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Qwen2.5 7B InstructReason. Code. Instruct.

Try Qwen2.5 7B InstructAPI Documentation

Master Core Capabilities

Math Excellence

Qwen2.5 7B Instruct MATH

Achieves 75.5 on MATH, 91.6 on GSM8K, surpassing Llama3.1-8B.

Code Mastery

Qwen2.5 7B Instruct Coding

Scores 84.8 on HumanEval with specialized coding and math training.

Multilingual Support

Qwen2.5 7B Instruct Languages

Handles 29+ languages, long contexts to 128K tokens via RoPE and GQA.

Examples

See what Qwen2.5 7B Instruct can create

Copy any prompt below and try it yourself in the playground.

Math Proof

“Prove the Pythagorean theorem step-by-step using geometric arguments, then verify with coordinates. Output in LaTeX format.”

Code Debugger

“Debug this Python function that sorts a list but fails on duplicates: def sort_list(lst): return sorted(set(lst)). Explain fixes and rewrite.”

JSON Generator

“Create a JSON schema for a task management API with endpoints for tasks, users, and projects. Include validation rules.”

Multilingual Summary

“Summarize this English article on quantum computing in Spanish, then translate key terms to Japanese. Keep under 200 words.”

For Developers

A few lines of code.
Instruct. Five lines.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Qwen2.5 7B Instruct

Read the docs

Qwen2.5 7B Instruct API provides access to a 7B parameter instruction-tuned LLM. It excels in reasoning, coding, and multilingual tasks. Supports fine-tuning via LoRA.

Outperforms Gemma2-9B-IT and Llama3.1-8B on MMLU-Pro (56.3), MATH (75.5), and HumanEval (84.8). Handles 128K context, generates 8K tokens.

Qwen2.5 7B Instruct serves as an efficient alternative to larger models like Llama3.1-8B. Offers better math and coding at 7B scale. Ideal for API deployments.

Supports up to 128K input tokens with RoPE embeddings. Uses GQA for faster inference and stable long-text generation.

Supports 29+ languages including Chinese, English, Spanish, Arabic. Trained on 18T tokens with byte-level BPE for diverse tasks.

Enhanced instruction following, structured JSON output, and role-play resilience. Strong in coding, math, and agentic systems.

Ready to create?

Start generating with Qwen2.5 7B Instruct on ModelsLab.

Try Qwen2.5 7B InstructAPI Documentation