README_en.md

# GLM-4

<p align="center">
🤗 <a href="https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7" target="_blank">HF Repo</a> • 🤖 <a href="https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat" target="_blank">ModelScope</a> • 🐦 <a href="https://twitter.com/thukeg" target="_blank">Twitter</a> • 👋 Join <a href="https://join.slack.com/t/chatglm/shared_invite/zt-25ti5uohv-A_hs~am_D3Q8XPZMpj7wwQ" target="_blank">Slack</a> and <a href="resources/WECHAT.md" target="_blank">WeChat</a>
</p>
<p align="center">
📍Experience and use a larger-scale GLM business model on the <a href="https://open.bigmodel.cn/?utm_campaign=open&_channel_track_key=OWTVNma9">Zhipu AI Open Platform</a>

</p>

## Model Introduction

GLM-4-9B is the open-source version of the latest generation of pre-trained models in the GLM-4 series launched by Zhipu
AI. In the evaluation of data sets in semantics, mathematics, reasoning, code, and knowledge, **GLM-4-9B**
and its human preference-aligned version **GLM-4-9B-Chat** have shown superior performance beyond Llama-3-8B. In
addition to multi-round conversations, GLM-4-9B-Chat also has advanced features such as web browsing, code execution,
custom tool calls (Function Call), and long text
reasoning (supporting up to 128K context). This generation of models has added multi-language support, supporting 26
languages including Japanese, Korean, and German. We have also launched the **GLM-4-9B-Chat-1M** model that supports 1M
context length (about 2 million Chinese characters) and the multimodal model GLM-4V-9B based on GLM-4-9B.
**GLM-4V-9B** possesses dialogue capabilities in both Chinese and English at a high resolution of 1120*1120.
In various multimodal evaluations, including comprehensive abilities in Chinese and English, perception & reasoning,
text recognition, and chart understanding, GLM-4V-9B demonstrates superior performance compared to
GPT-4-turbo-2024-04-09, Gemini 1.0 Pro, Qwen-VL-Max, and Claude 3 Opus.

## Model List

| Model            | Type | Seq Length | Download                                                                                                                                | Online Demo                                                                                                                                                                                |
|------------------|------|------------|-----------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| GLM-4-9B         | Base | 8K         | [🤗 Huggingface](https://huggingface.co/THUDM/glm-4-9b)  [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/glm-4-9b)                 | /                                                                                                                                                                                          |
| GLM-4-9B-Chat    | Chat | 128K       | [🤗 Huggingface](https://huggingface.co/THUDM/glm-4-9b-chat)  [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat)       | [🤖 ModelScope CPU](https://modelscope.cn/studios/dash-infer/GLM-4-Chat-DashInfer-Demo/summary)<br> [🤖 ModelScope vLLM](https://modelscope.cn/studios/ZhipuAI/glm-4-9b-chat-vllm/summary) |
| GLM-4-9B-Chat-1M | Chat | 1M         | [🤗 Huggingface](https://huggingface.co/THUDM/glm-4-9b-chat-1m)  [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat-1m) | /                                                                                                                                                                                          |
| GLM-4V-9B        | Chat | 8K         | [🤗 Huggingface](https://huggingface.co/THUDM/glm-4v-9b)  [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/glm-4v-9b)               | /                                                                                                                                                                                          |

## BenchMark

### Typical Tasks

| Model               | AlignBench | MT-Bench | IFEval | MMLU | C-Eval | GSM8K | MATH | HumanEval | NaturalCodeBench |
|:--------------------|:----------:|:--------:|:------:|:----:|:------:|:-----:|:----:|:---------:|:----------------:|
| Llama-3-8B-Instruct |    6.40    |   8.00   | 68.58  | 68.4 |  51.3  | 79.6  | 30.0 |   62.2    |       24.7       |
| ChatGLM3-6B         |    5.18    |   5.50   |  28.1  | 66.4 |  69.0  | 72.3  | 25.7 |   58.5    |       11.3       |
| GLM-4-9B-Chat       |    7.01    |   8.35   |  69.0  | 72.4 |  75.6  | 79.6  | 50.6 |   71.8    |       32.2       |

### Base Model

| Model               | MMLU | C-Eval | GPQA | GSM8K | MATH | HumanEval |
|:--------------------|:----:|:------:|:----:|:-----:|:----:|:---------:|
| Llama-3-8B          | 66.6 |  51.2  |  -   | 45.8  |  -   |   33.5    | 
| Llama-3-8B-Instruct | 68.4 |  51.3  | 34.2 | 79.6  | 30.0 |   62.2    |
| ChatGLM3-6B-Base    | 61.4 |  69.0  | 26.8 | 72.3  | 25.7 |   58.5    |
| GLM-4-9B            | 74.7 |  77.1  | 34.3 | 84.0  | 30.4 |   70.1    |

> Since `GLM-4-9B` adds some math, reasoning, and code-related instruction data during pre-training, Llama-3-8B-Instruct
> is also included in the comparison range.

### Long Context

The [needle-in-the-haystack experiment](https://github.com/LargeWorldModel/LWM/blob/main/scripts/eval_needle.py) was
conducted with a context length of 1M, and the results are as follows:

![needle](resources/eval_needle.jpeg)

The long text capability was further evaluated on LongBench-Chat, and the results are as follows:

<p align="center">
<img src="resources/longbench.png" alt="Description text" style="display: block; margin: auto; width: 65%;">
</p>

### Multi Language

The tests for GLM-4-9B-Chat and Llama-3-8B-Instruct are conducted on six multilingual datasets. The test results and the
corresponding languages selected for each dataset are shown in the table below:

| 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                           |

### Function Call

Tested
on [Berkeley Function Calling Leaderboard](https://github.com/ShishirPatil/gorilla/tree/main/berkeley-function-call-leaderboard).

| 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   |

### Multi-Modal

GLM-4V-9B is a multimodal language model with visual understanding capabilities. The evaluation results of its related
classic tasks are as follows:

|                            | **MMBench-EN-Test** | **MMBench-CN-Test** | **SEEDBench_IMG** | **MMStar** | **MMMU** | **MME** | **HallusionBench** | **AI2D** | **OCRBench** |
|----------------------------|---------------------|---------------------|-------------------|------------|----------|---------|--------------------|----------|--------------|
| **gpt-4o-2024-05-13**      | 83.4                | 82.1                | 77.1              | 63.9       | 69.2     | 2310.3  | 55                 | 84.6     | 736          |
| **gpt-4-turbo-2024-04-09** | 81.0                | 80.2                | 73.0              | 56.0       | 61.7     | 2070.2  | 43.9               | 78.6     | 656          |
| **gpt-4-1106-preview**     | 77.0                | 74.4                | 72.3              | 49.7       | 53.8     | 1771.5  | 46.5               | 75.9     | 516          |
| **InternVL-Chat-V1.5**     | 82.3                | 80.7                | 75.2              | 57.1       | 46.8     | 2189.6  | 47.4               | 80.6     | 720          |
| **LLaVA-Next-Yi-34B**      | 81.1                | 79                  | 75.7              | 51.6       | 48.8     | 2050.2  | 34.8               | 78.9     | 574          |
| **Step-1V**                | 80.7                | 79.9                | 70.3              | 50.0       | 49.9     | 2206.4  | 48.4               | 79.2     | 625          |
| **MiniCPM-Llama3-V2.5**    | 77.6                | 73.8                | 72.3              | 51.8       | 45.8     | 2024.6  | 42.4               | 78.4     | 725          |
| **Qwen-VL-Max**            | 77.6                | 75.7                | 72.7              | 49.5       | 52       | 2281.7  | 41.2               | 75.7     | 684          |
| **Gemini 1.0 Pro**         | 73.6                | 74.3                | 70.7              | 38.6       | 49       | 2148.9  | 45.7               | 72.9     | 680          |
| **Claude 3 Opus**          | 63.3                | 59.2                | 64                | 45.7       | 54.9     | 1586.8  | 37.8               | 70.6     | 694          |
| **GLM-4V-9B**              | 81.1                | 79.4                | 76.8              | 58.7       | 47.2     | 2163.8  | 46.6               | 81.1     | 786          |

## Quick call

**硬件配置和系统要求，请查看[这里](basic_demo/README_en.md)。**

### 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("THUDM/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(
    "THUDM/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))
```

Use the vLLM backend for inference:

```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams


# GLM-4-9B-Chat
# If you encounter OOM, you can try to reduce max_model_len or increase tp_size
max_model_len, tp_size = 131072, 1
model_name = "THUDM/glm-4-9b-chat"
prompt = [{"role": "user", "content": "你好"}]

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
llm = LLM(
    model=model_name,
    tensor_parallel_size=tp_size,
    max_model_len=max_model_len,
    trust_remote_code=True,
    enforce_eager=True,
    # if you encounter OOM in GLM-4-9B-Chat-1M, you can try to enable the following parameters
    # enable_chunked_prefill=True,
    # max_num_batched_tokens=8192
)
stop_token_ids = [151329, 151336, 151338]
sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids)

inputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
outputs = llm.generate(prompts=inputs, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)

```

### Use the following method to quickly call the GLM-4V-9B multimodal model

Use the transformers backend for inference:

```python
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"

tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4v-9b", trust_remote_code=True)

query = 'display this image'
image = Image.open("your image").convert('RGB')
inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}],
                                       add_generation_prompt=True, tokenize=True, return_tensors="pt",
                                       return_dict=True)  # chat mode

inputs = inputs.to(device)
model = AutoModelForCausalLM.from_pretrained(
    "THUDM/glm-4v-9b",
    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]))
```

Note: GLM-4V-9B does not support calling using vLLM method yet.

## Complete project list

If you want to learn more about the GLM-4-9B series open source models, this open source repository provides developers
with basic GLM-4-9B usage and development code through the following content

+ [base](basic_demo/README.md): Contains
+ Interaction code using transformers and vLLM backend
+ OpenAI API backend interaction code
+ Batch reasoning code

+ [composite_demo](composite_demo/README.md): Contains
+ Fully functional demonstration code for GLM-4-9B and GLM-4V-9B open source models, including All Tools capabilities,
  long document interpretation, and multimodal capabilities.

+ [fintune_demo](finetune_demo/README.md): Contains
+ PEFT (LORA, P-Tuning) fine-tuning code
+ SFT fine-tuning code

## Friendly Links

+ [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory): Efficient open-source fine-tuning framework,
  already supports GLM-4-9B-Chat language model fine-tuning.

## License

+ The use of GLM-4 model weights must follow
  the [Model License](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE).

+ The code in this open source repository follows the [Apache 2.0](LICENSE) license.

Please strictly follow the open source license.

## Reference

If you find our work helpful, please consider citing the following paper.

```
@inproceedings{zeng2022glm,
  title={{GLM-130B:} An Open Bilingual Pre-trained Model},
  author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others},
  booktitle={The Eleventh International Conference on Learning Representations,
                  {ICLR} 2023, Kigali, Rwanda, May 1-5, 2023},
  year= {2023},
}
```

```
@inproceedings{du2022glm,
  title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
  author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={320--335},
  year={2022}
}
```

```
@misc{wang2023cogvlm,
      title={CogVLM: Visual Expert for Pretrained Language Models}, 
      author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},
      year={2023},
      eprint={2311.03079},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```