Instructions to use openbmb/MiniCPM3-RAG-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use openbmb/MiniCPM3-RAG-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM3-4B") model = PeftModel.from_pretrained(base_model, "openbmb/MiniCPM3-RAG-LoRA") - Notebooks
- Google Colab
- Kaggle
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We also invite you to explore `MiniCPM3` and the RAG toolkit series:
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- Foundation Model: [MiniCPM3](https://huggingface.co/openbmb/MiniCPM3-4B)
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- Retrieval Model: [MiniCPM-Embedding](https://huggingface.co/openbmb/MiniCPM-Embedding)
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- Re-ranking Model: [MiniCPM-Reranker](https://huggingface.co/openbmb/MiniCPM-Reranker)
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- LoRA Plugin for RAG scenarios: [MiniCPM3-RAG-LoRA](https://huggingface.co/openbmb/MiniCPM3-RAG-LoRA)
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We also invite you to explore `MiniCPM3` and the RAG toolkit series:
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- Foundation Model: [MiniCPM3](https://huggingface.co/openbmb/MiniCPM3-4B)
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- Retrieval (Embedding) Model: [MiniCPM-Embedding](https://huggingface.co/openbmb/MiniCPM-Embedding)
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- Re-ranking Model: [MiniCPM-Reranker](https://huggingface.co/openbmb/MiniCPM-Reranker)
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- LoRA Plugin for RAG scenarios: [MiniCPM3-RAG-LoRA](https://huggingface.co/openbmb/MiniCPM3-RAG-LoRA)
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