---
title: "qwen2.5-7b-instruct"
publisher: "qwen"
type: "endpoint"
updated: "2025-05-22T18:02:31.590Z"
description: "Chinese and English LLM targeting for language, coding, mathematics, reasoning, etc."
canonical: "https://build.nvidia.com/qwen/qwen2_5-7b-instruct"
---

# Model Overview

## Description:
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 model is ready for commercial/non-commercial use.

## Third-Party Community Consideration
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA [Qwen2.5-7B-Instruct Model Card](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).

## License/Terms of Use
Qwen/Qwen2.5-7B-Instruct is licensed under the [Apache 2.0 License](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE)

## References:
[Blog](https://qwenlm.github.io/blog/qwen2.5/), [Github](https://github.com/QwenLM/Qwen2.5), [Documentation](https://qwen.readthedocs.io/en/latest/), [Technical Report](https://arxiv.org/abs/2412.15115)

## Model Architecture:
**Architecture Type:** Transformer <br>
**Network Architecture:** Qwen2.5-7B-Instruct

## Input:
**Input Type(s):** Text <br>
**Input Format(s):** String <br>
**Input Parameters:** 1D

## Output:
**Output Type(s):** Text <br>
**Output Format:** String <br>
**Output Parameters:** 1D

## Model Version(s):
Qwen2.5-7B-Instruct

## Training, Testing, and Evaluation Datasets:

## Training Dataset:
**Link:** Unknown <br>
**Data Collection Method by dataset:** Unknown <br>
**Labeling Method by dataset:** Unknown <br>
**Properties:** The size of the pre-training dataset is expanded from 7 trillion tokens used in Qwen2 to a maximum of 18 trillion tokens.

## Testing Dataset:
**Link:** Unknown <br>
**Data Collection Method by dataset:** Unknown <br>
**Labeling Method by dataset:** Unknown <br>
**Properties:** Unknown

## Evaluation Dataset:
**Link:** See evaluation section of the [Hugging Face Qwen2.5-7B-Instruct Model Card](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct#evaluation--performance) <br>
**Data Collection Method by dataset:** Unknown <br>
**Labeling Method by dataset:** Unknown <br>
**Properties:** Unknown

## Inference:
**Engine:** TensorRT-LLM <br>
**Test Hardware:** NVIDIA L40S

## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.  

Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

## Prototype

```python
from openai import OpenAI

client = OpenAI(
base_url = "https://integrate.api.nvidia.com/v1",
api_key = "$NVIDIA_API_KEY"
)

completion = client.chat.completions.create(
model="",
messages=[{"role":"user","content":""}],
temperature=,
top_p=,
max_tokens=,
stream=NaN
)

print(completion.choices[0].message)
```

```python
from langchain_nvidia_ai_endpoints import ChatNVIDIA

client = ChatNVIDIA(
model="",
api_key="$NVIDIA_API_KEY", 
temperature=,
top_p=,
max_tokens=,
)

response = client.invoke([{"role":"user","content":""}])
print(response.content)
```

```javascript
import OpenAI from 'openai';

const openai = new OpenAI({
apiKey: '$NVIDIA_API_KEY',
baseURL: 'https://integrate.api.nvidia.com/v1',
})

async function main() {
const completion = await openai.chat.completions.create({
model: "",
messages: [{"role":"user","content":""}],
temperature: ,
top_p: ,
max_tokens: ,
stream: ,
})

process.stdout.write(completion.choices[0]?.message?.content);

}

main();
```

```bash
curl https://integrate.api.nvidia.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $NVIDIA_API_KEY" \
-d '{
"model": "qwen/qwen2.5-7b-instruct",
"messages": [{"role":"user","content":""}],
"temperature": ,   
"top_p": ,
"max_tokens": ,
"stream":                 
}'
```