Ovisocr2 Mxfp8 Mlx

--- license: apache-2.0 base_model: ATH-MaaS/OvisOCR2 base_model_relation: quantized pipeline_tag: image-text-to-text library_name: mlx tags: - mlx - mlx-vlm - quantized - apple-silicon - qwen3_5 - ocr - document-parsing - markdown - tables - formulas language: - en - zh --- # ovisocr2-mxfp8-mlx MLX MX FP8 (micro-scaled 8-bit float) quantization of [ATH-MaaS/OvisOCR2](https://huggingface.co/ATH-MaaS/OvisOCR2), converted with [mlx-vlm](https://github.com/Blaizzy/mlx-vlm). **OvisOCR2** is a compact 0.8B end-to-end document-parsing model (Qwen3.5-0.8B backbone). Given a document page image, it generates a Markdown transcription in natural reading order — text, formulas (LaTeX), tables (HTML), and visual regions (bounding-box image tags). It scores 96.58 on OmniDocBench v1.6, state of the art for an end-to-end model, and an Avg3 of 75.06 on PureDocBench. ## Architecture | | | |---|---| | Base model | OvisOCR2 (Qwen3.5-VL arch, `Qwen3_5ForConditionalGeneration`, ~0.8B params) | | Quantization | MX FP8 (micro-scaled 8-bit float) | | Bits per weight | 9.168 | | Disk size | 958 MB (from ~1.6 GB bf16) | | Format | MLX (safetensors) | ## Usage ```python from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config model_path = "sahilchachra/ovisocr2-mxfp8-mlx" model, processor = load(model_path, trust_remote_code=True) config = load_config(model_path, trust_remote_code=True) prompt = ( "\nExtract all readable content from the image in natural human reading order " "and output the result as a single Markdown document. For charts or images, " 'represent them using an HTML image tag: <img src="images/bbox_{left}_{top}_{right}_{bottom}.jpg" />, ' "where left, top, right, bottom are bounding box coordinates scaled to [0, 1000). " "Format formulas as LaTeX. Format tables as HTML: <table>...</table>. Transcribe all " "other text as standard Markdown. Preserve the original text without translation or paraphrasing." ) formatted = apply_chat_template(processor, config, prompt, num_images=1, enable_thinking=False) out = generate(model, processor, formatted, image="page.png", max_tokens=2048, verbose=False) print(out.text if hasattr(out, "text") else out) ``` CLI: ```bash mlx_vlm.generate --model sahilchachra/ovisocr2-mxfp8-mlx \ --prompt "Extract all readable content from the image a...