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51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
"""Test HuggingFace Pipeline wrapper."""
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from pathlib import Path
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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from langchain.llms.huggingface_pipeline import HuggingFacePipeline
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from langchain.llms.loading import load_llm
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from tests.integration_tests.llms.utils import assert_llm_equality
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def test_huggingface_pipeline_text_generation() -> None:
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"""Test valid call to HuggingFace text generation model."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="gpt2", task="text-generation", model_kwargs={"max_new_tokens": 10}
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)
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output = llm("Say foo:")
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assert isinstance(output, str)
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def test_huggingface_pipeline_text2text_generation() -> None:
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"""Test valid call to HuggingFace text2text generation model."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="google/flan-t5-small", task="text2text-generation"
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)
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output = llm("Say foo:")
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assert isinstance(output, str)
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def test_saving_loading_llm(tmp_path: Path) -> None:
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"""Test saving/loading an HuggingFaceHub LLM."""
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llm = HuggingFacePipeline.from_model_id(
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model_id="gpt2", task="text-generation", model_kwargs={"max_new_tokens": 10}
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)
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llm.save(file_path=tmp_path / "hf.yaml")
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loaded_llm = load_llm(tmp_path / "hf.yaml")
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assert_llm_equality(llm, loaded_llm)
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def test_init_with_pipeline() -> None:
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"""Test initialization with a HF pipeline."""
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model_id = "gpt2"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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pipe = pipeline(
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"text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
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)
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llm = HuggingFacePipeline(pipeline=pipe)
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output = llm("Say foo:")
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assert isinstance(output, str)
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