Examples
This page contains different examples on how you can use the LLMs.
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)