--- license: other license_name: glm-4 license_link: https://huggingface.co/THUDM/glm-4-9b-chat/blob/main/LICENSE language: - en - zh library_name: transformers pipeline_tag: text-generation base_model: THUDM/glm-4-9b-chat tags: - Mental Health - Chatbot - LLM --- # Update **The model is now following the update from GLM-4-9B-Chat and now requires `transformers>=4.44.0`. Please update your dependencies accordingly.** **Also follow the [dependencies](https://github.com/THUDM/GLM-4/blob/main/basic_demo/requirements.txt) it before using** # Introduction This model is [GLM-4-9B-Chat](https://huggingface.co/THUDM/glm-4-9b-chat/tree/main), fine-tuned with the [Smile dataset](https://github.com/qiuhuachuan/smile) to focus on mental health care. Since it is fine-tuned with a Chinese dataset, please use it in Chinese, even though the base model supports English text. # Use the following method to quickly call the GLM-4-9B-Chat language model Use the transformers backend for inference: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" tokenizer = AutoTokenizer.from_pretrained("derek33125/project-angel-chatglm4", trust_remote_code=True) query = "我感到很悲伤" 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( "derek33125/project-angel-chatglm4", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True ).to(device).eval() gen_kwargs = {"max_length": 2500, "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)) ``` It also supports [VLLM](https://github.com/THUDM/GLM-4/blob/main/basic_demo/openai_api_server.py) and [LangChain](https://python.langchain.com/v0.2/docs/integrations/llms/huggingface_pipelines/) .