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---
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
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