README examples surfaced two gaps in the port:

- Image inputs passed as https:// URLs (the embedding/reranker README
  examples) hit `FileNotFoundError` because `_to_numpy_image` treated
  every string as a local path. Detect URLs and fetch via requests.

- `Qwen/Qwen3-VL-Reranker-2B` ships its chat template in
  chat_template.jinja, not in tokenizer_config.json. Add a
  `_load_qwen_vl_text` helper (local-then-Hub) and fall back to it when
  neither processor_config.json nor the tokenizer carries a template.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

Both helpers did the same local-then-Hub read; only the parsing differed.
Unify as _load_qwen_vl_file that dispatches on the .json suffix.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

The folder name already scopes these, so the prefix is noise:
- _load_qwen_vl_file  -> _load_file
- _qwen_vl_image_kwargs -> _image_kwargs
- _qwen_vl_video_kwargs -> _video_kwargs

Classes keep their qualified Qwen3VL* names since they're the module's
public surface.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

On the 6-query × 6-image retrieval benchmark, the mlx-embeddings output
had max|cosine diff| = 0.087 vs HF transformers reference and only 83%
top-1 agreement. Three fixes close the gap to max 0.006 diff and 100%
top-1/top-3 agreement:

1. Forward the embedder's MIN_PIXELS/MAX_PIXELS (4096..1,843,200) onto
   the inner image_processor. The Qwen3-VL preprocessor_config.json
   lists the full-context size bounds (16 MP), so without this override
   the image_processor resized to a different grid than the HF reference
   and the comparison ran on different visual tokens.

2. Work around mlx-vlm bug in Qwen3-VL get_input_embeddings: the
   upstream assigns `mx.eval(deepstack_image_embeds)` to
   `deepstack_visual_embeds`, but mx.eval returns None — so multi-scale
   deepstack features were silently dropped at every LM layer the
   model was supposed to inject them into. Re-run the vision tower in
   our Model.get_input_embeddings when we detect this.

3. Patch mlx-vlm's `_deepstack_process` on the language-model instance:
   upstream indexes the full concatenated visual_embeds at each batch
   sample's image positions, which only works for batch_size=1. Our
   patched version slices visual_embeds per sample using a running
   offset so multi-image batches work.

Once (2) is fixed upstream, (3) surfaces immediately — they're stacked
bugs that cancel for single-image batches.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

…ugs"

This reverts commit 45a501c.

Replaces the toy "embed 4 mixed inputs and print a 4x4 similarity"
snippet with a real retrieval workflow: embed an image gallery once,
score multiple text queries, and rank top-K per query.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>