Image-Text-to-Text
MLX
Safetensors
unlimited-ocr
ax-engine
mlx-vlm
ocr
mxfp8
int8
apple-silicon
automatosx
conversational
8-bit precision
Instructions to use AutomatosX/AX-Unlimited-OCR-3B-MoE-MLX-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AutomatosX/AX-Unlimited-OCR-3B-MoE-MLX-MXFP8 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("AutomatosX/AX-Unlimited-OCR-3B-MoE-MLX-MXFP8") config = load_config("AutomatosX/AX-Unlimited-OCR-3B-MoE-MLX-MXFP8") # 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
| { | |
| "model_path": "AX-Unlimited-OCR-3B-MoE-MLX-MXFP8", | |
| "served_revision": null, | |
| "image_path": "test_invoice.png", | |
| "prompt": "<image>document parsing.", | |
| "max_tokens": 256, | |
| "system": { | |
| "platform": "macOS-26.5.2-arm64-arm-64bit-Mach-O", | |
| "processor": "arm", | |
| "python_version": "3.14.6", | |
| "machine": "arm64", | |
| "mlx_version": "0.32.0", | |
| "mlx_vlm_version": "0.6.6", | |
| "chip": "Apple M3 Max", | |
| "total_memory_gb": 128.0 | |
| }, | |
| "model_load_time_seconds": 0.9827843750099419, | |
| "memory_after_load_mb": 4043.265625, | |
| "num_warmup": 1, | |
| "num_runs": 3, | |
| "mean_tps": 35.14004923388335, | |
| "std_tps": 0.8627581629341593, | |
| "mean_elapsed_seconds": 8.941998055335716, | |
| "mean_peak_memory_mb": 5184.062959, | |
| "runs": [ | |
| { | |
| "run": 1, | |
| "elapsed_seconds": 8.715698832995258, | |
| "tokens_generated": 256, | |
| "tokens_generated_source": "mlx-vlm token count", | |
| "tokens_per_second": 36.34797807809006, | |
| "tokens_per_second_source": "mlx-vlm generation_tps", | |
| "peak_memory_mb": 5183.887319, | |
| "memory_delta_mb": 1337.1867550000002, | |
| "process_peak_rss_mb": 4058.71875, | |
| "prompt_tokens": 697, | |
| "prompt_tokens_per_second": 422.9274407258055, | |
| "finish_reason": "length" | |
| }, | |
| { | |
| "run": 2, | |
| "elapsed_seconds": 9.117345000006026, | |
| "tokens_generated": 256, | |
| "tokens_generated_source": "mlx-vlm token count", | |
| "tokens_per_second": 34.685109648852176, | |
| "tokens_per_second_source": "mlx-vlm generation_tps", | |
| "peak_memory_mb": 5184.1507790000005, | |
| "memory_delta_mb": 1337.1880710000005, | |
| "process_peak_rss_mb": 4065.359375, | |
| "prompt_tokens": 697, | |
| "prompt_tokens_per_second": 413.1939079712154, | |
| "finish_reason": "length" | |
| }, | |
| { | |
| "run": 3, | |
| "elapsed_seconds": 8.992950333005865, | |
| "tokens_generated": 256, | |
| "tokens_generated_source": "mlx-vlm token count", | |
| "tokens_per_second": 34.38705997470784, | |
| "tokens_per_second_source": "mlx-vlm generation_tps", | |
| "peak_memory_mb": 5184.1507790000005, | |
| "memory_delta_mb": 1337.1880710000005, | |
| "process_peak_rss_mb": 4065.5625, | |
| "prompt_tokens": 697, | |
| "prompt_tokens_per_second": 463.0680271067021, | |
| "finish_reason": "length" | |
| } | |
| ] | |
| } |