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+*This model was published in HF papers on 2024-06-18 and contributed to Hugging Face Transformers on 2024-10-18.*
+
+# GLM-4
+
+<div class="flex flex-wrap space-x-1">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+<img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
+</div>
+
+## Overview
+
+The GLM Model was proposed
+in [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://huggingface.co/papers/2406.12793)
+by GLM Team, THUDM & ZhipuAI.
+
+The abstract from the paper is the following:
+
+*We introduce ChatGLM, an evolving family of large language models that we have been developing over time. This report
+primarily focuses on the GLM-4 language series, which includes GLM-4, GLM-4-Air, and GLM-4-9B. They represent our most
+capable models that are trained with all the insights and lessons gained from the preceding three generations of
+ChatGLM. To date, the GLM-4 models are pre-trained on ten trillions of tokens mostly in Chinese and English, along with
+a small set of corpus from 24 languages, and aligned primarily for Chinese and English usage. The high-quality alignment
+is achieved via a multi-stage post-training process, which involves supervised fine-tuning and learning from human
+feedback. Evaluations show that GLM-4 1) closely rivals or outperforms GPT-4 in terms of general metrics such as MMLU,
+GSM8K, MATH, BBH, GPQA, and HumanEval, 2) gets close to GPT-4-Turbo in instruction following as measured by IFEval, 3)
+matches GPT-4 Turbo (128K) and Claude 3 for long context tasks, and 4) outperforms GPT-4 in Chinese alignments as
+measured by AlignBench. The GLM-4 All Tools model is further aligned to understand user intent and autonomously decide
+when and which tool(s) to use—including web browser, Python interpreter, text-to-image model, and user-defined
+functions—to effectively complete complex tasks. In practical applications, it matches and even surpasses GPT-4 All
+Tools in tasks like accessing online information via web browsing and solving math problems using Python interpreter.
+Over the course, we have open-sourced a series of models, including ChatGLM-6B (three generations), GLM-4-9B (128K, 1M),
+GLM-4V-9B, WebGLM, and CodeGeeX, attracting over 10 million downloads on Hugging face in the year 2023 alone.*
+
+Tips:
+
+- This model was contributed by [THUDM](https://huggingface.co/THUDM). The most recent code can be
+  found [here](https://github.com/thudm/GLM-4).
+
+## Usage tips
+
+`GLM-4` can be found on the [Huggingface Hub](https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7)
+
+In the following, we demonstrate how to use `glm-4-9b-chat` for the inference. Note that we have used the ChatML format for dialog, in this demo we show how to leverage `apply_chat_template` for this purpose.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+model = AutoModelForCausalLM.from_pretrained("THUDM/glm-4-9b-chat", device_map="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat")
+
+prompt = "Give me a short introduction to large language model."
+
+messages = [{"role": "user", "content": prompt}]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True)
+
+generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+## GlmConfig
+
+[[autodoc]] GlmConfig
+
+## GlmModel
+
+[[autodoc]] GlmModel
+    - forward
+
+## GlmForCausalLM
+
+[[autodoc]] GlmForCausalLM
+    - forward
+
+## GlmForSequenceClassification
+
+[[autodoc]] GlmForSequenceClassification
+    - forward
+
+## GlmForTokenClassification
+
+[[autodoc]] GlmForTokenClassification
+    - forward