初始化项目，由ModelHub XC社区提供模型

Model: ICTNLP/Llama-2-7b-chat-TruthX
Source: Original Platform

README.md

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+---
+license: gpl-3.0
+---
+# TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space
+
+> [Shaolei Zhang](https://zhangshaolei1998.github.io/), [Tian Yu](https://tianyu0313.github.io/), [Yang Feng](https://people.ucas.edu.cn/~yangfeng?language=en)*
+
+Model for paper "[TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space](https://arxiv.org/pdf/2402.17811.pdf)".
+
+
+**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 provides **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](https://arxiv.org/pdf/2402.17811.pdf) for more details.
+
+## Licence
+Model weights and the inference code are released under The GNU General Public License v3.0 (GPLv3)
+
+## Citation
+
+If this repository is useful for you, please cite as:
+
+```
+@misc{zhang2024truthx,
+      title={TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space}, 
+      author={Shaolei Zhang and Tian Yu and Yang Feng},
+      year={2024},
+      eprint={2402.17811},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2402.17811}
+}
+```
+
+If you have any questions, feel free to contact `zhangshaolei20z@ict.ac.cn`.