langchain/libs/community/tests/integration_tests/llms/test_mlx_pipeline.py
Prince Canuma 1f9f4d8742
community[minor]: Add support for MLX models (chat & llm) (#18152)
**Description:** This PR adds support for MLX models both chat (i.e.,
instruct) and llm (i.e., pretrained) types/
**Dependencies:** mlx, mlx_lm, transformers
**Twitter handle:** @Prince_Canuma

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
2024-04-09 14:17:07 +00:00

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"""Test MLX Pipeline wrapper."""
from langchain_community.llms.mlx_pipeline import MLXPipeline
def test_mlx_pipeline_text_generation() -> None:
"""Test valid call to MLX text generation model."""
llm = MLXPipeline.from_model_id(
model_id="mlx-community/quantized-gemma-2b",
pipeline_kwargs={"max_tokens": 10},
)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def test_init_with_model_and_tokenizer() -> None:
"""Test initialization with a HF pipeline."""
from mlx_lm import load
model, tokenizer = load("mlx-community/quantized-gemma-2b")
llm = MLXPipeline(model=model, tokenizer=tokenizer)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
def test_huggingface_pipeline_runtime_kwargs() -> None:
"""Test pipelines specifying the device map parameter."""
llm = MLXPipeline.from_model_id(
model_id="mlx-community/quantized-gemma-2b",
)
prompt = "Say foo:"
output = llm.invoke(prompt, pipeline_kwargs={"max_tokens": 2})
assert len(output) < 10