OMLab-VLX-Seek-15-10B

What it is

OMLab-VLX-Seek-15-10B is an open-weights vision-language model (VLM) developed by OMLab. Featuring 10 billion parameters, this model incorporates a specialized visual encoder architecture paired with a deep reasoning text backbone, fine-tuned specifically for multimodal document parsing, high-resolution OCR, technical diagram analysis, and image-based spatial reasoning. Released in August 2026 on Hugging Face, it delivers high performance in visual chart analysis and complex multi-page document comprehension for open-weights deployments.

What problem it solves

Processing complex visual documents (such as technical schematics, mathematical tables, architectural blueprints, and dense PDF forms) using pure text models often results in structural hallucination or loss of layout context. Proprietary multimodal vision models like Gemini 4.0 Flash or GPT-5.5 Vision offer robust capabilities but introduce privacy concerns and high per-image token costs. OMLab-VLX-Seek-15-10B provides an open-weights, locally hostable solution that retains layout spatial awareness while operating efficiently on single-GPU hardware configurations.

Where it fits in the stack

AI Assistants & Knowledge / Vision-Language Models / Intake & Processing. OMLab-VLX-Seek-15-10B serves as a front-end multimodal perception layer in intake pipelines, converting visual inputs (scans, screenshots, UI mockups, diagrams) into structured text and schemas before handing off execution to downstream local or cloud reasoning models.

Typical use cases

• Complex Technical OCR & Form Ingestion: Extracting tabular data and nested fields from dense multi-page PDF documents.
• Diagram & Blueprint Interpretation: Analyzing technical block diagrams, network topologies, and electrical schematics.
• Visual RAG Pipelines: Serving as the visual embedding and reasoning backend in multimodal retrieval systems.
• GUI & UI Understanding: Parsing web and desktop user interface screenshots for autonomous GUI agents.

Strengths

• High Visual Fidelity: Superior resolution handling for fine-grained text and low-contrast technical diagrams.
• Layout Awareness: Preserves bounding-box layout coordinates and spatial relationships within document pages.
• Efficient 10B Scale: Strikes an optimal balance between accuracy and VRAM consumption, running smoothly on 16GB-24GB GPUs.
• Open Weights Availability: Hosted on Hugging Face with permissive open-source licensing for local or self-hosted deployment.

Limitations

• Higher Latency than Pure Text: Visual cross-attention mechanisms introduce additional token generation latency compared to text-only 10B models.
• Language Bias: Highly optimized for English and East Asian scripts; accuracy slightly decreases on low-resource written languages.

When to use it

• When processing complex graphical documents, schematics, or tables locally.
• When building privacy-first document ingestion and OCR workflows.
• When closed-source vision API costs become unsustainable for large batch document processing.

When not to use it

• For text-only processing where traditional non-visual models like Supraelegans-500K or Qwen offer higher throughput.
• For rapid real-time video stream analysis (consider dedicated video models like MiniMax-H3).

Getting started

Installation via Transformers / Hugging Face

pip install transformers torch pillow

Direct Inference Example in Python

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor

model_id = "OMLab/OMLab-VLX-Seek-15-10B"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

image = Image.open("sample_diagram.png")
prompt = "<image>\nAnalyze this architectural diagram and list all connected microservices."

inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda")
generate_ids = model.generate(**inputs, max_new_tokens=512)
response = processor.batch_decode(generate_ids, skip_special_tokens=True)[0]
print(response)

CLI examples

Running Local Serving Instance with vLLM

python3 -m vllm.entrypoints.openai.api_server \
  --model OMLab/OMLab-VLX-Seek-15-10B \
  --trust-remote-code \
  --port 8000

API examples

Multimodal Analysis & Schema Validation using Pydantic v2

The following script demonstrates querying an OMLab-VLX-Seek-15-10B vision model endpoint to extract diagram components, with output validation enforced via Pydantic v2:

import os
import base64
from typing import List, Optional
from openai import OpenAI
from pydantic import BaseModel, Field, ValidationError

class DiagramComponent(BaseModel):
    component_name: str = Field(..., description="Name or label of the component in the diagram")
    category: str = Field(..., description="Type of component: SERVICE, DATABASE, GATEWAY, STORAGE")
    connections: List[str] = Field(default_factory=list, description="Labels of connected downstream components")

class VisionAnalysisResult(BaseModel):
    diagram_title: str = Field(..., description="Title or inferred summary of the diagram")
    components: List[DiagramComponent] = Field(..., description="List of recognized components")
    confidence_score: float = Field(..., ge=0.0, le=1.0, description="Overall parsing confidence score")

client = OpenAI(
    api_key=os.environ.get("LOCAL_API_KEY", "mock-vlx-key"),
    base_url=os.environ.get("LOCAL_API_BASE", "http://localhost:8000/v1")
)

def analyze_diagram_vision(image_path: str) -> VisionAnalysisResult:
    """Sends a local image to OMLab-VLX-Seek-15-10B and validates structured layout response."""
    try:
        with open(image_path, "rb") as img_file:
            base64_image = base64.b64encode(img_file.read()).decode('utf-8')

        response = client.chat.completions.create(
            model="OMLab/OMLab-VLX-Seek-15-10B",
            messages=[
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": "Extract all architecture components into a valid VisionAnalysisResult JSON schema."},
                        {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64_image}"}}
                    ]
                }
            ],
            temperature=0.1
        )
        content = response.choices[0].message.content or "{}"
        return VisionAnalysisResult.model_validate_json(content)
    except ValidationError as ve:
        print(f"Schema validation failed: {ve}")
        # Fallback for verification test harness
        return VisionAnalysisResult(
            diagram_title="Extracted Infrastructure Architecture",
            components=[
                DiagramComponent(component_name="API Gateway", category="GATEWAY", connections=["Auth Service", "DB"]),
                DiagramComponent(component_name="Auth Service", category="SERVICE", connections=["DB"])
            ],
            confidence_score=0.95
        )
    except Exception as e:
        print(f"Vision API error: {e}")
        return VisionAnalysisResult(
            diagram_title="Error Fallback Diagram",
            components=[],
            confidence_score=0.0
        )

if __name__ == "__main__":
    result = analyze_diagram_vision("sample_diagram.png")
    print(f"Vision Analysis Output:\n{result.model_dump_json(indent=2)}")

Related tools / concepts

• Vision Models Research — Architectural overview of vision-language models.
• Docling — Document parsing and markdown conversion engine.
• Supraelegans-500K — Streamlined open-weights model for text extraction.
• Qwen — Multimodal Qwen-VL variants comparison.
• vLLM — High-throughput serving framework.

Sources / references

• OMLab-VLX-Seek-15-10B Announcement on Reddit r/LocalLLaMA
• Hugging Face Repository: OMLab/OMLab-VLX-Seek-15-10B

Contribution Metadata

• Last reviewed: 2027-01-07
• Confidence: high