Qwen LogoQwen 2.5 14B Instruct

A midsize model in the Qwen family of LLMs

Deploy Qwen 2.5 14B Instruct behind an API endpoint in seconds.

Deploy model

Example usage

Qwen uses the standard llama-style multi-turn messaging framework with system and user prompts.

Input
1import requests
2
3# Replace the empty string with your model id below
4model_id = ""
5baseten_api_key = os.environ["BASETEN_API_KEY"]
6
7data = {
8    "messages": [
9        {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
10        {"role": "user", "content": "What does Tongyi Qianwen mean?"},
11    ]
12    "stream": True,
13    "max_new_tokens": 512,
14    "temperature": 0.9
15}
16
17# Call model endpoint
18res = requests.post(
19    f"https://model-{model_id}.api.baseten.co/production/predict",
20    headers={"Authorization": f"Api-Key {baseten_api_key}"},
21    json=data,
22    stream=True
23)
24
25# Print the generated tokens as they get streamed
26for content in res.iter_content():
27    print(content.decode("utf-8"), end="", flush=True)
JSON output
1[
2    "streaming",
3    "output",
4    "text"
5]

Deploy any model in just a few commands

Avoid getting tangled in complex deployment processes. Deploy best-in-class open-source models and take advantage of optimized serving for your own models.

$

truss init -- example stable-diffusion-2-1-base ./my-sd-truss

$

cd ./my-sd-truss

$

export BASETEN_API_KEY=MdNmOCXc.YBtEZD0WFOYKso2A6NEQkRqTe

$

truss push

INFO

Serializing Stable Diffusion 2.1 truss.

INFO

Making contact with Baseten 👋 👽

INFO

🚀 Uploading model to Baseten 🚀

Upload progress: 0% | | 0.00G/2.39G