Instructions to use ProKope-AI/ProKope-4B-Eagle-Eye with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ProKope-AI/ProKope-4B-Eagle-Eye with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ProKope-AI/ProKope-4B-Eagle-Eye")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ProKope-AI/ProKope-4B-Eagle-Eye")
model = AutoModelForMultimodalLM.from_pretrained("ProKope-AI/ProKope-4B-Eagle-Eye", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ProKope-AI/ProKope-4B-Eagle-Eye with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ProKope-AI/ProKope-4B-Eagle-Eye"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ProKope-AI/ProKope-4B-Eagle-Eye",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ProKope-AI/ProKope-4B-Eagle-Eye

SGLang

How to use ProKope-AI/ProKope-4B-Eagle-Eye with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ProKope-AI/ProKope-4B-Eagle-Eye" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ProKope-AI/ProKope-4B-Eagle-Eye",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ProKope-AI/ProKope-4B-Eagle-Eye" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ProKope-AI/ProKope-4B-Eagle-Eye",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ProKope-AI/ProKope-4B-Eagle-Eye with Docker Model Runner:
```
docker model run hf.co/ProKope-AI/ProKope-4B-Eagle-Eye
```

ProKope-4B-Eagle-Eye / README.md

AlanCantoFTW

Restore proven leaderboard YAML header and add Commercial Licensing section

71ed9e9 verified 4 days ago

preview code

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---
library_name: transformers
pipeline_tag: image-text-to-text
gated: manual
extra_gated_prompt: "Access to ProKope-4B-Eagle-Eye weights is provided for academic evaluation, benchmarking, and commercial licensing inquiries. Please provide your affiliation and intended use case to request access."
extra_gated_fields:
  Company or Institution: text
  Corporate or Academic Email: text
  Intended Evaluation Use Case: text
  I agree not to redistribute the model weights outside approved evaluation harnesses: checkbox
datasets:
- allenai/olmOCR-bench
- llamaindex/ParseBench
- PaddlePaddle/Real5-OmniDocBench
- pulse-ai/PulseBench-Tab
- pulsia/fr-bench-pdf2md
tags:
- vision-language
- multimodal
- ocr
- document-parsing
- eval-results
- olmocr-bench
- parse-bench
- qwen3_5
- prokope
license: apache-2.0
language:
- en
- fr
- de
- es
- zh
- ja
- ko
- ru
- ar
model_name: ProKope-4B-Eagle-Eye
author: Alan Canto
creator: Alan Canto
---
<p align="center">
  <img src="banner.jpg" alt="ProKope-4B-Eagle-Eye" width="100%" style="border-radius: 8px; margin-bottom: 16px;">
</p>

# ProKope-4B-Eagle-Eye

## Introduction

**ProKope-4B-Eagle-Eye** is a 4.2B-parameter multimodal vision-language model developed by [ProKope AI](https://huggingface.co/ProKope-AI) for document parsing, structured HTML table reconstruction (`rowspan`/`colspan`), mathematical LaTeX transcription, multi-column reading-order extraction, and chart interpretation.

The model uses the `Qwen3_5ForConditionalGeneration` architecture (`4.2B` parameters in `bfloat16`) with a 32K token context window, and loads directly in Hugging Face `transformers` and `vLLM` without custom remote code.

---

## Evaluation

Evaluated with deterministic greedy decoding (`do_sample=False`, `0-shot` Pass@1) across standard document OCR and table extraction benchmarks:

| Benchmark | Dataset | Split | Metric | ProKope-4B-Eagle-Eye | LightOnOCR-3-4B | Chandra-OCR-2 (4B) | olmOCR-2-7B |
| :--- | :--- | :---: | :--- | :---: | :---: | :---: | :---: |
| **PulseBench-Tab** | [`pulse-ai/PulseBench-Tab`](https://huggingface.co/datasets/pulse-ai/PulseBench-Tab) | `train (1,820)` | TEDS Similarity | **89.40%** | 84.40% | 78.90% | 70.50% |
| **olmOCR-Bench** | [`allenai/olmOCR-bench`](https://huggingface.co/datasets/allenai/olmOCR-bench) | `test (7 splits)` | Pass Rate | **87.06%** | 86.30% | 85.80% | 82.40% |
| **ParseBench** | [`llamaindex/ParseBench`](https://huggingface.co/datasets/llamaindex/ParseBench) | `test (5 pillars)` | Macro Average | **86.01%** | 75.10% | 70.10% | 43.30% |
| **fr-bench-pdf2md** | [`pulsia/fr-bench-pdf2md`](https://huggingface.co/datasets/pulsia/fr-bench-pdf2md) | `long_table` | Unit-Test Pass Rate | **85.11%** | 64.00% | 72.20% | 61.40% |
| **Real5-OmniDocBench** | [`PaddlePaddle/Real5-OmniDocBench`](https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench) | `test (5 scenarios)` | Overall Similarity | **76.61%** | — | — | — |

### `allenai/olmOCR-bench` Category Breakdown

| Category | ProKope-4B-Eagle-Eye | LightOnOCR-3-4B | Chandra-OCR-2 (4B) | olmOCR-2-7B |
| :--- | :---: | :---: | :---: | :---: |
| **ArXiv Math** (`arxiv_math`) | **95.56%** | 91.10% | 86.90% | 83.00% |
| **Old Scans Math** (`old_scans_math`) | **93.10%** | 89.10% | 89.10% | 82.30% |
| **Multi-Column** (`multi_column`) | **92.86%** | 85.60% | 82.10% | 83.70% |
| **Old Scans** (`old_scans`) | **85.19%** | 52.50% | 51.10% | 47.70% |
| **Tables** (`table_tests`) | 85.07% | 90.20% | **92.10%** | 84.90% |
| **Long Tiny Text** (`long_tiny_text`) | 79.41% | 93.20% | **93.70%** | 81.90% |
| **Headers & Footers** (`headers_footers`) | 78.26% | 89.20% | 91.40% | **96.10%** |
| **Overall** | **87.06%** | 86.30% | 85.80% | 82.40% |

### `llamaindex/ParseBench` Pillar Breakdown

| Pillar | Metric | ProKope-4B-Eagle-Eye | LightOnOCR-3-4B | Nemotron-3.5-Parse |
| :--- | :--- | :---: | :---: | :---: |
| **Table** (`table`) | TEDS Similarity | **93.95%** | 83.80% | 84.67% |
| **Layout** (`layout`) | Reading Order & Content | **90.46%** | 68.30% | 70.67% |
| **Text Content** (`text_content`) | 12-Rule Verbatim Match | 87.98% | **89.90%** | 87.96% |
| **Text Formatting** (`text_formatting`) | 11-Rule Hierarchy & Emphasis | **82.51%** | 67.60% | 68.28% |
| **Chart** (`chart`) | Label & Value Extraction | **75.17%** | 66.10% | 64.40% |
| **Overall (`mean`)** | 5-Pillar Macro Average | **86.01%** | 75.10% | 75.20% |

---

## Model Details

| Property | Value |
| :--- | :--- |
| **Architecture** | `Qwen3_5ForConditionalGeneration` |
| **Total Tensor Footprint** | `4.2B` combined parameters (`9.08 GB` in `bfloat16`) |
| **Context Length** | 32,768 tokens |
| **Recommended VRAM** | 12 GB+ for `bfloat16` inference |
| **Precision** | `torch.bfloat16` |
| **License** | Apache 2.0 (Weights gated under manual review) |

---

## Quickstart

### 1. Document & Table Extraction (`transformers`)

Requires `transformers`, `torch`, and `pillow`:

```python
import torch
from PIL import Image, ImageOps
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
model_id = "ProKope-AI/ProKope-4B-Eagle-Eye"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
).eval()
image = Image.open("document_page.png").convert("RGB")
image = ImageOps.autocontrast(image, cutoff=1).resize((1024, 768), Image.Resampling.BILINEAR)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {
                "type": "text",
                "text": "[Visual Geometry: 1024x768px | Domain: complex_grid_table_html]\nExtract all tables and text on this document page into canonical HTML `<table>` structure and Markdown.",
            },
        ],
    }
]
prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[prompt], images=[image], return_tensors="pt").to(model.device)
inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
with torch.no_grad():
    generated_ids = model.generate(**inputs, max_new_tokens=1408, do_sample=False, repetition_penalty=1.08)
response = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)
```

### 2. Serving with `vLLM`

```bash
vllm serve ProKope-AI/ProKope-4B-Eagle-Eye \
  --dtype bfloat16 \
  --max-model-len 16384 \
  --limit-mm-per-prompt image=2
```

---

## Commercial Licensing & Enterprise Deployment

Access to `ProKope-4B-Eagle-Eye` weights is gated (`manual`) for benchmark verification, academic research, and commercial licensing evaluation. Production deployment is available through two options:

1. **Managed Parse API (`POST /v1/parse`)**: Pay-as-you-go multi-page PDF and image extraction returning clean Markdown, HTML tables, and structured JSON without managing GPU infrastructure.
2. **Self-Hosted Enterprise Node License**: Annual commercial license and turnkey Docker container (`vLLM` / `FastAPI`) for private VPC, HIPAA, SOC 2, and air-gapped on-premise deployments on a single 16GB–24GB GPU.

For API keys, enterprise trials, or commercial node licensing, contact **[prokope.model@gmail.com](mailto:prokope.model@gmail.com)** or submit the gated access form above with your corporate email and intended use case.

---

## Limitations & Intended Use

- **Intended Use**: Document OCR, structured Markdown conversion, HTML table reconstruction (`rowspan`/`colspan`), mathematical LaTeX transcription, and chart extraction.
- **Limitations**: Like all vision-language models, outputs on dense rasterized chart annotations (`ParseBench [chart]`: `75.17%`), sub-6pt footnote text (`olmOCR-bench [long_tiny_text]`: `79.41%`), or complex running watermarks (`olmOCR-bench [headers_footers]`: `78.26%`) may contain transcription errors. Verify critical financial or medical outputs independently.

---

## Contact & Citation

- **Organization**: [ProKope AI](https://huggingface.co/ProKope-AI)
- **Authors**: Alan Canto, Wesley Foreman
- **Contact**: [prokope.model@gmail.com](mailto:prokope.model@gmail.com)

```bibtex
@misc{canto2026prokope4beagleeye,
  author       = {Alan Canto and Wesley Foreman},
  title        = {ProKope-4B-Eagle-Eye: Multimodal Document Parsing and Table Reconstruction Model},
  howpublished = {Hugging Face},
  year         = {2026},
  url          = {https://huggingface.co/ProKope-AI/ProKope-4B-Eagle-Eye}
}
```

	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	gated: manual
	extra_gated_prompt: "Access to ProKope-4B-Eagle-Eye weights is provided for academic evaluation, benchmarking, and commercial licensing inquiries. Please provide your affiliation and intended use case to request access."
	extra_gated_fields:
	Company or Institution: text
	Corporate or Academic Email: text
	Intended Evaluation Use Case: text
	I agree not to redistribute the model weights outside approved evaluation harnesses: checkbox
	datasets:
	- allenai/olmOCR-bench
	- llamaindex/ParseBench
	- PaddlePaddle/Real5-OmniDocBench
	- pulse-ai/PulseBench-Tab
	- pulsia/fr-bench-pdf2md
	tags:
	- vision-language
	- multimodal
	- ocr
	- document-parsing
	- eval-results
	- olmocr-bench
	- parse-bench
	- qwen3_5
	- prokope
	license: apache-2.0
	language:
	- en
	- fr
	- de
	- es
	- zh
	- ja
	- ko
	- ru
	- ar
	model_name: ProKope-4B-Eagle-Eye
	author: Alan Canto
	creator: Alan Canto
	---

	<p align="center">
	<img src="banner.jpg" alt="ProKope-4B-Eagle-Eye" width="100%" style="border-radius: 8px; margin-bottom: 16px;">
	</p>

	# ProKope-4B-Eagle-Eye

	## Introduction

	ProKope-4B-Eagle-Eye is a 4.2B-parameter multimodal vision-language model developed by [ProKope AI](https://huggingface.co/ProKope-AI) for document parsing, structured HTML table reconstruction (`rowspan`/`colspan`), mathematical LaTeX transcription, multi-column reading-order extraction, and chart interpretation.

	The model uses the `Qwen3_5ForConditionalGeneration` architecture (`4.2B` parameters in `bfloat16`) with a 32K token context window, and loads directly in Hugging Face `transformers` and `vLLM` without custom remote code.

	---

	## Evaluation

	Evaluated with deterministic greedy decoding (`do_sample=False`, `0-shot` Pass@1) across standard document OCR and table extraction benchmarks:

	\| Benchmark \| Dataset \| Split \| Metric \| ProKope-4B-Eagle-Eye \| LightOnOCR-3-4B \| Chandra-OCR-2 (4B) \| olmOCR-2-7B \|
	\| :--- \| :--- \| :---: \| :--- \| :---: \| :---: \| :---: \| :---: \|
	\| PulseBench-Tab \| [`pulse-ai/PulseBench-Tab`](https://huggingface.co/datasets/pulse-ai/PulseBench-Tab) \| `train (1,820)` \| TEDS Similarity \| 89.40% \| 84.40% \| 78.90% \| 70.50% \|
	\| olmOCR-Bench \| [`allenai/olmOCR-bench`](https://huggingface.co/datasets/allenai/olmOCR-bench) \| `test (7 splits)` \| Pass Rate \| 87.06% \| 86.30% \| 85.80% \| 82.40% \|
	\| ParseBench \| [`llamaindex/ParseBench`](https://huggingface.co/datasets/llamaindex/ParseBench) \| `test (5 pillars)` \| Macro Average \| 86.01% \| 75.10% \| 70.10% \| 43.30% \|
	\| fr-bench-pdf2md \| [`pulsia/fr-bench-pdf2md`](https://huggingface.co/datasets/pulsia/fr-bench-pdf2md) \| `long_table` \| Unit-Test Pass Rate \| 85.11% \| 64.00% \| 72.20% \| 61.40% \|
	\| Real5-OmniDocBench \| [`PaddlePaddle/Real5-OmniDocBench`](https://huggingface.co/datasets/PaddlePaddle/Real5-OmniDocBench) \| `test (5 scenarios)` \| Overall Similarity \| 76.61% \| — \| — \| — \|

	### `allenai/olmOCR-bench` Category Breakdown

	\| Category \| ProKope-4B-Eagle-Eye \| LightOnOCR-3-4B \| Chandra-OCR-2 (4B) \| olmOCR-2-7B \|
	\| :--- \| :---: \| :---: \| :---: \| :---: \|
	\| ArXiv Math (`arxiv_math`) \| 95.56% \| 91.10% \| 86.90% \| 83.00% \|
	\| Old Scans Math (`old_scans_math`) \| 93.10% \| 89.10% \| 89.10% \| 82.30% \|
	\| Multi-Column (`multi_column`) \| 92.86% \| 85.60% \| 82.10% \| 83.70% \|
	\| Old Scans (`old_scans`) \| 85.19% \| 52.50% \| 51.10% \| 47.70% \|
	\| Tables (`table_tests`) \| 85.07% \| 90.20% \| 92.10% \| 84.90% \|
	\| Long Tiny Text (`long_tiny_text`) \| 79.41% \| 93.20% \| 93.70% \| 81.90% \|
	\| Headers & Footers (`headers_footers`) \| 78.26% \| 89.20% \| 91.40% \| 96.10% \|
	\| Overall \| 87.06% \| 86.30% \| 85.80% \| 82.40% \|

	### `llamaindex/ParseBench` Pillar Breakdown

	\| Pillar \| Metric \| ProKope-4B-Eagle-Eye \| LightOnOCR-3-4B \| Nemotron-3.5-Parse \|
	\| :--- \| :--- \| :---: \| :---: \| :---: \|
	\| Table (`table`) \| TEDS Similarity \| 93.95% \| 83.80% \| 84.67% \|
	\| Layout (`layout`) \| Reading Order & Content \| 90.46% \| 68.30% \| 70.67% \|
	\| Text Content (`text_content`) \| 12-Rule Verbatim Match \| 87.98% \| 89.90% \| 87.96% \|
	\| Text Formatting (`text_formatting`) \| 11-Rule Hierarchy & Emphasis \| 82.51% \| 67.60% \| 68.28% \|
	\| Chart (`chart`) \| Label & Value Extraction \| 75.17% \| 66.10% \| 64.40% \|
	\| Overall (`mean`) \| 5-Pillar Macro Average \| 86.01% \| 75.10% \| 75.20% \|

	---

	## Model Details

	\| Property \| Value \|
	\| :--- \| :--- \|
	\| Architecture \| `Qwen3_5ForConditionalGeneration` \|
	\| Total Tensor Footprint \| `4.2B` combined parameters (`9.08 GB` in `bfloat16`) \|
	\| Context Length \| 32,768 tokens \|
	\| Recommended VRAM \| 12 GB+ for `bfloat16` inference \|
	\| Precision \| `torch.bfloat16` \|
	\| License \| Apache 2.0 (Weights gated under manual review) \|

	---

	## Quickstart

	### 1. Document & Table Extraction (`transformers`)

	Requires `transformers`, `torch`, and `pillow`:

	```python
	import torch
	from PIL import Image, ImageOps
	from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

	model_id = "ProKope-AI/ProKope-4B-Eagle-Eye"

	processor = AutoProcessor.from_pretrained(model_id)
	model = Qwen3_5ForConditionalGeneration.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	).eval()

	image = Image.open("document_page.png").convert("RGB")
	image = ImageOps.autocontrast(image, cutoff=1).resize((1024, 768), Image.Resampling.BILINEAR)

	messages = [
	{
	"role": "user",
	"content": [
	{"type": "image", "image": image},
	{
	"type": "text",
	"text": "[Visual Geometry: 1024x768px \| Domain: complex_grid_table_html]\nExtract all tables and text on this document page into canonical HTML `<table>` structure and Markdown.",
	},
	],
	}
	]

	prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	inputs = processor(text=[prompt], images=[image], return_tensors="pt").to(model.device)
	inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)

	with torch.no_grad():
	generated_ids = model.generate(**inputs, max_new_tokens=1408, do_sample=False, repetition_penalty=1.08)

	response = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
	print(response)
	```

	### 2. Serving with `vLLM`

	```bash
	vllm serve ProKope-AI/ProKope-4B-Eagle-Eye \
	--dtype bfloat16 \
	--max-model-len 16384 \
	--limit-mm-per-prompt image=2
	```

	---

	## Commercial Licensing & Enterprise Deployment

	Access to `ProKope-4B-Eagle-Eye` weights is gated (`manual`) for benchmark verification, academic research, and commercial licensing evaluation. Production deployment is available through two options:

	1. Managed Parse API (`POST /v1/parse`): Pay-as-you-go multi-page PDF and image extraction returning clean Markdown, HTML tables, and structured JSON without managing GPU infrastructure.
	2. Self-Hosted Enterprise Node License: Annual commercial license and turnkey Docker container (`vLLM` / `FastAPI`) for private VPC, HIPAA, SOC 2, and air-gapped on-premise deployments on a single 16GB–24GB GPU.

	For API keys, enterprise trials, or commercial node licensing, contact [prokope.model@gmail.com](mailto:prokope.model@gmail.com) or submit the gated access form above with your corporate email and intended use case.

	---

	## Limitations & Intended Use

	- Intended Use: Document OCR, structured Markdown conversion, HTML table reconstruction (`rowspan`/`colspan`), mathematical LaTeX transcription, and chart extraction.
	- Limitations: Like all vision-language models, outputs on dense rasterized chart annotations (`ParseBench [chart]`: `75.17%`), sub-6pt footnote text (`olmOCR-bench [long_tiny_text]`: `79.41%`), or complex running watermarks (`olmOCR-bench [headers_footers]`: `78.26%`) may contain transcription errors. Verify critical financial or medical outputs independently.

	---

	## Contact & Citation

	- Organization: [ProKope AI](https://huggingface.co/ProKope-AI)
	- Authors: Alan Canto, Wesley Foreman
	- Contact: [prokope.model@gmail.com](mailto:prokope.model@gmail.com)

	```bibtex
	@misc{canto2026prokope4beagleeye,
	author = {Alan Canto and Wesley Foreman},
	title = {ProKope-4B-Eagle-Eye: Multimodal Document Parsing and Table Reconstruction Model},
	howpublished = {Hugging Face},
	year = {2026},
	url = {https://huggingface.co/ProKope-AI/ProKope-4B-Eagle-Eye}
	}
	```