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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-4v-9b/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-4v-9b |
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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 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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### 多模态能力 |
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GLM-4V-9B 是一个多模态语言模型，具备视觉理解能力，其相关经典任务的评测结果如下： |
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| | **MMBench-EN-Test** | **MMBench-CN-Test** | **SEEDBench_IMG** | **MMStar** | **MMMU** | **MME** | **HallusionBench** | **AI2D** | **OCRBench** | |
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|-------------------------|---------------------|---------------------|-------------------|------------|----------|---------|--------------------|----------|--------------| |
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| | 英文综合 | 中文综合 | 综合能力 | 综合能力 | 学科综合 | 感知推理 | 幻觉性 | 图表理解 | 文字识别 | |
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| **GPT-4o, 20240513** | 83.4 | 82.1 | 77.1 | 63.9 | 69.2 | 2310.3 | 55 | 84.6 | 736 | |
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| **GPT-4v, 20240409** | 81 | 80.2 | 73 | 56 | 61.7 | 2070.2 | 43.9 | 78.6 | 656 | |
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| **GPT-4v, 20231106** | 77 | 74.4 | 72.3 | 49.7 | 53.8 | 1771.5 | 46.5 | 75.9 | 516 | |
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| **InternVL-Chat-V1.5** | 82.3 | 80.7 | 75.2 | 57.1 | 46.8 | 2189.6 | 47.4 | 80.6 | 720 | |
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| **LlaVA-Next-Yi-34B** | 81.1 | 79 | 75.7 | 51.6 | 48.8 | 2050.2 | 34.8 | 78.9 | 574 | |
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| **Step-1V** | 80.7 | 79.9 | 70.3 | 50 | 49.9 | 2206.4 | 48.4 | 79.2 | 625 | |
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| **MiniCPM-Llama3-V2.5** | 77.6 | 73.8 | 72.3 | 51.8 | 45.8 | 2024.6 | 42.4 | 78.4 | 725 | |
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| **Qwen-VL-Max** | 77.6 | 75.7 | 72.7 | 49.5 | 52 | 2281.7 | 41.2 | 75.7 | 684 | |
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| **GeminiProVision** | 73.6 | 74.3 | 70.7 | 38.6 | 49 | 2148.9 | 45.7 | 72.9 | 680 | |
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| **Claude-3V Opus** | 63.3 | 59.2 | 64 | 45.7 | 54.9 | 1586.8 | 37.8 | 70.6 | 694 | |
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| **GLM-4v-9B** | 81.1 | 79.4 | 76.8 | 58.7 | 47.2 | 2163.8 | 46.6 | 81.1 | 786 | |
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**本仓库是 GLM-4V-9B 的模型仓库，支持`8K`上下文长度。** |
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## 运行模型 |
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```python |
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import torch |
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from PIL import Image |
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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-4v-9b", trust_remote_code=True) |
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query = '描述这张图片' |
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image = Image.open("your image").convert('RGB') |
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inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}], |
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add_generation_prompt=True, tokenize=True, return_tensors="pt", |
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return_dict=True) # chat mode |
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inputs = inputs.to(device) |
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model = AutoModelForCausalLM.from_pretrained( |
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"THUDM/glm-4v-9b", |
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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])) |
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``` |
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## 协议 (License) |
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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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