vLLM/recipes/models/PaddlePaddle/PaddleOCR-VL-1.5.yaml

meta:
  title: "PaddleOCR-VL-1.5"
  slug: "paddleocr-vl-1.5"
  provider: "PaddlePaddle"
  description: "PaddleOCR-VL-1.5 (0.9B) — next-gen compact VLM for document parsing; adds text spotting, seal recognition, and Tibetan/Bengali"
  date_added: 2026-05-11
  date_updated: 2026-05-11
  tasks:
    - multimodal
  related_recipes:
    - "PaddlePaddle/PaddleOCR-VL"

model:
  model_id: "PaddlePaddle/PaddleOCR-VL-1.5"
  min_vllm_version: "0.11.1"
  architecture: dense
  parameter_count: "0.9B"
  active_parameters: "0.9B"
  context_length: 131072
  base_args:
    - "--trust-remote-code"
    - "--max-num-batched-tokens"
    - "16384"
    - "--no-enable-prefix-caching"
    - "--mm-processor-cache-gb"
    - "0"
  base_env: {}

dependencies:
  - note: "PaddlePaddle runtime (install in a separate venv from vllm to avoid conflicts)"
    command: "uv pip install paddlepaddle-gpu==3.2.1 --extra-index-url https://www.paddlepaddle.org.cn/packages/stable/cu126/"
  - note: "PaddleOCR document-parsing helpers (1.5 ships under the same paddleocr[doc-parser] extra)"
    command: 'uv pip install -U "paddleocr[doc-parser]"'
  - note: "Safetensors loader used by the doc-parser path"
    command: "uv pip install safetensors"

features:
  text_only:
    description: "Skip loading the vision encoder for text-only workloads — frees VRAM for KV cache. Mutually exclusive with encoder_parallel."
    args:
      - "--language-model-only"
  encoder_parallel:
    description: "Run the vision encoder in data-parallel mode — avoids TP comm overhead on the small encoder. Mutually exclusive with text_only."
    args:
      - "--mm-encoder-tp-mode"
      - "data"

opt_in_features:
  - text_only
  - encoder_parallel

variants:
  default:
    precision: bf16
    vram_minimum_gb: 2
    description: "BF16 weights — small footprint, runs on most GPUs"

compatible_strategies:
  - single_node_tp
  - multi_node_tp

kv_offload_support:
  offloading_cpu: verified
  offloading_fs: verified

hardware_overrides:
  amd:
    extra_args: []
    extra_env:
      SAFETENSORS_FAST_GPU: "1"
strategy_overrides: {}

guide: |
  ## Overview

  [PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5) is the next
  generation of PaddleOCR-VL — same 0.9B architecture (NaViT-style dynamic-resolution
  vision encoder + ERNIE-4.5-0.3B LM), with substantial accuracy gains and new tasks:

  - **94.5% on OmniDocBench v1.5** (SOTA)
  - **Text spotting** — line-level localization + recognition in one pass
  - **Seal recognition** — new task with SOTA results
  - **Irregular-shape localization** — polygonal detection under skew/warping
  - **Multilingual** — adds Tibetan and Bengali
  - Cross-page table merging and paragraph-heading recognition

  Architecture is identical to PaddleOCR-VL, so the vLLM launch command and feature
  flags are the same.

  ## Prerequisites

  - Hardware: 1x GPU (small VRAM footprint)
  - vLLM >= 0.11.1 (nightly if not released yet)
  - AMD ROCm wheels require Python 3.12, ROCm 7.2.3, and glibc >= 2.35.

  ### Install vLLM (NVIDIA)

  ```bash
  uv venv
  source .venv/bin/activate
  uv pip install -U vllm --pre \
    --extra-index-url https://wheels.vllm.ai/nightly \
    --extra-index-url https://download.pytorch.org/whl/cu129 \
    --index-strategy unsafe-best-match
  ```

  ### Install vLLM (AMD ROCm)

  ```bash
  uv venv --python 3.12
  source .venv/bin/activate
  uv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm
  ```

  ## Launch command

  ```bash
  vllm serve PaddlePaddle/PaddleOCR-VL-1.5 \
    --trust-remote-code \
    --max-num-batched-tokens 16384 \
    --no-enable-prefix-caching \
    --mm-processor-cache-gb 0
  ```

  AMD MI300X/MI325X/MI355X:

  ```bash
  export SAFETENSORS_FAST_GPU=1

  vllm serve PaddlePaddle/PaddleOCR-VL-1.5 \
    --trust-remote-code \
    --max-num-batched-tokens 16384 \
    --mm-processor-cache-gb 0
  ```

  Tip: OCR workloads don't benefit much from prefix caching or image reuse, so disable
  those to avoid hashing/caching overhead.

  ## Client Usage

  Task-specific prompts (note the two new tasks `spotting` and `seal`):

  ```python
  from openai import OpenAI

  client = OpenAI(api_key="EMPTY", base_url="http://localhost:8000/v1", timeout=3600)

  TASKS = {
      "ocr": "OCR:",
      "table": "Table Recognition:",
      "formula": "Formula Recognition:",
      "chart": "Chart Recognition:",
      "spotting": "Spotting:",
      "seal": "Seal Recognition:",
  }

  response = client.chat.completions.create(
      model="PaddlePaddle/PaddleOCR-VL-1.5",
      messages=[{
          "role": "user",
          "content": [
              {"type": "image_url", "image_url": {"url": "https://.../receipt.png"}},
              {"type": "text", "text": TASKS["spotting"]},
          ],
      }],
      temperature=0.0,
  )
  print(response.choices[0].message.content)
  ```

  ## Offline Inference with PP-DocLayoutV2

  Use separate venvs for `vllm` and `paddlepaddle` to avoid conflicts. If you see
  "The model PaddleOCR-VL-1.5-0.9B does not exist.", add
  `--served-model-name PaddleOCR-VL-1.5-0.9B`.

  ```bash
  uv pip install paddlepaddle-gpu==3.2.1 --extra-index-url https://www.paddlepaddle.org.cn/packages/stable/cu126/
  uv pip install -U "paddleocr[doc-parser]"
  uv pip install safetensors
  ```

  ```python
  from paddleocr import PaddleOCRVL

  pipeline = PaddleOCRVL(
      vl_rec_model_name="PaddleOCR-VL-1.5-0.9B",
      vl_rec_backend="vllm-server",
      vl_rec_server_url="http://localhost:8000/v1",
      layout_detection_model_name="PP-DocLayoutV2",
      layout_detection_model_dir="/path/to/your/PP-DocLayoutV2/",
  )

  output = pipeline.predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png")
  for i, res in enumerate(output):
      res.save_to_json(save_path=f"output_{i}.json")
      res.save_to_markdown(save_path=f"output_{i}.md")
  ```

  ## References

  - [PaddleOCR-VL-1.5 on Hugging Face](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5)
  - [PaddleOCR GitHub](https://github.com/PaddlePaddle/PaddleOCR)
  - [vLLM support tracking issue](https://github.com/vllm-project/vllm/issues/33554)