Transformers Docs Source En Model Doc Glm46v Md At Main Github

There was an error while loading. Please reload this page. and get access to the augmented documentation experience ( text_config = None vision_config = None image_token_id = 151343 video_token_id = 151344 image_start_token_id = 151339 image_end_token_id = 151340 video_start_token_id = 151361 video_end_token_id = 151362 **kwargs ) This is the configuration class to store the configuration of a Glm4vModel. It is used to instantiate a GLM-4.6V model according to the specified arguments, defining the model architecture.

Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-4.1V-9B-Thinking zai-org/GLM-4.1V-9B-Thinking. Configuration objects inherit from PreTrainedConfig and can be used to control the model outputs. Read the documentation from PreTrainedConfig for more information. ( do_resize: bool = True size: typing.Optional[dict[str, int]] = None resample: Resampling = <Resampling.BICUBIC: 3> do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, list[float], NoneType] = None... This document provides a high-level introduction to the Transformers library, its role as a model-definition framework, core architecture, and major subsystems. Transformers is designed to centralize the definition of state-of-the-art machine learning models across text, vision, audio, video, and multimodal domains, making these definitions compatible across training frameworks, inference engines, and adjacent modeling libraries.

For detailed information about specific subsystems: Sources: README.md59-77 docs/source/en/index.md18-36 Transformers acts as the model-definition framework for state-of-the-art machine learning models. It centralizes model definitions so they are agreed upon across the entire ecosystem. When a model definition is supported in transformers, it becomes compatible with: The library provides over 1 million pretrained model checkpoints on the Hugging Face Hub, supporting both inference and training workflows.

pip install transformers Copy PIP instructions State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Português | తెలుగు | Français | Deutsch | Italiano | Tiếng Việt | العربية | اردو | বাংলা... State-of-the-art pretrained models for inference and training Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer vision, audio, video, and multimodal model, for both inference and training. and get access to the augmented documentation experience

Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer vision, audio, video, and multimodal model, for both inference and training. It centralizes the model definition so that this definition is agreed upon across the ecosystem. transformers is the pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, …), inference engines (vLLM, SGLang, TGI, …),... We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be simple, customizable, and efficient. There are over 1M+ Transformers model checkpoints on the Hugging Face Hub you can use. There was an error while loading.

Please reload this page. and get access to the augmented documentation experience State-of-the-art Machine Learning for PyTorch, TensorFlow and JAX. 🤗 Transformers provides APIs to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you time from training a model from scratch. The models can be used across different modalities such as:

Our library supports seamless integration between three of the most popular deep learning libraries: PyTorch, TensorFlow and JAX. Train your model in three lines of code in one framework, and load it for inference with another. Each 🤗 Transformers architecture is defined in a standalone Python module so they can be easily customized for research and experiments. We provide various pre-trained Sentence Transformers models via our Sentence Transformers Hugging Face organization. Additionally, over 6,000 community Sentence Transformers models have been publicly released on the Hugging Face Hub. All models can be found here:

Original models: Sentence Transformers Hugging Face organization. Community models: All Sentence Transformer models on Hugging Face. Each of these models can be easily downloaded and used like so: For the original models from the Sentence Transformers Hugging Face organization, it is not necessary to include the model author or organization prefix. For example, this snippet loads sentence-transformers/all-mpnet-base-v2. and get access to the augmented documentation experience

This model was released on 2024-06-18 and added to Hugging Face Transformers on 2024-10-18. The GLM Model was proposed in ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools 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... 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... 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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