#python #computer_vision #convolutional_networks #embedding_vectors #embeddings #feature_extraction #feature_vector #image_processing #image_retrieval #machine_learning #milvus #pipeline #towhee #transformer #unstructured_data #video_processing #vision_transformer #vit

https://github.com/towhee-io/towhee

#c_lang #openai #speech_to_text #transformer #whisper

https://github.com/ggerganov/whisper.cpp

#jupyter_notebook #ade20k #cityscapes #coco #image_segmentation #instance_segmentation #oneformer #panoptic_segmentation #semantic_segmentation #transformer #universal_segmentation

https://github.com/SHI-Labs/OneFormer

#python #computer_vision #machine_learning #multimodal #natural_language_processing #pretrained_language_model #speech_processing #transformer #translation

https://github.com/microsoft/torchscale

#python #bert #deep_learning #language_model #language_models #machine_learning #natural_language_processing #nlp #pytorch #transformer

https://github.com/extreme-bert/extreme-bert

#cplusplus #bert #gpt #pytorch #transformer

https://github.com/NVIDIA/FasterTransformer

#python #attention_mechanism #deep_learning #gpt #gpt_2 #gpt_3 #language_model #linear_attention #lstm #pytorch #rnn #rwkv #transformer #transformers

https://github.com/BlinkDL/RWKV-LM

#python #deep_learning #flax #jax #language_model #large_language_models #natural_language_processing #transformer

https://github.com/young-geng/EasyLM

#python #bloom #deep_learning #gpt #inference #nlp #pytorch #transformer

https://github.com/huggingface/text-generation-inference

#python #attention #dataset #pytorch #transformer

https://github.com/hyunwoongko/transformer

#python #deeplabv3 #image_segmentation #medical_image_segmentation #pspnet #pytorch #realtime_segmentation #retinal_vessel_segmentation #semantic_segmentation #swin_transformer #transformer #vessel_segmentation

MMSegmentation is an open-source semantic segmentation toolbox based on PyTorch, part of the OpenMMLab project. It offers a unified benchmark for various semantic segmentation methods, a modular design allowing easy customization, and support for multiple popular segmentation frameworks like PSPNet and DeepLabV3+. The toolbox is highly efficient and provides detailed tutorials, advanced guides, and support for numerous datasets and backbones. This makes it a powerful tool for researchers and developers to implement and develop new semantic segmentation methods efficiently. By using MMSegmentation, you can leverage its flexibility, extensive features, and community support to enhance your projects in image segmentation tasks.

https://github.com/open-mmlab/mmsegmentation

#python #bert #deep_learning #flax #hacktoberfest #jax #language_model #language_models #machine_learning #model_hub #natural_language_processing #nlp #nlp_library #pretrained_models #python #pytorch #pytorch_transformers #seq2seq #speech_recognition #tensorflow #transformer

The Hugging Face Transformers library provides thousands of pretrained models for various tasks like text, image, and audio processing. These models can be used for tasks such as text classification, image detection, speech recognition, and more. The library supports popular deep learning frameworks like JAX, PyTorch, and TensorFlow, making it easy to switch between them.

The benefit to the user is that you can quickly download and use these pretrained models with just a few lines of code, saving time and computational resources. You can also fine-tune these models on your own datasets and share them with the community. Additionally, the library offers a simple `pipeline` API for immediate use on different inputs, making it user-friendly for both researchers and practitioners. This helps in reducing compute costs and carbon footprint while enabling high-performance results across various machine learning tasks.

https://github.com/huggingface/transformers

#python #amd #cuda #gpt #inference #inferentia #llama #llm #llm_serving #llmops #mlops #model_serving #pytorch #rocm #tpu #trainium #transformer #xpu

vLLM is a library that makes it easy, fast, and cheap to use large language models (LLMs). It is designed to be fast with features like efficient memory management, continuous batching, and optimized CUDA kernels. vLLM supports many popular models and can run on various hardware including NVIDIA GPUs, AMD CPUs and GPUs, and more. It also offers seamless integration with Hugging Face models and supports different decoding algorithms. This makes it flexible and easy to use for anyone needing to serve LLMs, whether for research or other applications. You can install vLLM easily with `pip install vllm` and find detailed documentation on their website.

https://github.com/vllm-project/vllm

#python #llama #transformer #tts #valle #vits #vqgan #vqvae

Fish Speech is a powerful tool that converts text into speech in many languages, including English, Japanese, Korean, Chinese, and more. You can use it by inputting a short vocal sample to generate high-quality speech. It supports multiple languages without needing phonemes and is highly accurate with low error rates. The tool is fast, with real-time processing on various devices, and has a user-friendly web and GUI interface. You can try the demo online or set it up locally. It's released under a CC BY-NC-SA 4.0 license, which means you can use and modify it freely, but you must give credit and share any changes under the same license. This tool helps you create realistic speech quickly and easily, making it useful for various applications like voice cloning and multilingual communication.

https://github.com/fishaudio/fish-speech

#python #agents #ai #artificial_intelligence #attention_mechanism #chatgpt #gpt4 #gpt4all #huggingface #langchain #langchain_python #machine_learning #multi_modal_imaging #multi_modality #multimodal #prompt_engineering #prompt_toolkit #prompting #swarms #transformer_models #tree_of_thoughts

Swarms is an advanced multi-agent orchestration framework designed for enterprise-grade production use. Here are the key benefits and features Swarms offers production-ready infrastructure with high reliability, modular design, and comprehensive logging, reducing downtime and easing maintenance.
- **Agent Orchestration** Swarms allows multi-model support, custom agent creation, an extensive tool library, and multiple memory systems, providing flexibility and extended functionality.
- **Scalability** Swarms includes a simple API, extensive documentation, an active community, and CLI tools, making development faster and easier.
- **Security Features**//docs.swarms.world) for more detailed information.

https://github.com/kyegomez/swarms

#python #cuda #deepseek #deepseek_llm #deepseek_v3 #inference #llama #llama2 #llama3 #llama3_1 #llava #llm #llm_serving #moe #pytorch #transformer #vlm

SGLang is a tool that makes working with large language models and vision language models much faster and more manageable. It has a fast backend runtime that optimizes model performance with features like prefix caching, continuous batching, and quantization. The frontend language is flexible and easy to use, allowing for complex tasks like chained generation calls and multi-modal inputs. SGLang supports many different models and has an active community behind it. This means you can get your models running quickly and efficiently, saving time and resources. Additionally, the extensive documentation and community support make it easier to get started and resolve any issues.

https://github.com/sgl-project/sglang

#python #asr #automatic_speech_recognition #conformer #e2e_models #production_ready #pytorch #speech_recognition #transformer #whisper

WeNet is a powerful tool for speech recognition that helps turn spoken words into text. It's designed to be easy to use and works well in real-world situations, making it great for businesses and developers. WeNet provides accurate results on many public datasets and is lightweight, meaning it doesn't require a lot of resources to run. This makes it beneficial for users who need reliable speech-to-text functionality without complex setup or maintenance.

https://github.com/wenet-e2e/wenet