mirror of
https://github.com/meta-llama/llama-stack.git
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56 lines
3.9 KiB
JSON
56 lines
3.9 KiB
JSON
{
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"request": {
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"method": "POST",
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"url": "http://localhost:11434/v1/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "llama3.2:3b-instruct-fp16",
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"messages": [
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{
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"role": "user",
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"content": "Test trace openai 0"
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}
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],
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"endpoint": "/v1/chat/completions",
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"model": "llama3.2:3b-instruct-fp16"
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"message": {
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"content": "I'm afraid I don't have a built-in ability to directly interface with or \"test\" OpenAI models, including the original GPT-1 model. However, I can explain how you might approach this task:\n\nThe OpenAI GPT-1 is a large transformer-based language model that was trained on a massive dataset of text and achieved state-of-the-art results in various natural language processing tasks.\n\nTo test or evaluate the performance of a model like GPT-1, you would typically follow these steps:\n\n1. **Get access to the OpenAI API**: The OpenAI API provides a way for developers to interact with the GPT-1 model programmatically. You can sign up for an API key on the OpenAI website.\n2. **Choose a testing platform or environment**: You'll need a compute platform that supports the necessary algorithms and data structures to run inference on the GPT-1 model. Some popular options include AWS, Google Cloud, or Azure Compute Virtual Machines.\n3. **Prepare your test input data**: This will involve creating text inputs in the format expected by the OpenAI API (i.e., a JSON object containing the text to be processed).\n4. **Use the OpenAI Python library or SDK**: The OpenAI Python library provides an easy-to-use interface for interacting with the GPT-1 model through the API.\n\nHere's some example code that demonstrates how you might use the OpenAI Flask API to test a single input:\n\n```python\nfrom flask import Flask, request, jsonify\nimport json\n\napp = Flask(__name__)\n\n@ app . route ( '/ /gpt-en ', ' Text ', methods = ['POST'])\ndef gpt_en () -> Json :\n data = request . get_json ()\n if not data or \"message\" in ( data ):\n return None , 400 , { ' error' : \"Input must be a text string.\" }\n response = []\n while True:\n message = \"\"\n for token in data [\"input\"]:\n response_text = f\"{data['prompt']} {token}\"\n data[\"input\"] = [response_text]\n new_response = gpt_en()(data)\n if all([not item or not isinstance(item, dict) for item in new_response]):\n break\n\n message = json . dumps ({}\"text\": response_text})\n response.append(message)\n\n return jsonify ({\"output\": response}), 200 , {}\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n```\n\n5. **Evaluate the output**: Once you have processed your test input data using the GPT-1 model, you can evaluate the accuracy of the generated responses.\n\nKeep in mind that this is just a basic example to illustrate how you might approach testing the OpenAI GPT-1 model.",
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