Instructions to use LimitedMouse/Generative-Embedding-Benchmark-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LimitedMouse/Generative-Embedding-Benchmark-Checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LimitedMouse/Generative-Embedding-Benchmark-Checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download qwen3vl-2b/visual_only/tokenizer.json from LimitedMouse/Generative-Embedding-Benchmark-Checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/LimitedMouse/Generative-Embedding-Benchmark-Checkpoints/resolve/main/qwen3vl-2b/visual_only/tokenizer.json
- Command line
-
hf download hf://LimitedMouse/Generative-Embedding-Benchmark-Checkpoints/qwen3vl-2b/visual_only/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/LimitedMouse/Generative-Embedding-Benchmark-Checkpoints/resolve/main/qwen3vl-2b/visual_only/tokenizer.json
11.4 MB
- Xet hash:
- 3cc41d81d95c2ff8907b8d805537d56069e2c2a100aaf5b1d59b30eb40bc4af9
- Size of remote file:
- 11.4 MB
- SHA256:
- 3ebcdbf422aff6939ecdff16d65f85124465337acfa29b232cf318b71a814214
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