Image-Text-to-Text
Safetensors
MLX
mlx-vlm
mistral3
apple-silicon
pixtral
guardrail
content-moderation
safety-classification
multimodal
4-bit precision
conversational
Instructions to use AXONVERTEX-AI-RESEARCH/Shieldstral-1.0-3B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AXONVERTEX-AI-RESEARCH/Shieldstral-1.0-3B-MLX-4bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("AXONVERTEX-AI-RESEARCH/Shieldstral-1.0-3B-MLX-4bit") config = load_config("AXONVERTEX-AI-RESEARCH/Shieldstral-1.0-3B-MLX-4bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| #!/usr/bin/env python3 | |
| from pathlib import Path | |
| import sys | |
| ROOT = Path(__file__).resolve().parents[1] | |
| sys.path.insert(0, str(ROOT / "graphShieldMistral" / "src")) | |
| from graphshield_mistral.graph_builder import main | |
| if __name__ == "__main__": | |
| main(ROOT) | |