{ "hf_id": "PaddlePaddle/PaddleOCR-VL-1.5", "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" ] }, "recommended_command": { "hardware": "h200", "strategy": "single_node_tp", "variant": "default", "node_count": 1, "deploy_type": "single_node", "env": {}, "docker_image": "vllm/vllm-openai:latest", "command": "vllm serve PaddlePaddle/PaddleOCR-VL-1.5 \\\n --trust-remote-code \\\n --max-num-batched-tokens 16384 \\\n --no-enable-prefix-caching \\\n --mm-processor-cache-gb 0 \\\n --tensor-parallel-size 1", "argv": [ "vllm", "serve", "PaddlePaddle/PaddleOCR-VL-1.5", "--trust-remote-code", "--max-num-batched-tokens", "16384", "--no-enable-prefix-caching", "--mm-processor-cache-gb", "0", "--tensor-parallel-size", "1" ], "docker_command": "docker run --gpus all \\\n --privileged --ipc=host -p 8000:8000 \\\n -v ~/.cache/huggingface:/root/.cache/huggingface \\\n vllm/vllm-openai:latest PaddlePaddle/PaddleOCR-VL-1.5 \\\n --trust-remote-code \\\n --max-num-batched-tokens 16384 \\\n --no-enable-prefix-caching \\\n --mm-processor-cache-gb 0 \\\n --tensor-parallel-size 1", "docker_argv": [ "docker", "run", "--gpus", "all", "--privileged", "--ipc=host", "-p", "8000:8000", "-v", "~/.cache/huggingface:/root/.cache/huggingface", "vllm/vllm-openai:latest", "PaddlePaddle/PaddleOCR-VL-1.5", "--trust-remote-code", "--max-num-batched-tokens", "16384", "--no-enable-prefix-caching", "--mm-processor-cache-gb", "0", "--tensor-parallel-size", "1" ], "strategy_spec": { "name": "single_node_tp", "deploy_type": "single_node", "display_name": "Tensor Parallel", "short_label": "TP", "orientation": "latency", "description": "Single-node tensor parallel. Splits the model across all local GPUs. TP size is set to the GPU count at deploy time. The simplest multi-GPU strategy — works for all model architectures.\n", "hardware_match": { "min_gpus": 1, "max_gpus": 8, "multi_node": false }, "vllm_args": [], "parallel_flag": "--tensor-parallel-size" }, "hardware_profile": { "brand": "NVIDIA", "generation": "hopper", "compute_arch": "sm90", "display_name": "H200", "description": "NVIDIA H200 SXM 141 GB HBM3e · 8-GPU HGX node", "gpu_count": 8, "vram_gb": 1128, "multi_node": false }, "alternatives": { "multi_node_tp": "/PaddlePaddle/PaddleOCR-VL-1.5/strategies/multi_node_tp.json", "kv_store_centralized_mooncake": "/PaddlePaddle/PaddleOCR-VL-1.5/strategies/kv_store_centralized_mooncake.json", "kv_store_distributed_mooncake": "/PaddlePaddle/PaddleOCR-VL-1.5/strategies/kv_store_distributed_mooncake.json" }, "by_hardware": { "h200": "/PaddlePaddle/PaddleOCR-VL-1.5/hw/h200.json", "h100": "/PaddlePaddle/PaddleOCR-VL-1.5/hw/h100.json", "b200": "/PaddlePaddle/PaddleOCR-VL-1.5/hw/b200.json", "gb200": "/PaddlePaddle/PaddleOCR-VL-1.5/hw/gb200.json", "b300": "/PaddlePaddle/PaddleOCR-VL-1.5/hw/b300.json", "gb300": "/PaddlePaddle/PaddleOCR-VL-1.5/hw/gb300.json", "mi300x": "/PaddlePaddle/PaddleOCR-VL-1.5/hw/mi300x.json", "mi325x": "/PaddlePaddle/PaddleOCR-VL-1.5/hw/mi325x.json", "mi350x": "/PaddlePaddle/PaddleOCR-VL-1.5/hw/mi350x.json", "mi355x": "/PaddlePaddle/PaddleOCR-VL-1.5/hw/mi355x.json" } }, "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": {}, "install": { "pip": { "command": "uv venv\nsource .venv/bin/activate\nuv pip install -U vllm --torch-backend auto" }, "docker": { "command": "docker pull vllm/vllm-openai:latest" }, "extras": [ { "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" } }, "kv_offload_support": { "offloading_cpu": "verified", "offloading_fs": "verified" }, "hardware_overrides": { "amd": { "extra_args": [], "extra_env": { "SAFETENSORS_FAST_GPU": "1" } } }, "guide": "## Overview\n\n[PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5) is the next\ngeneration of PaddleOCR-VL — same 0.9B architecture (NaViT-style dynamic-resolution\nvision encoder + ERNIE-4.5-0.3B LM), with substantial accuracy gains and new tasks:\n\n- **94.5% on OmniDocBench v1.5** (SOTA)\n- **Text spotting** — line-level localization + recognition in one pass\n- **Seal recognition** — new task with SOTA results\n- **Irregular-shape localization** — polygonal detection under skew/warping\n- **Multilingual** — adds Tibetan and Bengali\n- Cross-page table merging and paragraph-heading recognition\n\nArchitecture is identical to PaddleOCR-VL, so the vLLM launch command and feature\nflags are the same.\n\n## Prerequisites\n\n- Hardware: 1x GPU (small VRAM footprint)\n- vLLM >= 0.11.1 (nightly if not released yet)\n- AMD ROCm wheels require Python 3.12, ROCm 7.2.3, and glibc >= 2.35.\n\n### Install vLLM (NVIDIA)\n\n```bash\nuv venv\nsource .venv/bin/activate\nuv pip install -U vllm --pre \\\n --extra-index-url https://wheels.vllm.ai/nightly \\\n --extra-index-url https://download.pytorch.org/whl/cu129 \\\n --index-strategy unsafe-best-match\n```\n\n### Install vLLM (AMD ROCm)\n\n```bash\nuv venv --python 3.12\nsource .venv/bin/activate\nuv pip install vllm --extra-index-url https://wheels.vllm.ai/rocm\n```\n\n## Launch command\n\n```bash\nvllm serve PaddlePaddle/PaddleOCR-VL-1.5 \\\n --trust-remote-code \\\n --max-num-batched-tokens 16384 \\\n --no-enable-prefix-caching \\\n --mm-processor-cache-gb 0\n```\n\nAMD MI300X/MI325X/MI355X:\n\n```bash\nexport SAFETENSORS_FAST_GPU=1\n\nvllm serve PaddlePaddle/PaddleOCR-VL-1.5 \\\n --trust-remote-code \\\n --max-num-batched-tokens 16384 \\\n --mm-processor-cache-gb 0\n```\n\nTip: OCR workloads don't benefit much from prefix caching or image reuse, so disable\nthose to avoid hashing/caching overhead.\n\n## Client Usage\n\nTask-specific prompts (note the two new tasks `spotting` and `seal`):\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(api_key=\"EMPTY\", base_url=\"http://localhost:8000/v1\", timeout=3600)\n\nTASKS = {\n \"ocr\": \"OCR:\",\n \"table\": \"Table Recognition:\",\n \"formula\": \"Formula Recognition:\",\n \"chart\": \"Chart Recognition:\",\n \"spotting\": \"Spotting:\",\n \"seal\": \"Seal Recognition:\",\n}\n\nresponse = client.chat.completions.create(\n model=\"PaddlePaddle/PaddleOCR-VL-1.5\",\n messages=[{\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"image_url\", \"image_url\": {\"url\": \"https://.../receipt.png\"}},\n {\"type\": \"text\", \"text\": TASKS[\"spotting\"]},\n ],\n }],\n temperature=0.0,\n)\nprint(response.choices[0].message.content)\n```\n\n## Offline Inference with PP-DocLayoutV2\n\nUse separate venvs for `vllm` and `paddlepaddle` to avoid conflicts. If you see\n\"The model PaddleOCR-VL-1.5-0.9B does not exist.\", add\n`--served-model-name PaddleOCR-VL-1.5-0.9B`.\n\n```bash\nuv pip install paddlepaddle-gpu==3.2.1 --extra-index-url https://www.paddlepaddle.org.cn/packages/stable/cu126/\nuv pip install -U \"paddleocr[doc-parser]\"\nuv pip install safetensors\n```\n\n```python\nfrom paddleocr import PaddleOCRVL\n\npipeline = PaddleOCRVL(\n vl_rec_model_name=\"PaddleOCR-VL-1.5-0.9B\",\n vl_rec_backend=\"vllm-server\",\n vl_rec_server_url=\"http://localhost:8000/v1\",\n layout_detection_model_name=\"PP-DocLayoutV2\",\n layout_detection_model_dir=\"/path/to/your/PP-DocLayoutV2/\",\n)\n\noutput = pipeline.predict(\"https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/paddleocr_vl_demo.png\")\nfor i, res in enumerate(output):\n res.save_to_json(save_path=f\"output_{i}.json\")\n res.save_to_markdown(save_path=f\"output_{i}.md\")\n```\n\n## References\n\n- [PaddleOCR-VL-1.5 on Hugging Face](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5)\n- [PaddleOCR GitHub](https://github.com/PaddlePaddle/PaddleOCR)\n- [vLLM support tracking issue](https://github.com/vllm-project/vllm/issues/33554)\n", "source": { "source": "vllm", "repo": "vllm-project/vllm", "ref": "main", "sha": "389ffebe555705c85337025d5df108d0384d365e", "path": "recipes/models/PaddlePaddle/PaddleOCR-VL-1.5.yaml", "edit_url": "https://github.com/vllm-project/vllm/edit/main/recipes/models/PaddlePaddle/PaddleOCR-VL-1.5.yaml", "overlays": [] } }