61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
import pytest
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@pytest.fixture(scope="module")
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def flash_starcoder_gptq_handle(launcher):
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with launcher("Narsil/starcoder-gptq", num_shard=2, quantize="gptq") as handle:
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yield handle
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@pytest.fixture(scope="module")
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async def flash_starcoder_gptq(flash_starcoder_gptq_handle):
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await flash_starcoder_gptq_handle.health(300)
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return flash_starcoder_gptq_handle.client
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_starcoder_gptq(flash_starcoder_gptq, generous_response_snapshot):
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response = await flash_starcoder_gptq.generate(
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"def geometric_mean(L: List[float]):",
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max_new_tokens=20,
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decoder_input_details=True,
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)
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assert response.details.generated_tokens == 20
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assert response == generous_response_snapshot
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_starcoder_gptq_default_params(
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flash_starcoder_gptq, generous_response_snapshot
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):
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response = await flash_starcoder_gptq.generate(
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"def geometric_mean(L: List[float]):",
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max_new_tokens=20,
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temperature=0.2,
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top_p=0.95,
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decoder_input_details=True,
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seed=0,
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)
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assert response.details.generated_tokens == 20
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assert response == generous_response_snapshot
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@pytest.mark.asyncio
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@pytest.mark.private
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async def test_flash_starcoder_gptq_load(
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flash_starcoder_gptq, generate_load, generous_response_snapshot
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):
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responses = await generate_load(
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flash_starcoder_gptq,
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"def geometric_mean(L: List[float]):",
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max_new_tokens=10,
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n=4,
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)
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assert len(responses) == 4
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assert all([r.generated_text == responses[0].generated_text for r in responses])
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assert responses == generous_response_snapshot
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