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DeepSeek-VL2-small - AI Vision Models Tool

Overview

DeepSeek-VL2-small is a multimodal vision–language model in the DeepSeek-VL2 family, published on Hugging Face by deepseek-ai. It is built as an image-text-to-text model intended for tasks that require jointly reasoning over images and natural language. Typical use cases called out by the authors include visual question answering (VQA), optical character recognition (OCR) on images and scans, document/table/chart understanding, and visual grounding for locating objects referenced by text. The model targets practitioners who need a single, generative multimodal model to produce text outputs conditioned on images and textual prompts. According to its Hugging Face page, DeepSeek-VL2-small exposes an image-text-to-text pipeline and contains 16.1B parameters, positioning it as a relatively large but compact member of the VL2 family suitable for research, prototyping, and production evaluation where a generative multimodal interface is required (e.g., extract table contents as CSV, answer questions about a chart, or transcribe annotated screenshots). (Source: Hugging Face model page: https://huggingface.co/deepseek-ai/deepseek-vl2-small)

Model Statistics

Downloads: 3,739
Likes: 170
Pipeline: image-text-to-text
Parameters: 16.1B

License: other

Model Details

Architecture and size: DeepSeek-VL2-small is described on the model page as a mixture-of-experts (MoE) style vision–language model with approximately 16.1 billion parameters. It is provided as an image-text-to-text model (a generative encoder–decoder style pipeline that consumes images plus optional text prompts and produces text outputs). Capabilities: The model is designed for multimodal tasks such as visual question answering, OCR and text extraction from images, document/table/chart understanding and structured extraction, and visual grounding (localizing regions or objects referenced by text). Because it is a generative image-text-to-text model, it can be prompted to produce plain-text answers, structured outputs (CSV/JSON), or step-by-step explanations depending on the prompt engineering. Deployment and license: The Hugging Face page lists the model pipeline as image-text-to-text and lists the license type as “other.” The model card does not indicate a specific upstream base model in its metadata. For usage, standard Hugging Face Transformers pipelines can be used to run inference; GPU acceleration (CUDA) is recommended for low-latency inference on a model of this parameter scale. (Source: Hugging Face model page: https://huggingface.co/deepseek-ai/deepseek-vl2-small)

Key Features

Image-text-to-text generative interface for producing text from images and prompts
Supports visual question answering across diverse image domains
OCR-capable text extraction from photos, scans, and screenshots
Document, table, and chart understanding with structured output capability
Visual grounding to localize regions referenced by user queries
Mixture-of-experts architecture for capacity scaling in multimodal tasks

Example Usage

Example (python):

from transformers import pipeline

# Install dependencies if needed:
# pip install transformers accelerate torch pillow

# Load the image-text-to-text pipeline for DeepSeek-VL2-small
pipe = pipeline(
    "image-text-to-text",
    model="deepseek-ai/deepseek-vl2-small",  # Hugging Face model ID
    device=0  # set to -1 for CPU
)

# Example: ask a question about an image (local file path or PIL.Image)
image_path = "receipt.jpg"
prompt = "Extract all line items and return CSV with columns: description, price"

# The pipeline accepts a list of inputs; each input is a dict with 'image' and 'text'.
outputs = pipe([{"image": image_path, "text": prompt}], max_new_tokens=512)

# Outputs are typically in the generated_text field (structure may vary by pipeline version)
print(outputs[0].get("generated_text", outputs[0]))