Qwen2.5 72B Instruct

Qwen 2-72B-Instruct is a powerful language model developed by Alibaba. It features 72.7 billion parameters and excels in tasks like language understanding and text generation. This model supports over 29 languages and can handle long texts of up to 131,072 tokens. It's designed for efficient coding, mathematics, and reasoning tasks, making it versatile for various applications.

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "qwen/qwen-2.5-72b-instruct",
    messages: [
      {
        "role": "user",
        "content": "Write a blog about cat."
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="qwen/qwen-2.5-72b-instruct",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

Qwen2.5 72B Instruct

ContextLong-context

Input$0.23 / M

Output$0.4 / M

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Description

Qwen 2.5 72B Instruct is created by Alibaba Cloud. It was released in September 2024. This model is strong in text generation and language translation. It can also hold conversations well. 

With 72.7 billion parameters, it can handle long texts up to 128k tokens. It can generate outputs of 8k tokens. The model uses advanced techniques like rope and swiglu. These help it process complex inputs effectively.

It supports over 29 languages, such as English, Chinese, French, and Spanish. The model follows instructions closely. It can create structured outputs, which is great for chatbots.

Qwen 2.5 72B Instruct shows big improvements in knowledge and coding skills. It was trained on a dataset of up to 18 trillion tokens. This gives it a huge knowledge base. In tests, it scored high and beat many other models.

For instance, it scored 82.3 on the MMLU language understanding benchmark. It also got an impressive 86.0 on the coding evaluation benchmark. This shows its strong coding abilities. The model is flexible for many tasks, making it useful in different areas.

In summary, Qwen 2.5 72B Instruct is a top model. It combines power, efficiency, and flexibility. It is ideal for those wanting advanced language processing. Using our AIAPILAB service to integrate this model can lead to better pricing options.

Model API Use Case

The Qwen2.5 72B Instruct API is useful for many tasks in natural language processing. It has 72.7 billion parameters, which helps it create text that sounds human. This API can handle inputs of up to 128,000 tokens, making it great for long documents or conversations.

For instance, businesses can use this API in chatbots to improve customer support. By looking at user questions, it gives accurate and relevant answers. Developers can also use it to quickly create code snippets in different programming languages.

Moreover, educational sites can use the API to make learning more interactive. Students can ask questions, and the model will provide detailed answers or solve math problems. This can help students stay engaged and understand better.

In tests, Qwen2.5 does better than many other models. It has a coding accuracy of 86% on the HumanEval dataset. It also supports over 29 languages, which makes it more versatile. For more information, check [Dataloop](https://dataloop.ai/library/model/qwen_qwen25-72b-instruct/).

Model Review

Pros

1. Qwen 2.5 72B Instruct generates human-like text efficiently. 2. It processes long contexts, up to 128k tokens, seamlessly. 3. The model understands and translates over 29 languages fluently. 4. It excels in coding tasks, solving complex problems accurately. 5. Qwen 2.5 produces structured outputs, enhancing chatbot interactions.

Cons

1. The model struggles with very long texts beyond its context window. It may lose context and coherence. 2. Multilingual support varies in quality. Some languages perform poorly compared to others. 3. High computational resources are necessary for optimal performance. This can limit accessibility for users.

Comparison

Feature/Aspect	Qwen2-72B Instruct	Qwen2.5-72B Instruct
Model Size	72.7 billion parameters	72.7 billion parameters
Key Features	Transformer architecture with swiglu activation; designed for language understanding and generation	Enhanced knowledge, coding capabilities, and instruction following; uses advanced techniques like rope and swiglu
Context Length	Up to 131,072 tokens	Up to 128,000 tokens
Multilingual Support	Supports multiple languages	Supports over 29 languages
Performance in Coding	Strong performance in coding tasks	Significant improvements in coding and mathematics capabilities due to specialized expert models

API

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "qwen/qwen-2.5-72b-instruct",
    messages: [
      {
        "role": "user",
        "content": "Write a blog about cat."
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="qwen/qwen-2.5-72b-instruct",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

FAQ

Q1：What is Qwen 2.5 72B Instruct?
A1：Qwen 2.5 72B Instruct is a powerful language model with 72 billion parameters.

Q2：How does Qwen 2.5 handle long texts?
A2：It processes inputs up to 128k tokens, enabling extensive context handling.

Q3：What tasks can Qwen 2.5 perform?
A3：It excels in text generation, coding, mathematics, and multilingual tasks.

Q4：What technologies enhance Qwen 2.5's performance?
A4：It utilizes advanced techniques like rope and swiglu for efficient processing.

Q5：How can I deploy Qwen 2.5?
A5：You can deploy it using vllm for optimal performance on long contexts.