Tasks and inputs
Apple LensVLM 9B is listed for Document question answering, Image-text-to-text. It accepts text, image and produces text. Use the publisher’s input format and preprocessing for your chosen deployment.
SyncDev model directory · Profile updated Oct 7, 2026
Apple LensVLM 9B is a research vision-language model for reading compressed visual representations of long text.
The specification score summarizes recorded features and limits. It is not a benchmark result.
Recorded modality support shows what this model can accept and produce. An undocumented modality is shown as unknown rather than assumed to be unsupported.
Accepts
Produces
Apple LensVLM 9B is a research vision-language model for reading compressed visual representations of long text.
LensVLM is built on a 9B Qwen vision-language backbone. The research workflow renders text into compressed page images and uses learned expansion tools to revisit relevant material at higher detail. This combines a model checkpoint with a document-processing and tool pipeline; loading the language model alone does not reproduce the complete method.
Use a long document with questions requiring both a small factual detail and a relationship across distant sections. Keep the original text and the rendered pages together. Check whether the answer identifies the correct evidence and whether expansion selects the pages containing it. Test dense tables, unusual typography and text near page boundaries separately from clean paragraphs.
The checkpoint is a research release under Apple’s Machine Learning Research Model License. The code has separate terms, so review both before choosing a deployment. The model card does not establish a general Apple consumer API, a hosted price or a universal context limit for every rendering configuration.
Compare the complete pipeline against ordinary text retrieval using the same documents and questions. Include preprocessing time, page-image storage, tool calls and failed answers in the result. For an application that needs exact quotations or numeric extraction, verify the returned passage against the original document rather than accepting a visually plausible reading of compressed text.
Compare the model on the work you need to complete.
Apple LensVLM 9B is listed for Document question answering, Image-text-to-text. It accepts text, image and produces text. Use the publisher’s input format and preprocessing for your chosen deployment.
Measure a completed task using the prompts, documents and tools your application will use. Include reasoning and retries in the result.
The weights are downloadable under Apple Machine Learning Research Model License. Check runtime support and size the complete model and working context for your expected concurrency.
These records share a directory category. Check the source card, output type and deployment requirements before treating a model as a replacement.
2KKLabs
2KKLabs/Kaleidoscope_large_v1 is a source-linked Hugging Face repository assigned to Document question answering. Its recorded task interface accepts document, text and produces text.
2KKLabs
Hugging Face repository for Document question answering.
ahirtonlopes
Hugging Face repository for Document question answering.
am-infoweb
am-infoweb/layoutlmv2-finetuned_docvqa is a source-linked Hugging Face repository assigned to Document question answering. Its recorded task interface accepts document, text and produces text.
am-infoweb
am-infoweb/layoutlmv3-finetuned_docvqa is a source-linked Hugging Face repository assigned to Document question answering. Its recorded task interface accepts document, text and produces text.
AntonioTH
Hugging Face repository for Document question answering.