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 it before using

Introduction

This model is GLM-4-9B-Chat, 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
SoulChat
single_turn_dataset_1 from EMOLLM
[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 and LangChain .

derek33125/project-angel-chatglm4-v2

Update

Introduction

Dataset

Training

Use the following method to quickly call the GLM-4-9B-Chat language model

derek33125/
project-angel-chatglm4-v2