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
| set -euo pipefail | |
| ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" | |
| cd "$ROOT" | |
| [[ "$(uname -s)" == "Darwin" ]] || { echo "ERROR: MLX requires macOS." >&2; exit 1; } | |
| [[ "$(uname -m)" == "arm64" ]] || { echo "ERROR: MLX requires Apple Silicon arm64." >&2; exit 1; } | |
| PYTHON_BIN="${PYTHON_BIN:-python3}" | |
| "$PYTHON_BIN" -m venv .venv | |
| source .venv/bin/activate | |
| python -m pip install --upgrade pip setuptools wheel | |
| python -m pip install -r requirements.txt | |
| python - <<'PY_VERSIONS' | |
| import importlib.metadata as md | |
| for name in ("mlx", "mlx-vlm", "transformers", "mistral-common", "huggingface-hub"): | |
| print(f"{name}: {md.version(name)}") | |
| PY_VERSIONS | |
| echo "PASS: Apple MLX runtime installed in $ROOT/.venv" | |