---
title: "mistral-7b-instruct-v0.3"
publisher: "mistralai"
type: "endpoint"
updated: "2025-06-11T01:51:36.563Z"
description: "This LLM follows instructions, completes requests, and generates creative text."
canonical: "https://build.nvidia.com/mistralai/mistral-7b-instruct-v03"
---

# Model Overview

## Description:
Mistral-7B-Instruct-v0.3 is a language model that can follow instructions, complete requests, and generate creative text formats.  It is an instruct version of the Mistral-7B-v0.3 generative text model fine-tuned using a variety of publicly available conversation datasets. 

This model is ready for 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 [Mistral's 7B Instruct Hugging Face Model Card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).

## Terms of use
By using this software or model, you are agreeing to the [terms and conditions](https://mistral.ai/terms-of-service/) of the license, acceptable use policy and Mistral's privacy policy. Mistral-7B is released under the Apache 2.0 license

## References(s):
Mistral 7B Instruct v0.3 [Model Card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on Hugging Face <br>
Mistral 7B [Paper](https://arxiv.org/abs/2310.06825) <br>
Mistral 7B [Blogpost](https://mistral.ai/news/announcing-mistral-7b/)

## Model Architecture:
**Architecture Type:** Transformer <br>
**Network Architecture:** Mistral-7B <br>
**Model Version:** 0.3 <br>

**Input** 
* Input Type: Text
* Input Format: String
* Input Parameters: max_tokens, temperature, top_p, stop, frequency_penalty, presence_penalty, seed

**Output** 
* Output Type: Text
* Output Format: String

## Software Integration:
* Supported Hardware Platform(s): NVIDIA Hopper 
* Preferred Operating System(s): Linux <br>

## Inference
**Engine:** TensorRT-LLM <br>
**Test Hardware:** H100

## 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": "mistralai/mistral-7b-instruct-v0.3",
"messages": [{"role":"user","content":""}],
"temperature": ,   
"top_p": ,
"max_tokens": ,
"stream":                 
}'
```