Text Generation
Transformers
Safetensors
English
markupdm
graphic design
design completion
multimodal
markup document
custom_code
Instructions to use cyberagent/markupdm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cyberagent/markupdm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cyberagent/markupdm", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("cyberagent/markupdm", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use cyberagent/markupdm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cyberagent/markupdm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cyberagent/markupdm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cyberagent/markupdm
- SGLang
How to use cyberagent/markupdm 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 "cyberagent/markupdm" \ --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": "cyberagent/markupdm", "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 "cyberagent/markupdm" \ --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": "cyberagent/markupdm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cyberagent/markupdm with Docker Model Runner:
docker model run hf.co/cyberagent/markupdm
Update processing_markupdm.py
Browse files- processing_markupdm.py +2 -3
processing_markupdm.py
CHANGED
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@@ -9,9 +9,8 @@ from pathlib import Path
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import numpy as np
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import torch
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from
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from PIL import Image, ImageDraw
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from svg import Style as SVGStyle
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from transformers import (
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ImageProcessingMixin,
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PreTrainedModel,
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@@ -430,7 +429,7 @@ class MarkupDMProcessor(ProcessorMixin): # type: ignore
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matched = re.search("<svg[^>]*>", svg)
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assert matched is not None, "SVG tag not found"
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i = matched.span()[1]
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style =
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example["svg"] = svg[:i] + style.as_str() + svg[i:]
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def render(self, example: dict, save_dir: str | Path | None = None) -> Image.Image:
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import numpy as np
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import torch
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from .fonts import FontManager
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from PIL import Image, ImageDraw
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from transformers import (
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ImageProcessingMixin,
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PreTrainedModel,
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matched = re.search("<svg[^>]*>", svg)
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assert matched is not None, "SVG tag not found"
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i = matched.span()[1]
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style = f"<style>{style_text}</style>"
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example["svg"] = svg[:i] + style.as_str() + svg[i:]
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def render(self, example: dict, save_dir: str | Path | None = None) -> Image.Image:
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