Instructions to use Dogacel/DeepSeek-OCR-Metal-MPS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dogacel/DeepSeek-OCR-Metal-MPS with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Dogacel/DeepSeek-OCR-Metal-MPS", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Dogacel/DeepSeek-OCR-Metal-MPS", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Dogacel/DeepSeek-OCR-Metal-MPS with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dogacel/DeepSeek-OCR-Metal-MPS"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dogacel/DeepSeek-OCR-Metal-MPS",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Dogacel/DeepSeek-OCR-Metal-MPS

SGLang

How to use Dogacel/DeepSeek-OCR-Metal-MPS with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Dogacel/DeepSeek-OCR-Metal-MPS" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dogacel/DeepSeek-OCR-Metal-MPS",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Dogacel/DeepSeek-OCR-Metal-MPS" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dogacel/DeepSeek-OCR-Metal-MPS",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Dogacel/DeepSeek-OCR-Metal-MPS with Docker Model Runner:
```
docker model run hf.co/Dogacel/DeepSeek-OCR-Metal-MPS
```

Incompatibility with transformers >= 4.41: DynamicCache.seen_tokens deprecated

by rafaeltuelho - opened Dec 3, 2025

Discussion

rafaeltuelho

Dec 3, 2025

Issue Description

The model's custom code (modeling_deepseek.py) uses the deprecated DynamicCache.seen_tokens attribute, which causes errors with newer versions of the transformers library (4.41+).

Error Message

AttributeError: 'DynamicCache' object has no attribute 'seen_tokens'

Environment

transformers version: 4.57.1 (also affects 4.49.0+)
PyTorch version: 2.7.0
Platform: macOS (Apple Silicon M4)
Python: 3.9

Root Cause

The seen_tokens attribute on DynamicCache was deprecated in transformers v4.41 and has been removed in later versions. The model's modeling_deepseek.py file references this attribute in the prepare_inputs_for_generation method.

Reference: https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/discussions/9

Suggested Fix

Update the model code to use cache_position instead of seen_tokens, as recommended by the transformers deprecation warning:

The seen_tokens attribute is deprecated and will be removed in v4.41. Use the cache_position model input instead.

Workaround

Currently, users must downgrade to transformers==4.43.4 to use this model, which may conflict with other dependencies.

Context

We're integrating this MPS-compatible model into Docling to provide DeepSeek-OCR support on Apple Silicon. The transformers version constraint (>=4.46.0) in Docling's dependencies makes this incompatibility a blocker for MPS users.

Thank you for creating this MPS-compatible fork! It would be great to have it updated for newer transformers versions.

Dogacel

Owner Dec 3, 2025

Issue Description

The model's custom code (modeling_deepseek.py) uses the deprecated DynamicCache.seen_tokens attribute, which causes errors with newer versions of the transformers library (4.41+).

Error Message

AttributeError: 'DynamicCache' object has no attribute 'seen_tokens'

Environment

transformers version: 4.57.1 (also affects 4.49.0+)

PyTorch version: 2.7.0

Platform: macOS (Apple Silicon M4)

Python: 3.9

Root Cause

The seen_tokens attribute on DynamicCache was deprecated in transformers v4.41 and has been removed in later versions. The model's modeling_deepseek.py file references this attribute in the prepare_inputs_for_generation method.

Reference: https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/discussions/9

Suggested Fix

Update the model code to use cache_position instead of seen_tokens, as recommended by the transformers deprecation warning:

The seen_tokens attribute is deprecated and will be removed in v4.41. Use the cache_position model input instead.

Workaround

Currently, users must downgrade to transformers==4.43.4 to use this model, which may conflict with other dependencies.

Context

We're integrating this MPS-compatible model into Docling to provide DeepSeek-OCR support on Apple Silicon. The transformers version constraint (>=4.46.0) in Docling's dependencies makes this incompatibility a blocker for MPS users.

Thank you for creating this MPS-compatible fork! It would be great to have it updated for newer transformers versions.

Thank you for the suggestion. I've went ahead and found the PR that introduced that change and applied the patch.

https://github.com/huggingface/transformers/pull/29467/files

Also tested with both old transformers and new transformers version. Let me know if there are any issues. I am happy to hear you are adding this model to docling.

rafaeltuelho

Dec 4, 2025

Awesome! Thank you for your prompt response.
I will test it here.

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