derek33125
/

project-angel-chatglm4-v2

@@ -1,200 +1,66 @@
 ---
 library_name: transformers
 tags:
 - llama-factory
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+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
 - llama-factory
+- EMOLLM
 ---
+# 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 various datasets 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.
+# Dataset
+- [Smile dataset](https://github.com/qiuhuachuan/smile)
+- [SoulChat](https://github.com/scutcyr/SoulChat)
+- [single_turn_dataset_1 from EMOLLM](https://github.com/SmartFlowAI/EmoLLM/blob/main/datasets/single_turn_dataset_1.json)
+- [self-defined role-playing dataset]
+# Training
+Using LLaMA-Factory to do the fine-tuning process. Here are the parameters:
+(TODO)
+# 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/) .