 2025-01-11 • Last Verified 2025-02-01

Try PlaygroundDocs

DeepSeek Math 7B is a versatile open-weight language model developed by DeepSeek. Built on modern Transformer architecture with Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE), DeepSeek Math 7B delivers strong performance in instruction following, multi-turn dialogue, creative synthesis, and structured JSON output generation.

Context Window128k
LicenseMIT License
Deployment
Cloud API Local Run
API AvailableYes (REST/SDK)
💡

Plain English Summary (What is this model & who is it for?)

Think of DeepSeek Math 7B as a versatile AI assistant for writing, research, and brainstorming. It helps you draft emails, write essays, summarize long articles, and generate creative ideas on any topic.

💡 Real-World Use Cases & Practical Examples

✍️ Email & Article Drafting: Draft professional emails, blog posts, and press releases in seconds.
📚 Long Document Summarization: Condense 50-page PDF reports into actionable bullet points.
💡 Brainstorming & Strategy: Generate marketing ideas, product names, and event outlines.
🎓 Learning Partner: Ask questions and get step-by-step explanations on any topic.

🚀 How to Run & Use This Model (Step-by-Step Guide)

Simple setup instructions for everyday users and developers.

1

Download a One-Click App (No Coding Required)

Download a free local AI launcher like LM Studio (lmstudio.ai) or Ollama (ollama.com) on your Mac, Windows, or Linux PC.

2

Load the Model

In LM Studio, search for "DeepSeek Math 7B". In Ollama, open your terminal and run "ollama run deepseek-math-7b".

3

Start Chatting or Generating

Type your text instructions or upload files into the app. The AI runs 100% privately on your hardware without internet requirement!

4

Developer API Integration

Developers can integrate DeepSeek Math 7B directly via Python (using Hugging Face transformers/diffusers) or connect via local OpenAI-compatible REST server (http://localhost:11434).

Benchmark Performance

MATH / Code90

Hardware Requirements for Local Running

8GB-12GB VRAM

Strengths

  • •High accuracy
  • •Fast inference

Limitations & Weaknesses

  • •Closed source API
Integration Code (text-chat)
import openai

client = openai.OpenAI()

response = client.chat.completions.create(
    model="deepseek-math-7b",
    messages=[
        {"role": "system", "content": "You are an expert AI assistant."},
        {"role": "user", "content": "Explain quantum computing in 2 sentences."}
    ]
)

print(response.choices[0].message.content)

Pricing Overview

MIT Open Source

Prices subject to provider tiers and volume discounts. Check documentation for current token rates.

Model Tags

#deepseek#mit-license#reasoning#local

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