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Luke-Bergen/
Mineral-Nano-1

Name: Mineral-Nano-1
Availability: InStock

deepseek-ai/DeepSeek-OCR

finetune

apache-2.0

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Mineral Nano 1 Vision

Mineral Nano 1 Vision is a compact, efficient vision-language model designed for fast inference and low-resource environments with multimodal capabilities.

Model Details

Model Name: mineral-nano-1
Model Type: Vision-Language Model (VLM)
Parameters: ~110M parameters
Context Length: 2048 tokens
Architecture: Transformer-based decoder with vision encoder (12 layers)
Precision: BFloat16
Image Resolution: 224x224
Modalities: Text + Images

Architecture

Language Model

Hidden Size: 768
Intermediate Size: 3072
Attention Heads: 12
Layers: 12
Vocabulary Size: 32,000 tokens
Positional Encoding: RoPE (Rotary Position Embeddings)

Vision Encoder

Image Size: 224x224
Patch Size: 16x16
Hidden Size: 768
Layers: 12
Image Tokens: 196 per image
Architecture: ViT-style encoder

Usage

Installation

pip install transformers pillow torch

Basic Image Understanding

python

from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import requests

model_name = "Luke-Bergen/mineral-nano-1"
processor = AutoProcessor.from_pretrained(model_name)
model = AutoModelForVision2Seq.from_pretrained(model_name)

# Load an image
url = "https://example.com/image.jpg"
image = Image.open(requests.get(url, stream=True).raw)

# Prepare inputs
prompt = "<image>What is in this image?"
inputs = processor(text=prompt, images=image, return_tensors="pt")

# Generate response
outputs = model.generate(**inputs, max_new_tokens=100)
response = processor.decode(outputs[0], skip_special_tokens=True)
print(response)

Multiple Images

python

from PIL import Image

images = [
    Image.open("image1.jpg"),
    Image.open("image2.jpg")
]

prompt = "<image>Describe the first image. <image>Now describe the second image."
inputs = processor(text=prompt, images=images, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(processor.decode(outputs[0], skip_special_tokens=True))

Chat with Images

python

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image"},
            {"type": "text", "text": "What objects are in this image?"}
        ]
    }
]

# Apply chat template
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, images=image, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7)
print(processor.decode(outputs[0], skip_special_tokens=True))

Local Images

python

from PIL import Image

# Load local image
image = Image.open("path/to/your/image.jpg")

prompt = "<image>Describe what you see in detail."
inputs = processor(text=prompt, images=image, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0], skip_special_tokens=True))

Training Details

Framework: PyTorch with Transformers
Training Data: Text + Image pairs
Training Duration: [Specify training time]
Hardware: [Specify GPUs used]
Vision Encoder: Pretrained ViT encoder fine-tuned with language model

Capabilities

✅ Image description and captioning ✅ Visual question answering ✅ Object detection and recognition ✅ Scene understanding ✅ Multi-image reasoning ✅ OCR and text extraction from images

Limitations

Limited to 224x224 resolution images
Context window of 2048 tokens including image tokens
May struggle with fine-grained details
Best for general image understanding tasks
Compact size means reduced capabilities compared to larger VLMs
Limited multilingual vision capabilities

Intended Use

This model is designed for:

Educational purposes and learning VLM architectures
Prototyping multimodal applications
Low-resource deployment scenarios
Fast inference with vision capabilities
Mobile and edge device applications
Personal projects requiring image understanding

Image Preprocessing

Images are automatically:

Resized to 224x224
Normalized with CLIP-style statistics
Converted to RGB
Split into 16x16 patches (196 total patches)

Performance Tips

Use square images when possible for best results
Ensure images are clear and well-lit
Keep prompts concise and specific
Use batch processing for multiple images
Enable use_cache=True for faster generation

License

[Specify your license - e.g., MIT, Apache 2.0, etc.]

Citation

bibtex

@misc{mineral-nano-1-vision,
  author = {Luke Bergen},
  title = {Mineral Nano 1 Vision: A Compact Vision-Language Model},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/Luke-Bergen/mineral-nano-1}
}

Contact

For questions or issues, please open an issue on the model repository.

Acknowledgments

This model builds upon research in vision transformers and multimodal learning.

Luke-Bergen/Mineral-Nano-1

Mineral Nano 1 Vision

Model Details

Architecture

Language Model

Vision Encoder

Usage

Installation

Basic Image Understanding

Multiple Images

Chat with Images

Local Images

Training Details

Capabilities

Limitations

Intended Use

Image Preprocessing

Performance Tips

License

Citation

Contact

Acknowledgments

Luke-Bergen/
Mineral-Nano-1