qwen/qwen2.5-coder-32b-instruct

Updated on Jun 11, 2025 Published on Jun 11, 2025

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Qwen2.5‑Coder is the newest code‑specialized entry in the Qwen model family (formerly known as CodeQwen), now available in six sizes—0.5B, 1.5B, 3B, 7B, 14B, and 32B parameters—to cater to developers with diverse requirements. Building on the foundation laid by CodeQwen‑1.5, Qwen2.5‑Coder introduces several key enhancements.

Model Name	qwen/qwen2.5-coder-32b-instruct
Model Type	Text Generation
Model Cost(Input)	$0.55 Per Million Tokens
Model Cost(Output)	$0.57 Per Million Tokens
Model Hosted (Region)	US, Norway, India and Japan
Model Available (Region)	Global
Model Base Architecture	Transformer
Context Window	32,500 Tokens
Model Endpoint Access URL	https://cloud.dataoorts.com/api/v1

Get Your API Credential From: https://cloud.dataoorts.com/ai

import requests
import json

# Define the endpoint URL
url = 'https://cloud.dataoorts.com/api/v1'

# Define the headers including the API Authorization token
headers = {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer $Dataoorts_Serverless_API_KEY'
}

# Define the Model Name and Data Payload with additional parameters
payload = {
    "model": "qwen/qwen2.5-coder-32b-instruct",
    "data": {
        "messages": [
            {"role": "system", "content": "You are an expert deep learning engineer"},
            {"role": "user", "content": "What is Deep Learning"}
        ],
        # Additional hyper-parameters for fine-tuning the output (optional):
        "temperature": 0.1,
        "top_p": 0.9,
        "top_k": 40,
        "max_tokens": 512
    }
}

# Make the POST request
response = requests.post(url, headers=headers, data=json.dumps(payload))
# Parse and print the JSON response with pretty formatting
response_json = response.json()
print(json.dumps(response_json, indent=4))

The provided example for API access is written in Python, but you can use any programming language of your choice to adapt and transform the code as needed. Below, the example output image is shared for reference.

{
    "errors": [],
    "messages": [],
    "result": {
        "response": "Deep Learning is a subset of machine learning that is inspired by the structure and function of the human brain, particularly the way neurons connect and learn from data. It involves the use of neural networks with many layers (hence \"deep\") to model and solve complex problems. Here are some key aspects of deep learning:\n\n1. **Neural Networks**: At the core of deep learning are artificial neural networks (ANNs), which are composed of layers of interconnected nodes or \"neurons.\" Each neuron receives input, processes it, and passes it on to the next layer.\n\n2. **Layers**: Deep learning models typically have multiple layers, including input layers, hidden layers, and output layers. The hidden layers are where the model learns to extract features from the input data.\n\n3. **Training**: Deep learning models are trained on large datasets using algorithms like backpropagation and gradient descent. During training, the model adjusts the weights of the connections between neurons to minimize the difference between its predictions and the actual outcomes.\n\n4. **Feature Learning**: One of the key advantages of deep learning is its ability to automatically learn features from raw data. This is particularly useful in domains like image and speech recognition, where manual feature extraction can be challenging.\n\n5. **Applications**: Deep learning has a wide range of applications, including image and video recognition, natural language processing, speech recognition, autonomous vehicles, and more.\n\n6. **Challenges**: Despite its successes, deep learning also faces challenges, such as the need for large amounts of data, high computational resources, and the difficulty of interpreting the decisions made by complex models (often referred to as the \"black box\" problem).\n\n7. **Frameworks**: Popular deep learning frameworks include TensorFlow, PyTorch, Keras, and Caffe, which provide tools and libraries to build, train, and deploy deep learning models efficiently.\n\nIn summary, deep learning is a powerful tool for solving complex problems by learning from data, and it has revolutionized many fields by enabling machines to perform tasks that were previously thought to be too complex for automation.",
        "tool_calls": [],
        "usage": {
            "completion_tokens": 416,
            "prompt_tokens": 24,
            "total_tokens": 440
        }
    },
    "success": true
}

You can include additional hyperparameters as optional settings to achieve more fine-grained output:

max_tokens	The maximum number of tokens to generate in the response
temperature	Default 0.6 minimum 0 and maximum 5
top_p	minimum 0 and maximum 2
top_k	minimum 1 and maximum 50
seed	minimum 1 and maximum 9999999999
repetition_penalty	minimum 0 and maximum 2
frequency_penalty	minimum 0 and maximum 2
presence_penalty	minimum 0 and maximum 2