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ProKope-AI
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Model Details
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ProKope-AI
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ProKope-VL-7B is a multimodal vision-language model developed by ProKope AI for high-resolution document understanding, chart and scientific figure interpretation, visual question answering, and desktop/web GUI coordinate grounding.
The model uses the Qwen2_5_VLForConditionalGeneration architecture (7.61B language backbone + 0.67B dynamic-resolution Vision Transformer, 8.29B total parameters) with a 128K token context window, and loads directly in Hugging Face transformers and vLLM without custom remote code.
Evaluated with deterministic greedy decoding (do_sample=False, 0-shot Pass@1) across standard multimodal and GUI grounding benchmarks:
| Benchmark | Dataset | Split | Metric | ProKope-VL-7B | Qwen2.5-VL-7B-Instruct | InternVL2.5-8B | Llama-3.2-11B-Vision |
|---|---|---|---|---|---|---|---|
| DocVQA | lmms-lab/DocVQA | test | ANLS / Exact Match | 95.00% | 95.70% | 93.00% | 88.40% |
| ScienceQA-IMG | derek-thomas/ScienceQA | test | Accuracy | 90.00% | 89.20% |
| Property | Value |
|---|---|
| Architecture | Qwen2_5_VLForConditionalGeneration |
| Language Backbone | 28 Transformer layers, 3,584 hidden size, 28 Q / 4 KV GQA heads (7.61B parameters) |
| Vision Encoder | 32-layer Dynamic-Resolution ViT + spatial patch merger (0.67B parameters) |
| Total Tensor Footprint | 8.29B combined parameters (16.58 GB in bfloat16 across 5 .safetensors shards) |
transformers)Requires transformers>=4.49.0, torch, and pillow:
python
import torchfrom PIL import Imagefrom transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration model_id = "ProKope-AI/ProKope-VL-7B" processor = AutoProcessor.from_pretrained(model_id)model = Qwen2_5_VLForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto",) image = Image.open("document_or_chart.png").convert("RGB")messages = [ { "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": "Extract the primary table values from this image."}, ], }] prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=[prompt], images=[image], return_tensors="pt").to(model.device) with torch.no_grad(): generated_ids = model.generate(**inputs, max_new_tokens=512, do_sample=False) response = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response)To localize an interface element on a desktop, mobile, or web screenshot:
python
screenshot = Image.open("desktop_screen.png").convert("RGB")messages = [ { "role": "user", "content": [ {"type": "image"}, { "type": "text", "text": "Locate the target UI element for: 'Export as PDF'. Return normalized click coordinates (x, y).", }, ], }] prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = processor(text=[prompt], images=[screenshot], return_tensors="pt").to(model.device) with torch.no_grad(): generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False) print(processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))vLLMbash
vllm serve ProKope-AI/ProKope-VL-7B \ --dtype bfloat16 \ --max-model-len 32768 \ --limit-mm-per-prompt image=4bibtex
@misc{canto2026prokopevl7b, author = {Alan Canto and Wesley Foreman}, title = {ProKope-VL-7B: Multimodal Vision-Language and GUI Grounding Model}, howpublished = {Hugging Face}, year = {2026}, url = {https://huggingface.co/ProKope-AI/ProKope-VL-7B}}| 88.50% |
| 85.20% |
| ChartQA | HuggingFaceM4/ChartQA | test | Relaxed Accuracy | 88.33% | 87.30% | 84.80% | 83.40% |
| ScreenSpot | rootsautomation/ScreenSpot | test | Single-Point Click Acc | 83.33% | 55.00% | 46.50% | 34.00% |
| MathVista | AI4Math/MathVista | testmini | Accuracy | 56.67% | 68.20% | 64.40% | 51.50% |
| MMMU | MMMU/MMMU | validation | Accuracy | 54.69% | 58.60% | 55.00% | 50.70% |
| MMMU-Pro (4-Opt) | MMMU/MMMU_Pro | test (4-opt) | Accuracy | 46.67% | 38.30% | 34.30% | 23.70% |
| MMMU-Pro (Vision) | MMMU/MMMU_Pro | test (vision) | Accuracy | 31.67% | 34.30% | 30.20% | 19.50% |
| ScreenSpot-Pro | likaixin/ScreenSpot-Pro | test | High-Res GUI Acc | 28.33% | 26.80% | 17.60% | 8.40% |
| Core 6-Task Avg | 6-Benchmark Macro Average | — | — | 78.00% | 75.67% | 72.03% | 65.53% |
| Context Length | 128,000 tokens |
| Recommended VRAM | 20 GB+ for bfloat16 inference |
| Precision | torch.bfloat16 |
| License | Apache 2.0 (Weights gated under manual review) |