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prithivMLmods/
proxima-ocr-d.markdown-post3.0.l

Name: proxima-ocr-d.markdown-post3.0.l
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proxima-ocr-d.markdown-post3.0.l

proxima-ocr-d.markdown-post3.0.l is an experimental document AI multimodal model fine-tuned on top of Qwen3-VL-8B-Instruct, optimized for high precision OCR and structured document reconstruction. The model converts documents into Markdown, HTML-Markdown, and hybrid enriched documentation formats capable of embedding inline programming languages and reconstructing complex layouts such as tables, forms, and mathematical content.

Key Enhancements

Dynamic Markdown Reconstruction Converts complex documents to structured Markdown or HTML-Markdown while preserving layout hierarchy, formatting consistency, semantic ordering, and section alignment.
Inline Code and Language Embedding Direct adaptation of Python, JavaScript, LaTeX, and shell syntax into reconstructed documents for technical and research documentation.
High Fidelity OCR and Visual Parsing Accurate recognition of text across structured and unstructured scanned documents, including multi page layout reasoning.
Complex Layout Interpretation Interprets tables, grids, equations, graphs, multi column layouts, and forms without structural distortion.
Document Retrieval and Semantic Linking Efficient multi page chunking with cross reference recognition and content traceability.
Multimodal Long Reasoning Supports advanced document question answering and reasoning across long input streams such as slides and manuscripts.

👉 This model is a stage progression model, and it may currently contain artifacts.

Example Preview

[1] Markdown HTML

Input Image	Markdown Preview Page 1	Markdown Preview Page 2
1	Page1	Page2

[2] JSON Nodes

Input Image	Node Preview Page 1	Node Preview Page 2
1	Page1	Page2

[3] YAML Nodes

Input Image	Node Preview Page 1	Node Preview Page 2
input	Page1	Page2

Quick Start with Transformers

python

from transformers import Qwen3VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen3VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/proxima-ocr-d.markdown-post3.0.l", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/proxima-ocr-d.markdown-post3.0.l")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Convert to Markdown."},
        ],
    }
]

text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=2048)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Intended Use

OCR to Markdown or HTML-Markdown conversion
Complex document reconstruction and formatting regeneration
Multi page document reasoning and retrieval
Table extraction and structured output transformation
Mathematical OCR and LaTeX conversion
Form extraction and structured entity generation
Knowledge base indexing and large document QA
Documentation regeneration for enterprise automation

Limitations

Accuracy may drop on extremely damaged or poorly scanned images
Significant GPU VRAM required for long sequences and multi page documents
Language accuracy varies for low resource scripts
Complex objects such as mixed orientation blocks may require secondary post processing
May occasionally produce formatting misalignment in highly irregular layouts

Training Details

Parameter	Value
Dataset Size	approx. 544K [ modular combination open source data & synthetic document data entries from Gemini 3 Pro ]
Architecture	Qwen3VLForConditionalGeneration
Training Time	approx. 17,040 seconds (4 h 44 m)
Precision	bfloat16
Hardware	4x H100 SXM (320 GB VRAM)
System Memory	752 GB RAM
CPU	80 vCPU

References

Qwen2.5 VL https://huggingface.co/papers/2502.13923
DocVLM Make Your VLM an Efficient Reader https://arxiv.org/pdf/2412.08746v1
YaRN Efficient Context Window Extension https://arxiv.org/pdf/2309.00071
Qwen2 VL High Resolution Perception https://arxiv.org/pdf/2409.12191
Qwen VL Vision Language Understanding and OCR https://arxiv.org/pdf/2308.12966
OCR Benchmark for Multimodal Models https://arxiv.org/pdf/2412.02210