Image-to-Text
Transformers
Safetensors
English
reward-model
poster
graphic-design
image-quality-assessment
preference-learning
qwen3-vl
Instructions to use MeiGen-AI/PosterReward_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MeiGen-AI/PosterReward_v1 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="MeiGen-AI/PosterReward_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MeiGen-AI/PosterReward_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download PosterReward-Lite/tokenizer.json from MeiGen-AI/PosterReward_v1: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/MeiGen-AI/PosterReward_v1/resolve/main/PosterReward-Lite/tokenizer.json
- Command line
-
hf download hf://MeiGen-AI/PosterReward_v1/PosterReward-Lite/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/MeiGen-AI/PosterReward_v1/resolve/main/PosterReward-Lite/tokenizer.json
11.4 MB
- Xet hash:
- 693ec4b3922b0bd306bf7b4989e115ffbfeb7b0c08b31bc6d956818c6bb07f61
- Size of remote file:
- 11.4 MB
- SHA256:
- aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
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