Instructions to use intfloat/llm-retriever-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intfloat/llm-retriever-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="intfloat/llm-retriever-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("intfloat/llm-retriever-base") model = AutoModel.from_pretrained("intfloat/llm-retriever-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c1d6b8d2199307e7e557f966db735325a618d78b63a0fcb2227e12f6cc84f8f
- Size of remote file:
- 438 MB
- SHA256:
- 1f2eb87aef91598f4f7677ea4b553d4bada275c89160b7a18b9efa43ed1f2643
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.