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: 2,271 Bytes
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"model_path": "sahilchachra/unlimited-ocr-mxfp8-mlx",
"served_revision": "55b8031a6c867de675279d9604e38cc94b9882a4",
"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": 2.9305669579916866,
"memory_after_load_mb": 3992.859375,
"num_warmup": 1,
"num_runs": 3,
"mean_tps": 35.07146198590831,
"std_tps": 0.290807343078657,
"mean_elapsed_seconds": 8.953089208662277,
"mean_peak_memory_mb": 5165.991419999999,
"runs": [
{
"run": 1,
"elapsed_seconds": 9.042429541994352,
"tokens_generated": 256,
"tokens_generated_source": "mlx-vlm token count",
"tokens_per_second": 34.74497597308309,
"tokens_per_second_source": "mlx-vlm generation_tps",
"peak_memory_mb": 5165.81578,
"memory_delta_mb": 1337.186768,
"process_peak_rss_mb": 4022.6875,
"prompt_tokens": 697,
"prompt_tokens_per_second": 429.97806661114964,
"finish_reason": "length"
},
{
"run": 2,
"elapsed_seconds": 9.023512416999438,
"tokens_generated": 256,
"tokens_generated_source": "mlx-vlm token count",
"tokens_per_second": 35.01812122387363,
"tokens_per_second_source": "mlx-vlm generation_tps",
"peak_memory_mb": 5166.07924,
"memory_delta_mb": 1337.188084,
"process_peak_rss_mb": 4023.046875,
"prompt_tokens": 697,
"prompt_tokens_per_second": 417.40764558358615,
"finish_reason": "length"
},
{
"run": 3,
"elapsed_seconds": 8.793325666993042,
"tokens_generated": 256,
"tokens_generated_source": "mlx-vlm token count",
"tokens_per_second": 35.451288760768186,
"tokens_per_second_source": "mlx-vlm generation_tps",
"peak_memory_mb": 5166.07924,
"memory_delta_mb": 1337.188084,
"process_peak_rss_mb": 4023.3125,
"prompt_tokens": 697,
"prompt_tokens_per_second": 449.69288066410945,
"finish_reason": "length"
}
]
} |