Image-Text-to-Text
GGUF
English
vision
multimodal
image-to-text
q4_k_m
quantized
nano-dream
conversational
Instructions to use dill-dev/NanoDream-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use dill-dev/NanoDream-7B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="dill-dev/NanoDream-7B", filename="NanoDream-7B-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use dill-dev/NanoDream-7B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dill-dev/NanoDream-7B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf dill-dev/NanoDream-7B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dill-dev/NanoDream-7B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf dill-dev/NanoDream-7B:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf dill-dev/NanoDream-7B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf dill-dev/NanoDream-7B:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf dill-dev/NanoDream-7B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf dill-dev/NanoDream-7B:Q4_K_M
Use Docker
docker model run hf.co/dill-dev/NanoDream-7B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use dill-dev/NanoDream-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dill-dev/NanoDream-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dill-dev/NanoDream-7B", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/dill-dev/NanoDream-7B:Q4_K_M
- Ollama
How to use dill-dev/NanoDream-7B with Ollama:
ollama run hf.co/dill-dev/NanoDream-7B:Q4_K_M
- Unsloth Studio new
How to use dill-dev/NanoDream-7B with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for dill-dev/NanoDream-7B to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for dill-dev/NanoDream-7B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dill-dev/NanoDream-7B to start chatting
- Docker Model Runner
How to use dill-dev/NanoDream-7B with Docker Model Runner:
docker model run hf.co/dill-dev/NanoDream-7B:Q4_K_M
- Lemonade
How to use dill-dev/NanoDream-7B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dill-dev/NanoDream-7B:Q4_K_M
Run and chat with the model
lemonade run user.NanoDream-7B-Q4_K_M
List all available models
lemonade list
Update README.md
Browse files
README.md
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---
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language: en
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license: apache-2.0
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tags:
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- vision
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- multimodal
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pipeline_tag: image-text-to-text
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---
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---
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language: en
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license: apache-2.0
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model_name: NanoDream-7B (GGUF)
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base_model: llava-hf/llava-1.5-7b-hf
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tags:
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- vision
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- gguf
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- multimodal
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- image-to-text
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- q4_k_m
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- quantized
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- nano-dream
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pipeline_tag: image-text-to-text
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library_name: gguf
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inference: false
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model_creator: dill-dev
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quantized_by: dill-dev
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---
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# 🎨 NanoDream-7B (GGUF)
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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.
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## 🚀 Key Highlights
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- **Optimized Architecture**: Fine-tuned for high-speed multi-modal reasoning.
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- **Quantization**: Q4_K_M (The industry standard for balancing quality and performance).
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- **Low Resource Usage**: Runs comfortably on devices with 8GB RAM or less.
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- **Unified Interface**: Perfect for real-time image description, object detection, and visual QA.
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## 🛠️ Quantization Details
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This model was quantized using llama.cpp to provide a seamless experience on local hardware.
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- **Method**: Q4_K_M (4-bit quantization with medium-sized K-quants)
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- **Format**: GGUF (Compatible with llama.cpp, LM Studio, and more)
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- **Model Size**: Approx. 4.08 GB
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## 💻 How to Use
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### 1. Using llama.cpp (Command Line)
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To interact with NanoDream-7B via terminal, use the following command:
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```bash
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./llama-cli \
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-m NanoDream-7B-Q4_K_M.gguf \
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--mmproj NanoDream-7B-mmproj-f16.gguf \
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--image input_sample.jpg \
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-p "Describe this image accurately."
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````
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### 2. Prompt Template
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For best results, use the standard interaction format:
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```
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USER: <image>\n<prompt>\nASSISTANT:
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```
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## 📊 Hardware Requirements
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| Resource | Minimum | Recommended |
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| ---------- | ------- | ----------- |
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| System RAM | 6 GB | 8 GB+ |
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| VRAM (GPU) | 4 GB | 6 GB+ |
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| Disk Space | 4.5 GB | 5 GB |
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## 🛡️ Disclaimer
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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.
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---
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**Maintained and Published by:** dill-dev
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