Instructions to use WaveMatrix/PaddleOCR-VL-1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WaveMatrix/PaddleOCR-VL-1.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="WaveMatrix/PaddleOCR-VL-1.5")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WaveMatrix/PaddleOCR-VL-1.5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use WaveMatrix/PaddleOCR-VL-1.5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "WaveMatrix/PaddleOCR-VL-1.5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "WaveMatrix/PaddleOCR-VL-1.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/WaveMatrix/PaddleOCR-VL-1.5
- SGLang
How to use WaveMatrix/PaddleOCR-VL-1.5 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 "WaveMatrix/PaddleOCR-VL-1.5" \ --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": "WaveMatrix/PaddleOCR-VL-1.5", "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 "WaveMatrix/PaddleOCR-VL-1.5" \ --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": "WaveMatrix/PaddleOCR-VL-1.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use WaveMatrix/PaddleOCR-VL-1.5 with Docker Model Runner:
docker model run hf.co/WaveMatrix/PaddleOCR-VL-1.5
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60ba429 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | {%- if not add_generation_prompt is defined -%}
{%- set add_generation_prompt = true -%}
{%- endif -%}
{%- if not cls_token is defined -%}
{%- set cls_token = "<|begin_of_sentence|>" -%}
{%- endif -%}
{%- if not eos_token is defined -%}
{%- set eos_token = "</s>" -%}
{%- endif -%}
{{- cls_token -}}
{%- for message in messages -%}
{%- if message["role"] == "user" -%}
{{- "User: " -}}
{%- for content in message["content"] -%}
{%- if content["type"] == "image" -%}
{{ "<|IMAGE_START|><|IMAGE_PLACEHOLDER|><|IMAGE_END|>" }}
{%- endif -%}
{%- endfor -%}
{%- for content in message["content"] -%}
{%- if content["type"] == "text" -%}
{{ content["text"] }}
{%- endif -%}
{%- endfor -%}
{{ "\n" -}}
{%- elif message["role"] == "assistant" -%}
{{- "Assistant:\n" -}}
{%- for content in message["content"] -%}
{%- if content["type"] == "text" -%}
{{ content["text"] }}
{%- endif -%}
{%- endfor -%}
{{ eos_token -}}
{%- elif message["role"] == "system" -%}
{%- for content in message["content"] -%}
{%- if content["type"] == "text" -%}
{{ content["text"] + "\n" }}
{%- endif -%}
{%- endfor -%}
{%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{- "Assistant:\n" -}}
{%- endif -%}
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