Feature Extraction
Transformers
Safetensors
English
mistral
medical
biology
retrieval
LLM
text-embeddings-inference
Instructions to use BMRetriever/BMRetriever-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BMRetriever/BMRetriever-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BMRetriever/BMRetriever-7B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BMRetriever/BMRetriever-7B") model = AutoModel.from_pretrained("BMRetriever/BMRetriever-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 307cf77790372b61b795c231260dc0d8caf3e67ee1c783d3b51b9417a5f7e389
- Size of remote file:
- 3.72 GB
- SHA256:
- 46f256da077a6a06a4f4cf1f1ac83d7b9a612d69094bbc845f5b1a3c78db7cfc
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