Examples
This page provides several examples showing how to use the LLMs and API endpoints, whereas the case studies here reference the API endpoint https://llm.hpc.itc.rwth-aachen.de.
More examples (e.g. for vLLM or Ollama) can be found in our Example Collection.
Querying model list
The following example shows how to query available models with curl:
curl https://llm.hpc.itc.rwth-aachen.de/v1/models \
-H "Authorization: Bearer YOUR-API-KEY"Simple completions
The following examples show how to perform a simple completion.
Example for curl:
curl https://llm.hpc.itc.rwth-aachen.de/v1/completions \
-H "Authorization: Bearer YOUR-API-KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"prompt": "San Francisco is a",
"max_tokens": 300
}'Example for OpenAI Python SDK:
import openai
client = openai.OpenAI(
base_url="https://llm.hpc.itc.rwth-aachen.de",
api_key="YOUR-API-KEY"
)
response = client.completions.create(
model="mistralai/Mistral-Small-3.2-24B-Instruct-2506",
prompt="San Francisco is a",
max_tokens=300
)
print(response)Example for Langchain Py:
from langchain_openai import ChatOpenAI, OpenAI
llm = OpenAI(
base_url="https://llm.hpc.itc.rwth-aachen.de",
api_key="YOUR-API-KEY",
model="mistralai/Mistral-Small-3.2-24B-Instruct-2506",
max_tokens=300
)
prompt = "San Francisco is a"
result = llm.invoke(prompt)
print(result)Chat completions
The following examples show how to perform a chat completion.
Example for curl:
curl -X POST https://llm.hpc.itc.rwth-aachen.de/v1/chat/completions \
-H "Authorization: Bearer YOUR-API-KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "San Francisco is a"}
],
"max_tokens": 300
}'Example for OpenAI Python SDK:
import openai
client = openai.OpenAI(
base_url="https://llm.hpc.itc.rwth-aachen.de",
api_key="YOUR-API-KEY"
)
response = client.chat.completions.create(
model="mistralai/Mistral-Small-3.2-24B-Instruct-2506",
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "San Francisco is a"}
],
max_tokens=300
)
print(response)Example for Langchain Py:
from langchain_openai import ChatOpenAI, OpenAI
llm = ChatOpenAI(
base_url="https://llm.hpc.itc.rwth-aachen.de",
api_key="YOUR-API-KEY",
model="mistralai/Mistral-Small-3.2-24B-Instruct-2506",
max_tokens=300
)
messages = [
("system", "You are a helpful assistant."),
("human", "San Francisco is a"),
]
result = llm.invoke(messages)
print(result.content)