Zero-Shot Object Detection
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
text-generation-inference
Instructions to use IDEA-Research/Rex-Thinker-GRPO-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IDEA-Research/Rex-Thinker-GRPO-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-object-detection", model="IDEA-Research/Rex-Thinker-GRPO-7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("IDEA-Research/Rex-Thinker-GRPO-7B") model = AutoModelForMultimodalLM.from_pretrained("IDEA-Research/Rex-Thinker-GRPO-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from IDEA-Research/Rex-Thinker-GRPO-7B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/IDEA-Research/Rex-Thinker-GRPO-7B/resolve/main/tokenizer.json
- Command line
-
hf download hf://IDEA-Research/Rex-Thinker-GRPO-7B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/IDEA-Research/Rex-Thinker-GRPO-7B/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- c2877dd5dfe3bbab19b1893534617e49012fa3a9ab6df5f20a9e0450dcf90e0d
- Size of remote file:
- 11.4 MB
- SHA256:
- 00c666486c97eb3968579ff4eef9fff09b6ac5dd5a48c8906d5d3056a1f3e71a
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