GLM-4-9B-Chat

2024年7月24日，我们发布了与长文本相关的最新技术解读，关注这里查看我们在训练GLM-4-9B开源模型中关于长文本技术的技术报告

模型介绍

GLM-4-9B是智谱AI推出的最新一代预训练模型GLM-4系列中的开源版本。在语义、数学、推理、代码和知识等多方面的数据集测评中，GLM-4-9B及其人类偏好对齐的版本GLM-4-9B-Chat均表现出较高的性能。除了能进行多轮对话，GLM-4-9B-Chat还具备网页浏览、代码执行、自定义工具调用（Function Call）和长文本推理（支持最大128K上下文）等高级功能。本代模型增加了多语言支持，支持包括日语、韩语、德语在内的26种语言。我们还推出了支持1M上下文长度（约200万中文字符）的模型。

评测结果

我们在一些经典任务上对GLM-4-9B-Chat模型进行了评测，并得到了如下的结果：

Model	AlignBench-v2	MT-Bench	IFEval	MMLU	C-Eval	GSM8K	MATH	HumanEval	NCB
Llama-3-8B-Instruct	5.12	8.00	68.58	68.4	51.3	79.6	30.0	62.2	24.7
ChatGLM3-6B	3.97	5.50	28.1	66.4	69.0	72.3	25.7	58.5	11.3
GLM-4-9B-Chat	6.61	8.35	69.0	72.4	75.6	79.6	50.6	71.8	32.2

多语言能力

在六个多语言数据集上对GLM-4-9B-Chat和Llama-3-8B-Instruct进行了测试，测试结果及数据集对应选取语言如下表

Dataset	Llama-3-8B-Instruct	GLM-4-9B-Chat	Languages
M-MMLU	49.6	56.6	all
FLORES	25.0	28.8	ru, es, de, fr, it, pt, pl, ja, nl, ar, tr, cs, vi, fa, hu, el, ro, sv, uk, fi, ko, da, bg, no
MGSM	54.0	65.3	zh, en, bn, de, es, fr, ja, ru, sw, te, th
XWinograd	61.7	73.1	zh, en, fr, jp, ru, pt
XStoryCloze	84.7	90.7	zh, en, ar, es, eu, hi, id, my, ru, sw, te
XCOPA	73.3	80.1	zh, et, ht, id, it, qu, sw, ta, th, tr, vi

工具调用能力

我们在 Berkeley Function Calling Leaderboard 上进行了测试并得到了以下结果：

Model	Overall Acc.	AST Summary	Exec Summary	Relevance
Llama-3-8B-Instruct	58.88	59.25	70.01	45.83
gpt-4-turbo-2024-04-09	81.24	82.14	78.61	88.75
ChatGLM3-6B	57.88	62.18	69.78	5.42
GLM-4-9B-Chat	81.00	80.26	84.40	87.92

本仓库是 GLM-4-9B-Chat 的模型仓库，支持128K上下文长度。

运行模型

使用 openMind 进行推理:

import torch
from openmind import AutoModelForCausalLM, AutoTokenizer

device = "npu"

tokenizer = AutoTokenizer.from_pretrained("AI-Research/glm-4-9b-chat",trust_remote_code=True)

query = "你好"

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(
    "AI-Research/glm-4-9b-chat",
    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))

协议

GLM-4 模型的权重的使用则需要遵循 LICENSE。

引用

如果你觉得我们的工作有帮助的话，请考虑引用下列论文。

@misc{glm2024chatglm,
      title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools},
      author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang},
      year={2024},
      eprint={2406.12793},
      archivePrefix={arXiv},
      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}