import pytest import requests @pytest.fixture(scope="module") def lora_mistral_handle(launcher): with launcher( "mistralai/Mistral-7B-v0.1", lora_adapters=[ "predibase/dbpedia", "predibase/customer_support", ], ) as handle: yield handle @pytest.fixture(scope="module") async def lora_mistral(lora_mistral_handle): await lora_mistral_handle.health(300) return lora_mistral_handle.client @pytest.mark.asyncio @pytest.mark.private async def test_lora_mistral(lora_mistral, response_snapshot): response = await lora_mistral.generate( "What is Deep Learning?", max_new_tokens=10, decoder_input_details=True ) assert ( response.generated_text == "\n\nDeep learning is a subset of machine learning" ) assert response.details.generated_tokens == 10 assert response == response_snapshot classification_prompt = """You are given the title and the body of an article below. Please determine the type of the article.\n### Title: Great White Whale\n\n### Body: Great White Whale is the debut album by the Canadian rock band Secret and Whisper. The album was in the works for about a year and was released on February 12 2008. A music video was shot in Pittsburgh for the album's first single XOXOXO. The album reached number 17 on iTunes's top 100 albums in its first week on sale.\n\n### Article Type:""" @pytest.mark.asyncio @pytest.mark.private async def test_lora_mistral_without_adapter(lora_mistral, response_snapshot): response = requests.post( f"{lora_mistral.base_url}/generate", json={ "inputs": classification_prompt, "parameters": { "max_new_tokens": 40, "details": True, }, }, ) assert response.status_code == 200 data = response.json() assert ( data["generated_text"] == "\n\n### 1. News\n### 2. Blog\n### 3. Article\n### 4. Review\n### 5. Other\n\n\n\n\n\n\n\n\n" ) assert data == response_snapshot @pytest.mark.asyncio @pytest.mark.private async def test_lora_mistral_with_dbpedia_adapter(lora_mistral, response_snapshot): response = requests.post( f"{lora_mistral.base_url}/generate", json={ "inputs": classification_prompt, "parameters": { "max_new_tokens": 40, "adapter_id": "predibase/dbpedia", "details": True, }, }, ) assert response.status_code == 200 data = response.json() assert data["generated_text"] == " 11" assert data == response_snapshot @pytest.mark.asyncio @pytest.mark.private async def test_lora_mistral_with_customer_support_adapter( lora_mistral, response_snapshot ): prompt = """Consider the case of a customer contacting the support center.\nThe term "task type" refers to the reason for why the customer contacted support.\n\n### The possible task types are: ### \n- replace card\n- transfer money\n- check balance\n- order checks\n- pay bill\n- reset password\n- schedule appointment\n- get branch hours\n- none of the above\n\nSummarize the issue/question/reason that drove the customer to contact support:\n\n### Transcript: [noise] [noise] [noise] [noise] hello hello hi i'm sorry this this call uh hello this is harper valley national bank my name is dawn how can i help you today hi oh okay my name is jennifer brown and i need to check my account balance if i could [noise] [noise] [noise] [noise] what account would you like to check um [noise] uhm my savings account please [noise] [noise] oh but the way that you're doing one moment hello yeah one moment uh huh no problem [noise] your account balance is eighty two dollars is there anything else i can help you with no i don't think so thank you so much you were very helpful thank you have a good day bye bye [noise] you too \n\n### Task Type:\n\ntest_transcript = """ response = requests.post( f"{lora_mistral.base_url}/generate", json={ "inputs": prompt, "parameters": { "max_new_tokens": 40, "adapter_id": "predibase/customer_support", "details": True, }, }, ) assert response.status_code == 200 data = response.json() assert data["generated_text"] == " check balance" assert data == response_snapshot response = requests.post( f"{lora_mistral.base_url}/generate", json={ "inputs": prompt, "parameters": { "max_new_tokens": 40, # "adapter_id": "predibase/customer_support", }, }, ) assert response.status_code == 200 data = response.json() assert ( data["generated_text"] == "\n\n### Transcript: [noise] [noise] [noise] [noise] hello hello hi i'm sorry this this call uh hello this is" )