---
language: en
license: apache-2.0
model_name: NanoDream-7B (GGUF)
tags:
  - vision
  - gguf
  - multimodal
  - image-to-text
  - q4_k_m
  - quantized
  - nano-dream
pipeline_tag: image-text-to-text
library_name: gguf
inference: false
model_creator: dill-dev
quantized_by: dill-dev
---

# 🎨 NanoDream-7B (GGUF)

NanoDream-7B is a high-performance, next-generation multimodal model optimized for efficiency, speed, and advanced image reasoning. This model brings professional-grade Vision-Language capabilities to consumer-grade hardware, laptops, and mobile devices using the GGUF format.

## 🚀 Key Highlights

- **Optimized Architecture**: Fine-tuned for high-speed multi-modal reasoning.  
- **Quantization**: Q4_K_M (The industry standard for balancing quality and performance).  
- **Low Resource Usage**: Runs comfortably on devices with 8GB RAM or less.  
- **Unified Interface**: Perfect for real-time image description, object detection, and visual QA.  

## 🛠️ Quantization Details

This model was quantized using llama.cpp to provide a seamless experience on local hardware.

- **Method**: Q4_K_M (4-bit quantization with medium-sized K-quants)  
- **Format**: GGUF (Compatible with llama.cpp, LM Studio, and more)  
- **Model Size**: Approx. 4.08 GB  

## 💻 How to Use

### 1. Using llama.cpp (Command Line)

To interact with NanoDream-7B via terminal, use the following command:

```bash
./llama-cli \
  -m NanoDream-7B-Q4_K_M.gguf \
  --mmproj NanoDream-7B-mmproj-f16.gguf \
  --image input_sample.jpg \
  -p "Describe this image accurately."
````

### 2. Prompt Template

For best results, use the standard interaction format:

```
USER: <image>\n<prompt>\nASSISTANT:
```

## 📊 Hardware Requirements

| Resource   | Minimum | Recommended |
| ---------- | ------- | ----------- |
| System RAM | 6 GB    | 8 GB+       |
| VRAM (GPU) | 4 GB    | 6 GB+       |
| Disk Space | 4.5 GB  | 5 GB        |

## 🛡️ Disclaimer

NanoDream-7B is a powerful tool for visual understanding. However, users should verify critical information generated by the model. It is not intended for use in high-risk medical, legal, or safety-critical applications.

---

**Maintained and Published by:** dill-dev