Instructions to use graph-based-captions/GBC10M-PromptGen-200M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use graph-based-captions/GBC10M-PromptGen-200M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="graph-based-captions/GBC10M-PromptGen-200M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("graph-based-captions/GBC10M-PromptGen-200M")
model = AutoModelForCausalLM.from_pretrained("graph-based-captions/GBC10M-PromptGen-200M")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use graph-based-captions/GBC10M-PromptGen-200M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "graph-based-captions/GBC10M-PromptGen-200M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "graph-based-captions/GBC10M-PromptGen-200M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/graph-based-captions/GBC10M-PromptGen-200M

SGLang

How to use graph-based-captions/GBC10M-PromptGen-200M 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 "graph-based-captions/GBC10M-PromptGen-200M" \
    --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": "graph-based-captions/GBC10M-PromptGen-200M",
		"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 "graph-based-captions/GBC10M-PromptGen-200M" \
        --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": "graph-based-captions/GBC10M-PromptGen-200M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use graph-based-captions/GBC10M-PromptGen-200M with Docker Model Runner:
```
docker model run hf.co/graph-based-captions/GBC10M-PromptGen-200M
```

Add library_name, pipeline_tag and project page

by nielsr HF Staff - opened Mar 1, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

Files changed (1) hide show

README.md +7 -3

README.md CHANGED Viewed

@@ -1,9 +1,11 @@
 ---
-license: apple-ascl
 datasets:
 - graph-based-captions/GBC10M
 language:
 - en
 ---
 ### Graph-based captioning (GBC) is a new image annotation paradigm that combines the strengths of long captions, region captions, and scene graphs
@@ -11,10 +13,9 @@ language:
 GBC interconnects region captions to create a unified description akin to a long caption, while also providing structural information similar to scene graphs.
 ![assets/GBC_illustration.png](assets/GBC_illustration.png)
 ### Text-to-Image with GBC as Middleware
-We propose to use GBC as middleware for text-to-image generation. This repository provides model for generating GBC annotation from simple text prompt.
 ![assets/GBC_promptgen.png](assets/GBC_promptgen.png)
 For futher detail on how to use the model please refer to the [accompanying code repository](https://github.com/apple/ml-gbc?tab=readme-ov-file#-gbc-text-to-image).
@@ -32,3 +33,6 @@ For license please checkout the [LICENSE](LICENSE) file.
   journal={arXiv preprint arXiv:2407.06723},
   year={2024}
 }

 ---
 datasets:
 - graph-based-captions/GBC10M
 language:
 - en
+license: apple-ascl
+library_name: transformers
+pipeline_tag: text-generation
 ---
 ### Graph-based captioning (GBC) is a new image annotation paradigm that combines the strengths of long captions, region captions, and scene graphs
 GBC interconnects region captions to create a unified description akin to a long caption, while also providing structural information similar to scene graphs.
 ![assets/GBC_illustration.png](assets/GBC_illustration.png)
 ### Text-to-Image with GBC as Middleware
+We propose to use GBC as middleware for text-to-image generation. This repository provides a model for generating GBC annotation from a simple text prompt.
 ![assets/GBC_promptgen.png](assets/GBC_promptgen.png)
 For futher detail on how to use the model please refer to the [accompanying code repository](https://github.com/apple/ml-gbc?tab=readme-ov-file#-gbc-text-to-image).
   journal={arXiv preprint arXiv:2407.06723},
   year={2024}
 }
+```
+[Project page](https://huggingface.co/graph-based-captions)