Text Classification
PEFT
lora
document-question-answering
structured-decisions
calibration
synthetic-evaluation
Instructions to use botp/Solomon with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use botp/Solomon with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 853 Bytes
1d2de8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | """Pinned tokenizer/image sidecars can be tested before large weights arrive."""
from pathlib import Path
import numpy as np
import pytest
from mlx_vlm.models.qwen3_vl.processing_qwen3_vl import Qwen3VLProcessor
from PIL import Image
@pytest.mark.skipif(
not Path("snapshots/base/tokenizer.json").exists(), reason="Pinned tokenizer not downloaded"
)
def test_torch_free_processor_and_image_token_count():
processor = Qwen3VLProcessor.from_pretrained("snapshots/base", trust_remote_code=False)
image = Image.new("RGB", (512, 512), "white")
out = processor.image_processor(images=[image])
assert out["image_grid_thw"].tolist() == [[1, 32, 32]]
assert np.asarray(out["pixel_values"]).shape == (1024, 1536)
assert np.isfinite(np.asarray(out["pixel_values"])).all()
assert processor.image_processor.max_pixels == 16777216
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