Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- zh
+base_model:
+- THUDM/glm-4-9b
+pipeline_tag: text-generation
+---
+# MentalGLM is a series of large language models designed for mental health analysis tasks in Chinese.
+We have developed the MentalGLM series, the first Chinese open-source interpretable large language models for mental health analysis, based on GLM-4-9b and GLM-4-9b-chat.
+
+## How to use
+
+```bash
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+device = "cuda"
+
+tokenizer = AutoTokenizer.from_pretrained("zwzzz/MentalGLM", trust_remote_code=True)
+
+query = "考虑以下这个帖子，帖子体现了什么认知路径？这已经够糟糕的了。不过在那一周我将完全失去我的支持。我没有什么可期待的。"
+
+inputs = tokenizer.apply_chat_template([{"role": "user", "content": query}],
+                                       add_generation_prompt=True,
+                                       tokenize=True,
+                                       return_tensors="pt",
+                                       return_dict=True
+                                       )
+
+inputs = inputs.to(device)
+model = AutoModelForCausalLM.from_pretrained(
+    "zwzzz/MentalGLM",
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True
+).to(device).eval()
+
+gen_kwargs = {"max_length": 1000, "do_sample": True, "top_k": 1}
+with torch.no_grad():
+    outputs = model.generate(**inputs, **gen_kwargs)
+    outputs = outputs[:, inputs['input_ids'].shape[1]:]
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Citation
+
+If you find the technical report or resource is useful, please cite the following technical report in your paper.
+
+Article address：[https://arxiv.org/pdf/2410.10323.pdf](https://arxiv.org/pdf/2410.10323.pdf)
+```bash
+@article{zhai2024mentalglm,
+  title={MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media},
+  author={Zhai, Wei and Bai, Nan and Zhao, Qing and Li, Jianqiang and Wang, Fan and Qi, Hongzhi and Jiang, Meng and Wang, Xiaoqin and Yang, Bing Xiang and Fu, Guanghui},
+  journal={arXiv preprint arXiv:2410.10323},
+  year={2024}
+}
+```