Github Transformers Features Alternatives Toolerific

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. Transformers is a state-of-the-art pretrained models library that acts as the model-definition framework for machine learning models in text, computer vision, audio, video, and multimodal tasks. It centralizes model definition for compatibility across various training frameworks, inference engines, and modeling libraries. The library simplifies the usage of new models by providing simple, customizable, and efficient model definitions. With over 1M+ Transformers model checkpoints available, users can easily find and utilize models for their tasks. English | 简体中文 | 繁體中文 | 한국어 | Español | 日本語 | हिन्दी | Русский | Português | తెలుగు | Français | Deutsch | 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. A Flexible Framework for Experiencing Cutting-edge LLM Inference Optimizations KTransformers is a flexible Python-centric framework designed to enhance the user's experience with advanced kernel optimizations and placement/parallelism strategies for Transformers. It provides a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and a simplified ChatGPT-like web UI. The framework aims to serve as a platform for experimenting with innovative LLM inference optimizations, focusing on local deployments constrained by limited resources and supporting heterogeneous computing opportunities like GPU/CPU offloading of quantized models.

https://github.com/user-attachments/assets/fafe8aec-4e22-49a8-8553-59fb5c6b00a2 https://github.com/user-attachments/assets/faa3bda2-928b-45a7-b44f-21e12ec84b8a https://github.com/user-attachments/assets/ebd70bfa-b2c1-4abb-ae3b-296ed38aa285 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 with text, computer vision, audio, video, and multimodal models, 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. In the rapidly evolving world of AI and natural language processing, transformers have become the backbone of many intelligent business tools and scalable tech solutions. However, not every developer or small business owner can rely solely on the popular transformer libraries like Hugging Face’s Transformers due to resource constraints, licensing, or specific project requirements. If you’re searching for the best transformers alternatives for library use, you’re not alone.

This post explores practical, efficient, and scalable options that can fit your technical needs without compromising performance or flexibility. Whether you’re building chatbots, recommendation engines, or advanced text analytics, understanding the landscape of transformer alternatives will empower you to make smarter technology choices. Transformers revolutionized AI by enabling models to capture context and dependencies in data more effectively than traditional architectures. Yet, they come with challenges: For developers and SMBs aiming to deploy intelligent business tools with limited resources, exploring the best transformers alternatives for library use is essential. These alternatives offer a balance of performance, scalability, and ease of integration.

If your priority is deploying scalable tech tools on limited hardware or edge devices, lightweight transformer alternatives can be a game-changer. These options reduce model size and computational overhead without sacrificing much accuracy. In this article, I will explore various alternatives to transformers, considering their architectural improvements, computational efficiency, and performance results across different benchmarks. I intend to continually update this post with new models in the future. If you believe there are any models or important points that should be included or any corrections that need to be made, please feel free to contact me. Traditional sequential models, like recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), faced challenges in effectively capturing long-range dependencies and parallelizing computations.

The Transformer architecture addresses these issues by relying on self-attention mechanisms. At the core of the Transformer is the self-attention mechanism. Unlike traditional approaches, where each element in a sequence is processed one at a time, self-attention allows the model to weigh the importance of different elements relative to each other. This enables capturing relationships between distant words in a sentence. Transformer has some limitations and constraints in terms of computation and storage. The Transformer is based on dot-product attention that computes softmax(Q*K.t), which is computationally heavy, and it needs to store a KV cache that is also heavy in memory at inference.

This is a limiting factor, especially in problems with extended context sizes. Transformers’ space complexity increases quadratically with the increasing context size. The Transformer is a key component of the current LLM revolution, and researchers are actively seeking alternatives to address its limitations. While there have been several proposed alternatives, the original model has yet to be as successful as the original model. Nevertheless, considering the scale of the state-of-the-art LLM problem and the high cost of training these models, even a slight improvement can have a significant impact. This library empowers users to seamlessly port pretrained models and checkpoints on the HuggingFace (HF) hub (developed using HF transformers library) into inference-ready formats that run efficiently on Qualcomm Cloud AI 100 accelerators.

Efficient Transformers Library provides reimplemented blocks of Large Language Models (LLMs) to make models functional and highly performant on Qualcomm Cloud AI 100. It includes graph transformations, handling for under-flows and overflows, patcher modules, exporter module, sample applications, and unit test templates. The library supports seamless inference on pre-trained LLMs with documentation for model optimization and deployment. Contributions and suggestions are welcome, with a focus on testing changes for model support and common utilities. [04/2025] Support for SpD, multiprojection heads. Implemented post-attention hidden size projections to speculate tokens ahead of the base model

[04/2025] QNN Compilation support for AutoModel classes. QNN compilation capabilities for multi-models, embedding models and causal models. [04/2025] Added support for separate prefill and decode compilation for encoder (vision) and language models. This feature will be utilized for disaggregated serving. 21 Lessons, Get Started Building with Generative AI InfluxDB – Built for High-Performance Time Series Workloads.

InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now. 🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation,... 🧠

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024) Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch A collection of tricks and tools to speed up transformer models A collection of tricks to simplify and speed up transformer models by removing parts from neural networks. Includes Flash normalization, slim attention, matrix-shrink, precomputing the first layer, and removing weights from skipless transformers. Follows recent trends in neural network optimization.

A collection of tricks to simplify and speed up transformer models: Many of these tricks follow a recent trend of removing parts from neural networks such as RMSNorm’s removal of mean centering from LayerNorm, PaLM's removal of bias-parameters, decoder-only transformer's removal of the encoder stack,... For example, our FlashNorm removes the weights from RMSNorm and merges them with the next linear layer. And slim attention removes the entire V-cache from the context memory for MHA transformers. Transformers continue to be one of the most frequently used models for various NLP tasks since 2017. However, due to their high computational resource requirements and difficult maintenance, they may not be the most efficient choice out there all the time.

This is especially true for simple sentiment classification tasks. In such circumstances, among the alternatives, there is the feature-based approach, where we use transformers as feature extractors for a simple model. What crucial in this approach is that since a transformer’s body weights are frozen, the hidden states need to be precomputed only once for them to be used as features for the model, meaning... In this post, we will do a simple binary sentiment classification task using Rotten Tomatoes movie review dataset. We will obtain it through the Hugging Face Dataset library and make use of DistilRoBERTa to provide our simple Logistic Regression model with the features it needs to be trained with. DistilRoBERTa is a distilled version of the RoBERTa base model, which is an improved version of BERT due to longer training with more training data while using only Masked Language Modeling (MLM) objective.

On average, DistilRoBERTa is twice as fast as RoBERTa because of having much less parameters of 82M (6 layers, 768 dimension and 12 heads). Our first step is to install the Hugging Face’s Dataset and Transformers libraries. Then, we need to load the other needed dependencies.

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