Instructions to use Respair/NeMo_Canary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use Respair/NeMo_Canary with NeMo:
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- Notebooks
- Google Colab
- Kaggle
Download tests/evaluation/adapters/test_utils.py from Respair/NeMo_Canary: direct link, hf CLI and curl.
- Browser
- Download file 1.99 kB
-
https://huggingface.co/Respair/NeMo_Canary/resolve/main/tests/evaluation/adapters/test_utils.py
- Command line
-
hf download hf://Respair/NeMo_Canary/tests/evaluation/adapters/test_utils.py
-
curl -L -o test_utils.py https://huggingface.co/Respair/NeMo_Canary/resolve/main/tests/evaluation/adapters/test_utils.py
1.99 kB
| # Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Utilities for adapter testing.""" | |
| import multiprocessing | |
| import pytest | |
| from flask import Flask, jsonify, request | |
| from nemo.collections.llm.evaluation.adapters.utils import wait_for_server | |
| DEFAULT_FAKE_RESPONSE = { | |
| "object": "chat.completion", | |
| "choices": [ | |
| { | |
| "message": { | |
| "role": "assistant", | |
| "content": "This is a fake LLM response</think>This survives reasoning", | |
| } | |
| } | |
| ], | |
| } | |
| def create_and_run_fake_endpoint(): | |
| """Create and run a fake OpenAI API endpoint.""" | |
| app = Flask(__name__) | |
| def chat_completion(): | |
| data = request.json | |
| if "fake_response" in data: | |
| response = data["fake_response"] | |
| else: | |
| response = DEFAULT_FAKE_RESPONSE | |
| return jsonify(response) | |
| app.run(host="localhost", port=3300) | |
| def create_fake_endpoint_process(): | |
| """Create a process running a fake OpenAI endpoint. | |
| Returns: | |
| The multiprocessing.Process object running the endpoint. | |
| """ | |
| p = multiprocessing.Process(target=create_and_run_fake_endpoint) | |
| p.start() | |
| # Wait for the server to be ready | |
| if not wait_for_server("localhost", 3300): | |
| p.terminate() | |
| pytest.fail("Fake OpenAI endpoint did not start within the timeout period") | |
| return p | |