DeepSeek-OCR – Apple Metal Performance Shaders (MPS) & CPU Support
This repository uses the weights from the original DeepSeek-OCR and modifies model to support MPS and CPU inference
Usage
Inference using Huggingface transformers on Metal Performance Shaders (MPS) and CPU. Requirements tested on python 3.12.9：
git clone git@hf.co:Dogacel/DeepSeek-OCR-Metal-MPS
cd DeepSeek-OCR-Metal-MPS/demo
# Use mamba or conda
mamba create -n deepseek-ocr python=3.12.9 -y
mamba activate deepseek-ocr
pip install -r requirements.txt
python run_dpsk_ocr.py
from transformers import AutoModel, AutoTokenizer
import torch
model_name = 'Dogacel/DeepSeek-OCR-Metal-MPS'
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModel.from_pretrained(
model_name,
_attn_implementation='eager',
trust_remote_code=True,
use_safetensors=True,
)
device = torch.device("mps")
dtype = torch.float16
model = model.eval().to(device).to(dtype)
prompt = "<image>\n<|grounding|>Convert the document to markdown. "
image_file = 'image.png'
output_path = 'results4'
res = model.infer(
tokenizer,
device=device,
dtype=dtype,
prompt=prompt,
image_file=image_file,
output_path = output_path,
base_size=1024,
image_size=640,
crop_mode=False,
save_results = True,
test_compress = True,
)
vLLM
vLLM integration hasn't been tested yet.
Refer to 🌟GitHub for guidance on model inference acceleration and PDF processing, etc.
uv venv
source .venv/bin/activate
# Until v0.11.1 release, you need to install vLLM from nightly build
uv pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
from vllm import LLM, SamplingParams
from vllm.model_executor.models.deepseek_ocr import NGramPerReqLogitsProcessor
from PIL import Image
# Create model instance
llm = LLM(
model="Dogacel/DeepSeek-OCR-Metal-MPS",
enable_prefix_caching=False,
mm_processor_cache_gb=0,
logits_processors=[NGramPerReqLogitsProcessor]
)
# Prepare batched input with your image file
image_1 = Image.open("path/to/your/image_1.png").convert("RGB")
image_2 = Image.open("path/to/your/image_2.png").convert("RGB")
prompt = "<image>\nFree OCR."
model_input = [
{
"prompt": prompt,
"multi_modal_data": {"image": image_1}
},
{
"prompt": prompt,
"multi_modal_data": {"image": image_2}
}
]
sampling_param = SamplingParams(
temperature=0.0,
max_tokens=8192,
# ngram logit processor args
extra_args=dict(
ngram_size=30,
window_size=90,
whitelist_token_ids={128821, 128822}, # whitelist: <td>, </td>
),
skip_special_tokens=False,
)
# Generate output
model_outputs = llm.generate(model_input, sampling_param)
# Print output
for output in model_outputs:
print(output.outputs[0].text)