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
File size: 2,244 Bytes
6708e4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | {
"model_path": "AX-Unlimited-OCR-3B-MoE-MLX-MXFP8-cal-bfloat16",
"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": 0.97142708300089,
"memory_after_load_mb": 4240.671875,
"num_warmup": 1,
"num_runs": 3,
"mean_tps": 38.770309481806514,
"std_tps": 0.6887167709523151,
"mean_elapsed_seconds": 8.391549889337815,
"mean_peak_memory_mb": 5420.697071,
"runs": [
{
"run": 1,
"elapsed_seconds": 8.60731708399544,
"tokens_generated": 256,
"tokens_generated_source": "mlx-vlm token count",
"tokens_per_second": 38.03899778227293,
"tokens_per_second_source": "mlx-vlm generation_tps",
"peak_memory_mb": 5420.521431,
"memory_delta_mb": 1337.1867550000002,
"process_peak_rss_mb": 4253.21875,
"prompt_tokens": 697,
"prompt_tokens_per_second": 380.356298056712,
"finish_reason": "length"
},
{
"run": 2,
"elapsed_seconds": 8.399659084010636,
"tokens_generated": 256,
"tokens_generated_source": "mlx-vlm token count",
"tokens_per_second": 38.57884453275097,
"tokens_per_second_source": "mlx-vlm generation_tps",
"peak_memory_mb": 5420.784891,
"memory_delta_mb": 1337.188071,
"process_peak_rss_mb": 4260.28125,
"prompt_tokens": 697,
"prompt_tokens_per_second": 401.47375057984203,
"finish_reason": "length"
},
{
"run": 3,
"elapsed_seconds": 8.167673500007368,
"tokens_generated": 256,
"tokens_generated_source": "mlx-vlm token count",
"tokens_per_second": 39.693086130395656,
"tokens_per_second_source": "mlx-vlm generation_tps",
"peak_memory_mb": 5420.784891,
"memory_delta_mb": 1337.188071,
"process_peak_rss_mb": 4260.46875,
"prompt_tokens": 697,
"prompt_tokens_per_second": 411.1295077060184,
"finish_reason": "length"
}
]
} |