Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Runtimes and servers that execute model inference at speed and scale.
Signals: llm-inference, inference, inference-engine, model-serving, llm-serving, llama-cpp, vllm, triton-inference-server
410 results
Ollama alternative for Rockchip NPU: An efficient solution for running AI and Deep learning models on Rockchip devices with optimized NPU support ( rkllm )
Rust multiprovider generative AI client (Ollama, OpenAi, Anthropic, Gemini, DeepSeek, xAI/Grok, Groq,Cohere, ...)
Local models support for Microsoft's graphrag using ollama (llama3, mistral, gemma2 phi3)- LLM & Embedding extraction
Welcome to the Ollama Docker Compose Setup! This project simplifies the deployment of Ollama using Docker Compose, making it easy to run Ollama with all its dependencies in a containerized environment
Python API client for AI providers that intends to replace LangChain and LangGraph for most common use cases.
Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraph. This is the companion repository for the book on generative AI with LangChain.
RNA vaccines have become a key tool in moving forward through the challenges raised both in the current pandemic and in numerous other public health and medical challenges. With the rollout of vaccines for COVID-19, these synthetic mRNAs have become broadly distributed RNA species in numerous human populations. Despite their ubiquity, sequences are not always available for such RNAs. Standard methods facilitate such sequencing. In this note, we provide experimental sequence information for the RNA components of the initial Moderna (https://pubmed.ncbi.nlm.nih.gov/32756549/) and Pfizer/BioNTech (https://pubmed.ncbi.nlm.nih.gov/33301246/) COVID-19 vaccines, allowing a working assembly of the former and a confirmation of previously reported sequence information for the latter RNA. Sharing of sequence information for broadly used therapeutics has the benefit of allowing any researchers or clinicians using sequencing approaches to rapidly identify such sequences as therapeutic-derived rather than host or infectious in origin. For this work, RNAs were obtained as discards from the small portions of vaccine doses that remained in vials after immunization; such portions would have been required to be otherwise discarded and were analyzed under FDA authorization for research use. To obtain the small amounts of RNA needed for characterization, vaccine remnants were phenol-chloroform extracted using TRIzol Reagent (Invitrogen), with intactness assessed by Agilent 2100 Bioanalyzer before and after extraction. Although our analysis mainly focused on RNAs obtained as soon as possible following discard, we also analyzed samples which had been refrigerated (~4 ℃) for up to 42 days with and without the addition of EDTA. Interestingly a substantial fraction of the RNA remained intact in these preparations. We note that the formulation of the vaccines includes numerous key chemical components which are quite possibly unstable under these conditions-- so these data certainly do not suggest that the vaccine as a biological agent is stable. But it is of interest that chemical stability of RNA itself is not sufficient to preclude eventual development of vaccines with a much less involved cold-chain storage and transportation. For further analysis, the initial RNAs were fragmented by heating to 94℃, primed with a random hexamer-tailed adaptor, amplified through a template-switch protocol (Takara SMARTerer Stranded RNA-seq kit), and sequenced using a MiSeq instrument (Illumina) with paired end 78-per end sequencing. As a reference material in specific assays, we included RNA of known concentration and sequence (from bacteriophage MS2). From these data, we obtained partial information on strandedness and a set of segments that could be used for assembly. This was particularly useful for the Moderna vaccine, for which the original vaccine RNA sequence was not available at the time our study was carried out. Contigs encoding full-length spikes were assembled from the Moderna and Pfizer datasets. The Pfizer/BioNTech data [Figure 1] verified the reported sequence for that vaccine (https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/), while the Moderna sequence [Figure 2] could not be checked against a published reference. RNA preparations lacking dsRNA are desirable in generating vaccine formulations as these will minimize an otherwise dramatic biological (and nonspecific) response that vertebrates have to double stranded character in RNA (https://www.nature.com/articles/nrd.2017.243). In the sequence data that we analyzed, we found that the vast majority of reads were from the expected sense strand. In addition, the minority of antisense reads appeared different from sense reads in lacking the characteristic extensions expected from the template switching protocol. Examining only the reads with an evident template switch (as an indicator for strand-of-origin), we observed that both vaccines overwhelmingly yielded sense reads (>99.99%). Independent sequencing assays and other experimental measurements are ongoing and will be needed to determine whether this template-switched sense read fraction in the SmarterSeq protocol indeed represents the actual dsRNA content in the original material. This work provides an initial assessment of two RNAs that are now a part of the human ecosystem and that are likely to appear in numerous other high throughput RNA-seq studies in which a fraction of the individuals may have previously been vaccinated. ProtoAcknowledgements: Thanks to our colleagues for help and suggestions (Nimit Jain, Emily Greenwald, Lamia Wahba, William Wang, Amisha Kumar, Sameer Sundrani, David Lipman, Bijoyita Roy). Figure 1: Spike-encoding contig assembled from BioNTech/Pfizer BNT-162b2 vaccine. Although the full coding region is included, the nature of the methodology used for sequencing and assembly is such that the assembled contig could lack some sequence from the ends of the RNA. Within the assembled sequence, this hypothetical sequence shows a perfect match to the corresponding sequence from documents available online derived from manufacturer communications with the World Health Organization [as reported by https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/]. The 5’ end for the assembly matches the start site noted in these documents, while the read-based assembly lacks an interrupted polyA tail (A30(GCATATGACT)A70) that is expected to be present in the mRNA.
Run any Llama 2 locally with gradio UI on GPU or CPU from anywhere (Linux/Windows/Mac). Use `llama2-wrapper` as your local llama2 backend for Generative Agents/Apps.
📚 Introduction to Modern Statistics - A college-level open-source textbook with a modern approach highlighting multivariable relationships and simulation-based inference.
Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀
Acceleration package for neural networks on multi-core CPUs
Distributed LLM inference. Connect home devices into a powerful cluster to accelerate LLM inference. More devices means faster inference.
ONNXMLTools enables conversion of models to ONNX
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.
A tool to modify ONNX models in a visualization fashion, based on Netron and Flask.
ONNX-TensorRT: TensorRT backend for ONNX
Deep Learning Visualization Toolkit（『飞桨』深度学习可视化工具 ）
Remove backgrounds from images directly in the browser environment with ease and no additional costs or privacy concerns. Explore an interactive demo.
A collection of pre-trained, state-of-the-art models in the ONNX format
Fast and accurate AI powered file content types detection
Lightweight nudity detection
支持按轴凹总力战, 无缝制造三解, 用于实现蔚蓝档案自动化的程序( Steam已适配 )
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Robust realtime face and facial landmark tracking on CPU with Unity integration
24,523 repositories in the index in total.
