llama-stack-mirror/tests/integration/recordings/responses/d4f56d7d1996.json
Ashwin Bharambe 3f36bfaeaa
chore(tests): normalize recording IDs and timestamps to reduce git diff noise (#3676)
IDs are now deterministic hashes based on request content, and
timestamps are normalized to constants, eliminating spurious changes
when re-recording tests.

## Changes
- Updated `inference_recorder.py` to normalize IDs and timestamps during
recording
- Added `scripts/normalize_recordings.py` utility to re-normalize
existing recordings
- Created documentation in `tests/integration/recordings/README.md`
- Normalized 350 existing recording files
2025-10-03 17:26:11 -07:00

56 lines
3.3 KiB
JSON

{
"request": {
"method": "POST",
"url": "http://localhost:11434/v1/v1/completions",
"headers": {},
"body": {
"model": "llama3.2:3b-instruct-fp16",
"messages": [
{
"role": "user",
"content": "Test trace openai 2"
}
],
"stream": false
},
"endpoint": "/v1/completions",
"model": "llama3.2:3b-instruct-fp16"
},
"response": {
"body": {
"__type__": "openai.types.chat.chat_completion.ChatCompletion",
"__data__": {
"id": "rec-d4f56d7d1996",
"choices": [
{
"finish_reason": "stop",
"index": 0,
"logprobs": null,
"message": {
"content": "I'd be happy to help you test the OpenAI 2 model, also known as GPT-2. Keep in mind that I'll be providing information and guidance based on publicly available resources, and not directly testing the model myself.\n\nOpenAI 2 is a large language model developed by OpenAI Research, which was released in 2019. It's a transformer-based model with 1.5 billion parameters, making it one of the largest language models at that time.\n\nTo test the OpenAI 2 model, you can try the following:\n\n1. **Read the paper**: Start by reading the original paper published in the ArXiv preprint repository [1]. This will give you a deeper understanding of the model's architecture and capabilities.\n2. **Use online generators**: Websites like [2] and [3] provide interactive interfaces to test and generate text using the OpenAI 2 model.\n3. **Try code examples**: You can find code examples in various programming languages, such as Python, that demonstrate how to use the OpenAI 2 model for tasks like text processing and generation.\n\nSome specific things you might want to try when testing OpenAI 2 include:\n\n* Generating coherent paragraphs on a given topic\n* Answering questions based on context\n* Completing sentences or stories with missing information\n* Translating short texts from one language to another\n\nKeep in mind that the OpenAI 2 model is quite large and computationally intensive, so it might not be suitable for use on all devices or platforms.\n\nReferences:\n\n[1] Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2019). Improving Language Understanding by Generative Pre-Training. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL).\n\n[2] https://beta.openai.com/ (use the \"chat\" interface to interact with the OpenAI 2 model)\n\n[3] https://gpt2-test.openai.co/ (test a demo version of the OpenAI 2 model)\n\nI hope this helps! If you have any specific questions or need further guidance, feel free to ask.",
"refusal": null,
"role": "assistant",
"annotations": null,
"audio": null,
"function_call": null,
"tool_calls": null
}
}
],
"created": 0,
"model": "llama3.2:3b-instruct-fp16",
"object": "chat.completion",
"service_tier": null,
"system_fingerprint": "fp_ollama",
"usage": {
"completion_tokens": 450,
"prompt_tokens": 31,
"total_tokens": 481,
"completion_tokens_details": null,
"prompt_tokens_details": null
}
}
},
"is_streaming": false
}
}