Feature Extraction
Transformers
Safetensors
qwen3_5_text
embeddings
sentence-similarity
information-retrieval
memory-retrieval
chinese
etsu
qwen3.5
Instructions to use Rebine/Etsu-Embedding-0.8B-Memory with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rebine/Etsu-Embedding-0.8B-Memory with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Rebine/Etsu-Embedding-0.8B-Memory")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Rebine/Etsu-Embedding-0.8B-Memory") model = AutoModel.from_pretrained("Rebine/Etsu-Embedding-0.8B-Memory", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Rebine/Etsu-Embedding-0.8B-Memory: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/Rebine/Etsu-Embedding-0.8B-Memory/resolve/main/tokenizer.json
- Command line
-
hf download hf://Rebine/Etsu-Embedding-0.8B-Memory/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Rebine/Etsu-Embedding-0.8B-Memory/resolve/main/tokenizer.json
20 MB
- Xet hash:
- d2ce249cdd4ff29ef30afe68cbfa4045522e3939221d5aa2004e590b2c85fa26
- Size of remote file:
- 20 MB
- SHA256:
- 6ae9719a160898e8c38d3072c2862d8b3cf610c4069e447a8dfd457cff2a6c0f
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