Add GLM-4 and Later GLM Model (Draft) #31977
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| <!--Copyright 2024 The GLM & ZhipuAI team and The HuggingFace Team. All rights reserved. | ||||||
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| Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||||||
| the License. You may obtain a copy of the License at | ||||||
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| http://www.apache.org/licenses/LICENSE-2.0 | ||||||
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| Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||||||
| an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||||||
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| ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be | ||||||
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| # GLM | ||||||
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| ## Overview | ||||||
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| The GLM Model was proposed | ||||||
| in [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://arxiv.org/html/2406.12793v1) | ||||||
| by GLM Team, THUDM & ZhipuAI. | ||||||
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| The abstract from the paper is the following: | ||||||
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| *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.* | ||||||
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| Tips: | ||||||
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| - This model was contributed by [THUDM](https://huggingface.co/THUDM). The most recent code can be | ||||||
| found [here](https://github.com/thudm/GLM-4). | ||||||
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| ## Usage tips | ||||||
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| `GLM-4` can be found on the [Huggingface Hub](https://huggingface.co/collections/THUDM/glm-4-665fcf188c414b03c2f7e3b7) | ||||||
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| 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. | ||||||
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| ```python | ||||||
| >>> from transformers import AutoModelForCausalLM, AutoTokenizer | ||||||
| >>> device = "cuda" # the device to load the model onto | ||||||
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| >>> model = AutoModelForCausalLM.from_pretrained("THUDM/glm-4-9b-chat", device_map="auto") | ||||||
| >>> tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4-9b-chat") | ||||||
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| >>> prompt = "Give me a short introduction to large language model." | ||||||
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| >>> messages = [{"role": "user", "content": prompt}] | ||||||
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| >>> text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | ||||||
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| >>> model_inputs = tokenizer([text], return_tensors="pt").to(device) | ||||||
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| >>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True) | ||||||
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| >>> generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] | ||||||
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| >>> response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] | ||||||
| ``` | ||||||
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| ## GLMConfig | ||||||
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| [[autodoc]] GLMConfig | ||||||
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There was a problem hiding this comment.
Suggested change
let's use camel casing everywhere we can! |
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| ## GLMTokenizer | ||||||
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| [[autodoc]] GLMTokenizer | ||||||
| - save_vocabulary | ||||||
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| ## GLMTokenizerFast | ||||||
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| [[autodoc]] GLMTokenizerFast | ||||||
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| ## GLMModel | ||||||
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| [[autodoc]] GLMModel | ||||||
| - forward | ||||||
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| ## GLMForCausalLM | ||||||
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| [[autodoc]] GLMForCausalLM | ||||||
| - forward | ||||||
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| ## GLMForSequenceClassification | ||||||
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| [[autodoc]] GLMForSequenceClassification | ||||||
| - forward | ||||||
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| ## GLMForTokenClassification | ||||||
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| [[autodoc]] GLMForTokenClassification | ||||||
| - forward | ||||||
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let's add the abstract of the paper here!
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finish