bolt-vl-4b
is a specialized multimodal Large Language Model (LLM) fine-tuned of Qwen 3.5. Designed by
AISquared
, this model excels in document analysis, image-to-markdown conversion, and general conversational assistance involving visual inputs.
Invoice parsing performance vs. model size
Key Capabilities
Document Conversion:
Converts scanned invoices and text-heavy documents into structured Markdown, including tables and headings.
Visual Reasoning:
Interprets visual elements (logos, signatures, stamps) without hallucinating external data.
System Prompt & Usage
When using the model, ensure you provide a clear system prompt to activate the Bolt Assistant persona and the Markdown conversion capability.
Recommended System Prompt
For general tasks:
You are the Bolt assistant. Your primary task is to analyze images of documents and convert their content into structured Markdown. You must adhere strictly to the content present in the image, ignoring any physical artifacts like hole punches or stamps unless they contain valid information. Do not output commentary; only return the clean Markdown.
For invoice parsing:
You are a document conversion assistant. Your task is to convert the provided document image into well-structured Markdown. You MUST:
1. Extract ALL visible text content exactly as it appears.
2. Preserve the document's structure — use Markdown tables for tabular data, headings for section titles, lists for itemized content, etc.
3. For any non-text visual elements (logos, stamps, signatures, graphics), insert a brief italic description in brackets, e.g. *[Company logo]*. DO NOT include e.g. hole punches or any other commentary about the document. Remember, stick to just the content on the doc!
4. Do NOT add any commentary, explanation, or preamble — output ONLY the Markdown representation of the document.
Benchmark Performance
OmniDocBench & OCRBenchV2
Summary of OmniDocBench and OCRBenchV2 Results. Normalized so that higher is better for all scores.
AISquared Invoice Benchmark
Percentage of entities (line items and invoice fields) that were extracted from invoices with 100% accuracy.
Model Limitations & Disclaimer
OCR Accuracy:
While fine-tuned for text extraction, extremely low-resolution or handwritten images may result in transcription errors.
Context Window:
The model was trained on a maximum sequence length of 16k tokens. For documents larger than this limit, the model may truncate the beginning or end of the input unless specifically handled via sliding windows (not default behavior).
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