Instructions to use Cubex11/Solari-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cubex11/Solari-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Cubex11/Solari-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Cubex11/Solari-GGUF", dtype="auto") - llama-cpp-python
How to use Cubex11/Solari-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Cubex11/Solari-GGUF", filename="Solari-f16.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 Settings
- llama.cpp
How to use Cubex11/Solari-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Cubex11/Solari-GGUF:F16 # Run inference directly in the terminal: llama cli -hf Cubex11/Solari-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Cubex11/Solari-GGUF:F16 # Run inference directly in the terminal: llama cli -hf Cubex11/Solari-GGUF:F16
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 Cubex11/Solari-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf Cubex11/Solari-GGUF:F16
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 Cubex11/Solari-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Cubex11/Solari-GGUF:F16
Use Docker
docker model run hf.co/Cubex11/Solari-GGUF:F16
- LM Studio
- Jan
- vLLM
How to use Cubex11/Solari-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Cubex11/Solari-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Cubex11/Solari-GGUF", "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/Cubex11/Solari-GGUF:F16
- SGLang
How to use Cubex11/Solari-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Cubex11/Solari-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Cubex11/Solari-GGUF", "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 images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Cubex11/Solari-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Cubex11/Solari-GGUF", "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" } } ] } ] }' - Ollama
How to use Cubex11/Solari-GGUF with Ollama:
ollama run hf.co/Cubex11/Solari-GGUF:F16
- Unsloth Studio
How to use Cubex11/Solari-GGUF 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 Cubex11/Solari-GGUF 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 Cubex11/Solari-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Cubex11/Solari-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Cubex11/Solari-GGUF with Docker Model Runner:
docker model run hf.co/Cubex11/Solari-GGUF:F16
- Lemonade
How to use Cubex11/Solari-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Cubex11/Solari-GGUF:F16
Run and chat with the model
lemonade run user.Solari-GGUF-F16
List all available models
lemonade list
Solari-GGUF
GGUF quantized version of Solari โ a 500M parameter vision-language model fine-tuned for reduced hallucination on real-world images.
For full model details, training procedure, and benchmark analysis, see the Solari model card.
Solari-f16.ggufandSolari_v2-f16.ggufare deprecated. Please useSolari_v3-f16.gguffor the best results. Older versions may produce incorrect or degraded outputs.
Model Details
- Base Model: Cubex11/Solari
- Original Base: SmolVLM2-500M-Video-Instruct
- Format: GGUF (for use with llama.cpp, Ollama, LM Studio, etc.)
- Fine-tuning Method: QLoRA + DPO on RLAIF-V (72K preference pairs)
- License: Apache-2.0
Benchmark Results
Solari improves on 7 out of 8 benchmarks vs the base model:
| Benchmark | Base | Solari | Change |
|---|---|---|---|
| POPE Overall | 82.67 | 85.08 | +2.41 |
| POPE Recall | 76.73 | 85.33 | +8.60 |
| AMBER Avg | 79.38 | 79.77 | +0.39 |
| A-OKVQA | 68.12 | 69.00 | +0.88 |
| MMStar | 38.33 | 39.60 | +1.27 |
| MMBench | 53.14 | 53.42 | +0.28 |
| RealWorldQA | 49.80 | 50.59 | +0.78 |
| HallusionBench | 27.58 | 28.14 | +0.56 |
| MME Perception | 1216 | 1119 | -97.7 |
Note: Benchmarks were evaluated on the full-precision model. GGUF quantization may cause minor performance differences.
Usage
With llama.cpp
# Download the GGUF file
huggingface-cli download Cubex11/Solari-GGUF --local-dir ./solari-gguf
# Run inference
./llama-cli -m ./solari-gguf/Solari.gguf -p "Describe this image" --image your_image.jpg
With Ollama
# Create a Modelfile
echo 'FROM ./Solari.gguf' > Modelfile
ollama create solari -f Modelfile
ollama run solari
Links
- Full-precision model: Cubex11/Solari
- Training dataset: RLAIF-V
- Base model: SmolVLM2-500M-Video-Instruct
Citation
@misc{solari2026,
title={Solari: Hallucination-Reduced Vision Language Model via QLoRA DPO on RLAIF-V},
author={Cubex11},
year={2026},
url={https://huggingface.co/Cubex11/Solari}
}
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HuggingFaceTB/SmolLM2-360M