Instructions to use noctuashap/ZhiXin_embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use noctuashap/ZhiXin_embedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="noctuashap/ZhiXin_embedding")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("noctuashap/ZhiXin_embedding") model = AutoModel.from_pretrained("noctuashap/ZhiXin_embedding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: mit
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# ZhiXin_embedding
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<!-- Provide a quick summary of what the model is/does. -->
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This model contains the fine-tuned retrieval embeddings of the ZhiXin model. For the fine-tuned model, see ```ZhiXin_model```.
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ZhiXin, introduced in the paper ```ZhiXin – A RAG-based Virtual Assistant for Persons with Intellectual and Developmental Disabilities```, is a Retrieval-Augmented Generation (RAG) language model system specifically tailored to support parents of children with intellectual and developmental disorders. The core component of ZhiXin is a fine-tuned language model based on the ```Qwen2-7b-Instruct``` model. Leveraging Supervised Fine-Tuning (SFT) techniques, this specialized LLM has been trained to understand and address the unique challenges faced by parents and caregivers, providing relevant, empathetic, and reliable guidance.
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