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README.md

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 license: gpl-3.0
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+# TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
+
+> [Shaolei Zhang](https://zhangshaolei1998.github.io/), Tian Yu, [Yang Feng](https://people.ucas.edu.cn/~yangfeng?language=en)*
+
+**TruthX** is an inference-time method to elicit the truthfulness of LLMs by editing their internal representations in truthful space, thereby mitigating the hallucinations of LLMs. On the [TruthfulQA benchmark](https://paperswithcode.com/sota/question-answering-on-truthfulqa), TruthX yields an average **enhancement of 20% in truthfulness** across 13 advanced LLMs.
+
+<div  align="center">   
+  <img src="./truthx_results.png" alt="img" width="100%" />
+</div>
+<p align="center">
+  TruthfulQA MC1 accuracy of TruthX across 13 advanced LLMs
+</p>
+
+This repo provide **Llama-2-7B-Chat-TruthX**, a Llama-2-7B-Chat model with baked-in TruthX model. You can directly download this baked-in model and use it like standard Llama, no additional operations are required.
+
+## Quick Starts
+Inference with Llama-2-7B-Chat-TruthX:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+llama2chat_with_truthx = "ICTNLP/Llama-2-7b-chat-TruthX"
+tokenizer = AutoTokenizer.from_pretrained(llama2chat_with_truthx, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(llama2chat_with_truthx, trust_remote_code=True,torch_dtype=torch.float16).cuda()
+
+question = "What are the benefits of eating an apple a day?"
+encoded_inputs = tokenizer(question, return_tensors="pt")["input_ids"]
+outputs = model.generate(encoded_inputs.cuda())[0, encoded_inputs.shape[-1] :]
+outputs_text = tokenizer.decode(outputs, skip_special_tokens=True).strip()
+print(outputs_text)
+```
+
+Please refer to [GitHub repo](https://github.com/ictnlp/TruthX) and our paper for more details.