AutomatosX MLX Model Catalog
Collection
Complete index of every Hub pack. Prefer the family collections above. • 95 items • Updated
How to use AutomatosX/AX-DeepSeek-OCR-2-MLX-AXQ-6bit 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-DeepSeek-OCR-2-MLX-AXQ-6bit")
config = load_config("AutomatosX/AX-DeepSeek-OCR-2-MLX-AXQ-6bit")
# 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)Development AXQuant (AXQ) 6-bit language MLX pack of
deepseek-ai/DeepSeek-OCR-2
@ aaa02f3811945a91062062994c5c4a3f4c0af2b0.
Language experts/attention/MLP at 6-bit; vision towers BF16-preserved. Total BPW includes large BF16 vision (policy minimum ~8.3 when 4-bit is excluded).
Converted via MLX-VLM deepseekocr_2 from
mlx-community/DeepSeek-OCR-2-bf16
@ 9946f9ac306378a3e6a86cad7d7f8be8e536f092.
| Claim | Status |
|---|---|
| AXQuant architecture-prior / development quant | Yes |
| OCR accuracy / document-bench scores | Not claimed |
| Vision optimization | No — SAM + Qwen2 encoder + projector BF16-preserved |
| Certified release | No |
Base weights © DeepSeek AI (Apache-2.0). Quantization by AXQuant (development).
6-bit
Base model
deepseek-ai/DeepSeek-OCR-2