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