Comparez et testez des modèles open weight pour le texte, le code, la voix et la vision, sans inscription. Passez ensuite en production avec la même API compatible OpenAI.
Le modèle Mistral-Nemo-Instruct-2407, développé en collaboration par Mistral AI et NVIDIA, est un LLM ajusté pour les instructions publié en 2024. Conçu pour les applications multilingues, il excelle dans des tâches telles que le dialogue conversationnel, la génération de code et la compréhension instructionnelle dans diverses langues.
First install the requests library:
pip install requestsNext, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.
Finally, run the following Python code:
import os
import requests
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}",
}
# With /chat/completions
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/chat/completions"
payload = {
"max_tokens": 512,
"messages": [
{
"content": "Explain gravity to a 6 years old",
"role": "user"
}
],
"model": "Mistral-Nemo-Instruct-2407",
"temperature": 0,
}
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
# Handle response
response_data = response.json()
# Parse JSON response
choices = response_data["choices"]
for choice in choices:
text = choice["message"]["content"]
# Process text and finish_reason
print(text)
else:
print("Error:", response.status_code, response.text)
# With /responses
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/responses"
payload = {
"max_output_tokens": 512,
"input": [
{
"content": "Explain gravity to a 6 years old",
"role": "user"
}
],
"store": False,
"model": "Mistral-Nemo-Instruct-2407",
"temperature": 0,
}
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
# Handle response
response_data = response.json()
text = response_data["output"][0]["content"][0]["text"]
print(text)
else:
print("Error:", response.status_code, response.text)The Mistral-Nemo-Instruct-2407 API is compatible with the OpenAI specification.
First install the openai library:
pip install openaiNext, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>Finally, run the following Python code:
import os
from openai import OpenAI
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"
client = OpenAI(
base_url=url,
api_key=os.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN")
)
def chat_completion(new_message: str) -> str:
history_openai_format = [{"role": "user", "content": new_message}]
return client.chat.completions.create(
model="Mistral-Nemo-Instruct-2407",
messages=history_openai_format,
temperature=0,
max_tokens=1024
).choices.pop().message.content
def responses(new_message: str) -> str:
response = client.responses.create(
model="Mistral-Nemo-Instruct-2407",
input=new_message,
temperature=0,
max_output_tokens=1024,
store=False
)
return response.output[0].content[0].text
if __name__ == '__main__':
# With chat completion endpoint
print(chat_completion("Explain gravity for a 6 years old"))
# With responses endpoint
print(responses("Explain gravity for a 12 years old"))The Batch API (/v1/batches) lets you submit a large number of inference requests in a single asynchronous job, instead of sending them one by one through our synchronous endpoints. Batch mode is ideal when you do not need an immediate answer but want to process a high volume of prompts (evaluations, offline labelling, content generation at scale, dataset preparation, etc...).
Batch mode requests are billed at 50% off compared to directly calling the generation endpoints (/v1/chat/completions or /v1/responses).
Results are delivered within a maximum of 24 hours after the batch is submitted.
The workflow follows the OpenAI Batch API specification:
Install the OpenAI library and export your access token:
pip install openai
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>The example below builds a JSONL file, runs the full batch lifecycle, and prints each response next to its custom_id:
import json
import os
import time
from openai import OpenAI
client = OpenAI(
base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
api_key=os.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN"),
)
# 1. Build the JSONL input file
prompts = [
"What's the capital of France?",
"What's the capital of Québec?",
]
with open("batch_input.jsonl", "w") as f:
for i, prompt in enumerate(prompts):
f.write(json.dumps({
"custom_id": f"request-{i}",
"method": "POST",
"url": "/v1/chat/completions",
"body": {
"model": "Mistral-Nemo-Instruct-2407",
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 512,
},
}) + "\n")
# 2. Upload it
input_file = client.files.create(
file=open("batch_input.jsonl", "rb"),
purpose="batch",
)
# 3. Create the batch
batch = client.batches.create(
input_file_id=input_file.id,
endpoint="/v1/chat/completions",
completion_window="24h",
)
print(f"Batch created: {batch.id}")
# 4. Poll until the batch reaches a terminal state
terminal = {"completed", "failed", "expired", "cancelled"}
while batch.status not in terminal:
time.sleep(30)
batch = client.batches.retrieve(batch.id)
print(f"Status: {batch.status}")
# 5. Download and parse the results
if batch.status == "completed":
output = client.files.content(batch.output_file_id).text
for line in output.strip().splitlines():
result = json.loads(line)
custom_id = result["custom_id"]
content = result["response"]["body"]["choices"][0]["message"]["content"]
print(f"{custom_id}: {content}")
else:
print(f"Batch finished with status: {batch.status}")You can have more details on the dedicated Batch Mode documentation.
When using AI Endpoints, the following rate limits apply:
If you exceed this limit, a 429 error code will be returned.
If you require higher usage, please get in touch with us to discuss increasing your rate limits.
Want to explore the full capabilities of the LLM API? Dive into our dedicated Structured Output and Function Calling guides.
For a broader overview of AI Endpoints, explore the full AI Endpoints Documentation.
Reach out to our support team or join the OVHcloud Discord #ai-endpoints channel to share your questions, feedback, and suggestions for improving the service, to the team and the community.
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