Image-Text-to-Text
Transformers
Safetensors
deepseek_vl_v2
feature-extraction
auto-round
mxfp8
compressed-tensors
custom_code
Instructions to use intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound
- SGLang
How to use intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound 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 "intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound" \ --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": "intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound", "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 "intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound" \ --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": "intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound with Docker Model Runner:
docker model run hf.co/intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound
Download quantization_config.json from intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound/resolve/main/quantization_config.json
- Command line
-
hf download hf://intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound/quantization_config.json
-
curl -L -o quantization_config.json https://huggingface.co/intel-ai/DeepSeek-OCR-2-MXFP8-CT-AutoRound/resolve/main/quantization_config.json
14.2 kB
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| "model.sam_model.net_2", | |
| "model.sam_model.net_3", | |
| "model.sam_model.patch_embed.proj" | |
| ], | |
| "provider": "auto-round" | |
| } |