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 modeling_markupdm.py
Browse files- modeling_markupdm.py +3 -3
modeling_markupdm.py
CHANGED
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@@ -105,12 +105,12 @@ class MarkupDMForCausalLM(PreTrainedModel, GenerationMixin): # type: ignore
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def from_pretrained(cls, *args: Any, **kwargs: Any) -> "MarkupDMForCausalLM":
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assert "config" in kwargs, "Config must be provided"
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config = kwargs["config"]
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-
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# Initialize text model
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text_model = AutoModelForCausalLM.from_config(
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config.text_model,
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-
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attn_implementation=config._attn_implementation,
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)
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@@ -119,7 +119,7 @@ class MarkupDMForCausalLM(PreTrainedModel, GenerationMixin): # type: ignore
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vision_model = AutoModel.from_config(
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config.vision_model,
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trust_remote_code=True,
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-
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)
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return super().from_pretrained( # type: ignore
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def from_pretrained(cls, *args: Any, **kwargs: Any) -> "MarkupDMForCausalLM":
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assert "config" in kwargs, "Config must be provided"
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config = kwargs["config"]
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+
dtype = kwargs.get("dtype", kwargs.get("torch_dtype", None))
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# Initialize text model
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text_model = AutoModelForCausalLM.from_config(
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config.text_model,
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dtype=dtype,
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attn_implementation=config._attn_implementation,
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)
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vision_model = AutoModel.from_config(
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config.vision_model,
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trust_remote_code=True,
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+
dtype=dtype,
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)
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return super().from_pretrained( # type: ignore
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