Tongyi
Qwen

Qwen2.5 7B Instruct

qwen/qwen2.5-7b-instruct

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:

  • Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
  • Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
  • Long-context Support up to 128K tokens and can generate up to 8K tokens.
  • Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

This repo contains the instruction-tuned 7B Qwen2.5 model, which has the following features:

  • Type: Causal Language Models
  • Training Stage: Pretraining & Post-training
  • Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
  • Number of Parameters: 7.61B
  • Number of Paramaters (Non-Embedding): 6.53B
  • Number of Layers: 28
  • Number of Attention Heads (GQA): 28 for Q and 4 for KV
  • Context Length: Full 131,072 tokens and generation 8192 tokens
    • Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts.

For more details, please refer to our blog, GitHub, and Documentation.

Tools

Function Calling

Community

Open Source

Context Window

128,000

Max Output Tokens

65,536

Language

PythonJavaScriptCurl

Using Qwen2.5 7B Instruct with Python API

Using Qwen2.5 7B Instruct with OpenAI compatible API

import openai

client = openai.Client(
  api_key= '{your_api_key}',
  base_url="https://api.model.box/v1",
)
response = client.chat.completions.create(
model="qwen/qwen2.5-7b-instruct",
messages: [
  {
    role: 'user',
    content:
      'introduce your self',
    },
  ]
)
print(response)