Update README.md

README.md

@@ -1,3 +1,58 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+
+
+# Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language models
+
+> [Tian Yu](https://tianyu0313.github.io/), [Shaolei Zhang](https://zhangshaolei1998.github.io/), and [Yang Feng](https://people.ucas.edu.cn/~yangfeng?language=en)*
+
+
+## Model Details
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+- **Discription:** These are the LoRA weights obtained by training with synthesized iterative retrieval instruction data. Details can be found in our paper.
+- **Developed by:** ICTNLP Group. Authors: Tian Yu, Shaolei Zhang and Yang Feng.
+- **Github Repository:** https://github.com/ictnlp/Auto-RAG
+- **Finetuned from model:** Meta-Llama3-8B-Instruct
+
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+Merge the Meta-Llama3-8B-Instruct weights and Adapter weights.
+
+```
+import os
+from transformers import AutoTokenizer, LlamaForCausalLM
+import torch
+
+model = LlamaForCausalLM.from_pretrained(PATH_TO_META_LLAMA3_8B_INSTRUCT,
+                                  device_map="cpu", 
+                                  )
+from peft import PeftModel
+
+model = PeftModel.from_pretrained(model, 
+                                  PATH_TO_ADAPTER)
+
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained(PATH_TO_META_LLAMA3_8B_INSTRUCT)
+
+model = model.merge_and_unload()
+model.save_pretrained(SAVE_PATH)
+tokenizer.save_pretrained(SAVE_PATH)
+```
+
+Subsequently, you can deploy using frameworks such as vllm.
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+