readme update

README.md

@@ -15,11 +15,15 @@ inference: false
 
 # GLM-4-9B-Chat-1M
 
-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。
+Read this in [English](README_en.md)
+
+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** 均表现出超越 Llama-3-8B 的卓越性能。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 **GLM-4-9B-Chat-1M** 模型和基于 GLM-4-9B 的多模态模型
+GLM-4V-9B。**GLM-4V-9B** 具备 1120 * 1120 高分辨率下的中英双语多轮对话能力，在中英文综合能力、感知推理、文字识别、图表理解等多方面多模态评测中，GLM-4V-9B
+表现出超越 GPT-4-turbo-2024-04-09、Gemini
+1.0 Pro、Qwen-VL-Max 和 Claude 3 Opus 的卓越性能。
 
 ## 评测结果
 

README_en.md

@@ -0,0 +1,131 @@
+# GLM-4-9B-Chat-1M
+
+## 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.
+
+### Long Context
+
+The [eval_needle 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](https://raw.githubusercontent.com/THUDM/GLM-4/main/resources/eval_needle.jpeg)
+
+The long text capability was further evaluated on LongBench, and the results are as follows:
+
+![leaderboard](https://raw.githubusercontent.com/THUDM/GLM-4/main/resources/longbench.png)
+
+**This repository is the model repository of GLM-4-9B-Chat-1M, supporting `1M` context length.**
+
+## Quick call
+
+**For hardware configuration and system requirements, please check [here](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-1m",trust_remote_code=True)
+
+query = "hello"
+
+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-1m",
+    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-1M
+
+# If you encounter OOM, it is recommended to reduce max_model_len or increase tp_size
+
+max_model_len, tp_size = 1048576, 4
+
+model_name = "THUDM/glm-4-9b-chat-1m"
+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,
+    # GLM-4-9B-Chat-1M If you encounter OOM phenomenon, it is recommended to turn on 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)
+```
+
+## LICENSE
+
+The weights of the GLM-4 model are available under the terms of [LICENSE](LICENSE).
+
+## Citations
+
+If you find our work useful, please consider citing the following paper.
+
+```
+@article{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},
+  journal={arXiv preprint arXiv:2210.02414},
+  year={2022}
+}
+```
+
+```
+@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}
+}
+```

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "eos_token_id": [
+    151329,
+    151336,
+    151338
+  ],
+  "pad_token_id": 151329,
+  "do_sample": true,
+  "temperature": 0.8,
+  "max_length": 1024000,
+  "top_p": 0.8,
+  "transformers_version": "4.38.2"
+}