61 lines
1.6 KiB
Python
61 lines
1.6 KiB
Python
import openai # use the official client for correctness check
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import pytest
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# downloading lora to test lora requests
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from huggingface_hub import snapshot_download
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from ...utils import RemoteOpenAIServer
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# any model with a chat template should work here
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MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
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# technically this needs Mistral-7B-v0.1 as base, but we're not testing
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# generation quality here
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LORA_NAME = "typeof/zephyr-7b-beta-lora"
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@pytest.fixture(scope="module")
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def zephyr_lora_files():
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return snapshot_download(repo_id=LORA_NAME)
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@pytest.fixture(scope="module")
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def server(zephyr_lora_files):
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args = [
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# use half precision for speed and memory savings in CI environment
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"--dtype",
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"bfloat16",
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"--max-model-len",
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"8192",
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"--enforce-eager",
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# lora config below
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"--enable-lora",
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"--lora-modules",
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f"zephyr-lora={zephyr_lora_files}",
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f"zephyr-lora2={zephyr_lora_files}",
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"--max-lora-rank",
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"64",
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"--max-cpu-loras",
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"2",
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"--max-num-seqs",
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"128",
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]
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with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
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yield remote_server
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@pytest.fixture(scope="module")
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def client(server):
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return server.get_async_client()
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@pytest.mark.asyncio
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async def test_check_models(client: openai.AsyncOpenAI):
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models = await client.models.list()
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models = models.data
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served_model = models[0]
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lora_models = models[1:]
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assert served_model.id == MODEL_NAME
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assert all(model.root == MODEL_NAME for model in models)
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assert lora_models[0].id == "zephyr-lora"
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assert lora_models[1].id == "zephyr-lora2"
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