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--- |
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license: other |
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license_name: glm-4 |
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license_link: https://huggingface.co/THUDM/glm-4-9b-chat/blob/main/LICENSE |
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language: |
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- zh |
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- en |
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tags: |
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- glm |
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- chatglm |
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- thudm |
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inference: false |
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--- |
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# GLM-4-9B-Chat |
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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。 |
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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。 |
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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 |
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上下文）等高级功能。 |
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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。 |
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## 评测结果 |
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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: |
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| Model | AlignBench-v2 | MT-Bench | IFEval | MMLU | C-Eval | GSM8K | MATH | HumanEval | NCB | |
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|:--------------------|:-------------:|:--------:|:------:|:----:|:------:|:-----:|:----:|:---------:|:----:| |
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| Llama-3-8B-Instruct | 5.12 | 8.00 | 68.58 | 68.4 | 51.3 | 79.6 | 30.0 | 62.2 | 24.7 | |
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| ChatGLM3-6B | 3.97 | 5.50 | 28.1 | 66.4 | 69.0 | 72.3 | 25.7 | 58.5 | 11.3 | |
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| GLM-4-9B-Chat | 6.61 | 8.35 | 69.0 | 72.4 | 75.6 | 79.6 | 50.6 | 71.8 | 32.2 | |
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### 长文本 |
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在 1M 的上下文长度下进行[大海捞针实验](https://github.com/LargeWorldModel/LWM/blob/main/scripts/eval_needle.py)，结果如下： |
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在 LongBench-Chat 上对长文本能力进行了进一步评测，结果如下: |
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### 多语言能力 |
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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 |
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| Dataset | Llama-3-8B-Instruct | GLM-4-9B-Chat | Languages |
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|:------------|:-------------------:|:-------------:|:----------------------------------------------------------------------------------------------:| |
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| M-MMLU | 49.6 | 56.6 | all |
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| 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 |
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| MGSM | 54.0 | 65.3 | zh, en, bn, de, es, fr, ja, ru, sw, te, th |
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| XWinograd | 61.7 | 73.1 | zh, en, fr, jp, ru, pt |
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| XStoryCloze | 84.7 | 90.7 | zh, en, ar, es, eu, hi, id, my, ru, sw, te |
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| XCOPA | 73.3 | 80.1 | zh, et, ht, id, it, qu, sw, ta, th, tr, vi |
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### 工具调用能力 |
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我们在 [Berkeley Function Calling Leaderboard](https://github.com/ShishirPatil/gorilla/tree/main/berkeley-function-call-leaderboard)上进行了测试并得到了以下结果： |
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| Model | Overall Acc. | AST Summary | Exec Summary | Relevance | |
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|:-----------------------|:------------:|:-----------:|:------------:|:---------:| |
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| Llama-3-8B-Instruct | 58.88 | 59.25 | 70.01 | 45.83 | |
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| gpt-4-turbo-2024-04-09 | 81.24 | 82.14 | 78.61 | 88.75 | |
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| ChatGLM3-6B | 57.88 | 62.18 | 69.78 | 5.42 | |
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| GLM-4-9B-Chat | 81.00 | 80.26 | 84.40 | 87.92 | |
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**本仓库是 GLM-4-9B-Chat 的模型仓库，支持`128K`上下文长度。** |
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## 运行模型 |
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使用 transformers 后端进行推理: |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" |
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tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat",trust_remote_code=True) |
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query = "你好" |
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inputs = tokenizer.apply_chat_template([{"role": "user", "content": query}], |
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add_generation_prompt=True, |
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tokenize=True, |
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return_tensors="pt", |
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return_dict=True |
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) |
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inputs = inputs.to(device) |
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model = AutoModelForCausalLM.from_pretrained( |
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"THUDM/glm-4-9b-chat", |
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torch_dtype=torch.bfloat16, |
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low_cpu_mem_usage=True, |
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trust_remote_code=True |
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).to(device).eval() |
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gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1} |
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with torch.no_grad(): |
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outputs = model.generate(**inputs, **gen_kwargs) |
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outputs = outputs[:, inputs['input_ids'].shape[1]:] |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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使用 VLLM后端进行推理: |
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```python |
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from transformers import AutoTokenizer |
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from vllm import LLM, SamplingParams |
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# GLM-4-9B-Chat-1M |
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# max_model_len, tp_size = 1048576, 4 |
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# GLM-4-9B-Chat |
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from transformers import AutoTokenizer |
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from vllm import LLM, SamplingParams |
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# 如果遇见 OOM 现象，建议减少max_model_len，或者增加tp_size |
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max_model_len, tp_size = 131072, 1 |
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model_name = "THUDM/glm-4-9b-chat" |
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prompt = [{"role": "user", "content": "你好"}] |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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llm = LLM( |
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model=model_name, |
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tensor_parallel_size=tp_size, |
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max_model_len=max_model_len, |
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trust_remote_code=True, |
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enforce_eager=True, |
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# GLM-4-9B-Chat-1M 如果遇见 OOM 现象，建议开启下述参数 |
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# enable_chunked_prefill=True, |
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# max_num_batched_tokens=8192 |
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) |
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stop_token_ids = [151329, 151336, 151338] |
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sampling_params = SamplingParams(temperature=0.95, max_tokens=1024, stop_token_ids=stop_token_ids) |
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inputs = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True) |
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outputs = llm.generate(prompts=inputs, sampling_params=sampling_params) |
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print(outputs[0].outputs[0].text) |
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``` |
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## 协议 |
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GLM-4 模型的权重的使用则需要遵循 [LICENSE](LICENSE)。 |
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Rhe use of the GLM-4 model weights needs to comply with the [LICENSE](LICENSE). |
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## 引用 |
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如果你觉得我们的工作有帮助的话，请考虑引用下列论文。 |
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``` |
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@article{zeng2022glm, |
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title={Glm-130b: An open bilingual pre-trained model}, |
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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}, |
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journal={arXiv preprint arXiv:2210.02414}, |
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year={2022} |
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} |
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``` |
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``` |
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@inproceedings{du2022glm, |
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title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling}, |
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author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie}, |
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booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, |
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pages={320--335}, |
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year={2022} |
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} |
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``` |
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