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
| """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 | |
| 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 | |