Instructions to use anerjy/Step-3.7-Flash-MLX-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use anerjy/Step-3.7-Flash-MLX-adapter 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("anerjy/Step-3.7-Flash-MLX-adapter") config = load_config("anerjy/Step-3.7-Flash-MLX-adapter") # 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
- Atomic Chat
Commit History
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Initial: Step 3.7 Flash MLX adapter (text + vision)
anerjy +1: Claude Opus 4.7First MLX-native runtime adapter for stepfun-ai/Step-3.7-Flash on Apple Silicon. mlx-vlm (multimodal) + mlx-lm (text-only fallback). Includes ImagePatcher port for multi-crop vision (728² global + N×504² sub-patches) and chat_template with reasoning_effort=none. 4 port bugs documented in README (MLX nn.Upsample NHWC, lazy serialize zeros, vision cache stale on multi-crop, vision weight provenance). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>98d164a