Search Results for "mod-apache-snmp" - Page 31
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Showing 940 open source projects for "mod-apache-snmp"
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ReplitLM
Inference code and configs for the ReplitLM model family
ReplitLM is a family of open-source language models developed by Replit for assisting with programming tasks such as code generation and completion. The project includes model checkpoints, configuration files, and example code that enable developers to run and experiment with the models locally or within machine learning frameworks. These models are designed specifically for coding workflows and are trained on large datasets of source code covering many programming languages and development... -
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CogView
Text-to-Image generation. The repo for NeurIPS 2021 paper
CogView is a large-scale pretrained text-to-image transformer model, introduced in the NeurIPS 2021 paper CogView: Mastering Text-to-Image Generation via Transformers. With 4 billion parameters, it was one of the earliest transformer-based models to successfully generate high-quality images from natural language descriptions in Chinese, with partial support for English via translation. The model incorporates innovations such as PB-relax and Sandwich-LN to enable stable training of very deep... -
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PyTorch Implementation of SDE Solvers
Differentiable SDE solvers with GPU support and efficient sensitivity
This library provides stochastic differential equation (SDE) solvers with GPU support and efficient backpropagation. examples/demo.ipynb gives a short guide on how to solve SDEs, including subtle points such as fixing the randomness in the solver and the choice of noise types. examples/latent_sde.py learns a latent stochastic differential equation, as in Section 5 of [1]. The example fits an SDE to data, whilst regularizing it to be like an Ornstein-Uhlenbeck prior process. The model can be... -
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Doctor Dignity
Doctor Dignity is an LLM that can pass the US Medical Licensing Exam
Doctor Dignity is a prototype project exploring how AI-assisted tooling might support compassionate, accessible health guidance for people who struggle to get timely care. The repository centers on a simple end-to-end pipeline—intake of user-reported symptoms, basic triage logic, and clear, supportive messaging—intended to demonstrate how such systems could be built. It emphasizes a humane UX: plain-language prompts, de-jargonized outputs, and guardrails that nudge users toward professional... -
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Medusa
Framework for Accelerating LLM Generation with Multiple Decoding Heads
Medusa is a framework aimed at accelerating the generation capabilities of Large Language Models (LLMs) by employing multiple decoding heads. This approach allows for parallel processing during text generation, significantly enhancing throughput and reducing response times. Medusa is designed to be simple to implement and integrates with existing LLM infrastructures, making it a practical solution for scaling LLM applications. -
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xTuring
Easily build, customize and control your own LLMs
xTuring is an open-source AI personalization software. xTuring makes it easy to build and control LLMs by providing a simple interface to personalize LLMs to your own data and application. xTuring provides fast, efficient and simple fine-tuning of LLMs, such as LLaMA, GPT-J, Galactica, and more. By providing an easy-to-use interface for fine-tuning LLMs to your own data and application, xTuring makes it simple to build, customize and control LLMs. The entire process can be done inside your... -
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ControlNet
Let us control diffusion models
ControlNet is a neural network architecture designed to add conditional control to text-to-image diffusion models. Rather than training from scratch, ControlNet “locks” the weights of a pre-trained diffusion model and introduces a parallel trainable branch that learns additional conditions—like edges, depth maps, segmentation, human pose, scribbles, or other guidance signals. This allows the system to control where and how the model should focus during generation, enabling users to steer... -
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Detic
Code release for "Detecting Twenty-thousand Classes
Detic (“Detecting Twenty-thousand Classes using Image-level Supervision”) is a large-vocabulary object detector that scales beyond fully annotated datasets by leveraging image-level labels. It decouples localization from classification, training a strong box localizer on standard detection data while learning classifiers from weak supervision and large image-tag corpora. A shared region proposal backbone feeds a flexible classification head that can expand to tens of thousands of categories... -
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UniEM
Unified embedding model
UniEM is a unified embedding model designed to create high-quality text embeddings for various natural language processing tasks. -
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LangChain Apps on Production with Jina
Langchain Apps on Production with Jina & FastAPI
Jina is an open-source framework for building scalable multi-modal AI apps on Production. LangChain is another open-source framework for building applications powered by LLMs. long-chain-serve helps you deploy your LangChain apps on Jina AI Cloud in a matter of seconds. You can benefit from the scalability and serverless architecture of the cloud without sacrificing the ease and convenience of local development. And if you prefer, you can also deploy your LangChain apps on your own... -
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ChatGLM Efficient Tuning
Fine-tuning ChatGLM-6B with PEFT
ChatGLM-Efficient-Tuning is a hands-on toolkit for fine-tuning ChatGLM-family models with parameter-efficient methods on everyday hardware. It wraps techniques like LoRA and prompt-tuning into simple training scripts so you can adapt a large model to your domain without full retraining. The project exposes practical switches for quantization and mixed precision, allowing bigger models to fit into limited VRAM. It includes examples for instruction tuning and dialogue datasets, making it... -
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MetaTransformer
Meta-Transformer for Unified Multimodal Learning
We're thrilled to present OneLLM, an ensembling Meta-Transformer framework with Multimodal Large Language Models, which performs multimodal joint training, supports more modalities including fMRI, Depth, and Normal Maps, and demonstrates very impressive performances on 25 benchmarks. -
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MMClassification
OpenMMLab Image Classification Toolbox and Benchmark
MMClassification is an open-source image classification toolbox based on PyTorch. It is a part of the OpenMMLab project. Supports DenseNet, VAN and PoolFormer, and provide pre-trained models. Supports training on IPU. Supports a series of CSP networks, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet. MMClassification is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or... -
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Promptify
se GPT or other prompt based models to get structured output
Promptify is an open-source Python library designed to simplify prompt engineering and the development of natural language processing pipelines using large language models. The project provides tools that help developers generate structured prompts for different NLP tasks and apply them across multiple generative AI systems. Instead of manually crafting prompts for each task, Promptify introduces a unified architecture that combines prompt templates, language model interfaces, and processing... -
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finetuner
Task-oriented finetuning for better embeddings on neural search
Fine-tuning is an effective way to improve performance on neural search tasks. However, setting up and performing fine-tuning can be very time-consuming and resource-intensive. Jina AI’s Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud. With Finetuner, you can easily enhance the performance of pre-trained models, making them production-ready without extensive labeling or expensive hardware. Create... -
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Gorilla CLI
LLMs for your CLI
Gorilla CLI powers your command-line interactions with a user-centric tool. Simply state your objective, and Gorilla CLI will generate potential commands for execution. Gorilla today supports ~1500 APIs, including Kubernetes, AWS, GCP, Azure, GitHub, Conda, Curl, Sed, and many more. No more recalling intricate CLI arguments. -
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GLM-130B
GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)
GLM-130B is an open bilingual (English and Chinese) dense language model with 130 billion parameters, released by the Tsinghua KEG Lab and collaborators as part of the General Language Model (GLM) series. It is designed for large-scale inference and supports both left-to-right generation and blank filling, making it versatile across NLP tasks. Trained on over 400 billion tokens (200B English, 200B Chinese), it achieves performance surpassing GPT-3 175B, OPT-175B, and BLOOM-176B on multiple... -
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hloc
Visual localization made easy with hloc
This is hloc, a modular toolbox for state-of-the-art 6-DoF visual localization. It implements Hierarchical Localization, leveraging image retrieval and feature matching, and is fast, accurate, and scalable. This codebase won the indoor/outdoor localization challenges at CVPR 2020 and ECCV 2020, in combination with SuperGlue, our graph neural network for feature matching. We provide step-by-step guides to localize with Aachen, InLoc, and to generate reference poses for your own data using... -
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Chinese-LLaMA-Alpaca-2 v2.0
Chinese LLaMA & Alpaca large language model + local CPU/GPU training
This project has open-sourced the Chinese LLaMA model and the Alpaca large model with instruction fine-tuning to further promote the open research of large models in the Chinese NLP community. Based on the original LLaMA , these models expand the Chinese vocabulary and use Chinese data for secondary pre-training, which further improves the basic semantic understanding of Chinese. At the same time, the Chinese Alpaca model further uses Chinese instruction data for fine-tuning, which... -
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FastEdit
Editing large language models within 10 seconds
FastEdit focuses on rapid “model editing,” letting you surgically update facts or behaviors in an LLM without full fine-tuning. It implements practical editing algorithms that insert or revise knowledge with targeted parameter updates, aiming to preserve model quality outside the edited scope. This approach is valuable when you need urgent corrections—think product names, APIs, or fast-changing facts—without retraining on large corpora. The repository provides evaluation harnesses so you can... -
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MMOCR
OpenMMLab Text Detection, Recognition and Understanding Toolbox
MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. It is part of the OpenMMLab project. The toolbox supports not only text detection and text recognition, but also their downstream tasks such as key information extraction. The toolbox supports a wide variety of state-of-the-art models for text detection, text recognition and key information extraction. The modular... -
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Lightning Bolts
Toolbox of models, callbacks, and datasets for AI/ML researchers
Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production. Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. We can introduce sparsity during fine-tuning with SparseML, which ultimately allows us to leverage the DeepSparse engine to see performance improvements at inference time. -
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AI Explainability 360
Interpretability and explainability of data and machine learning model
The AI Explainability 360 toolkit is an open-source library that supports the interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different... -
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Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, etc.
...With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve. ... -
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Lightning Flash
Flash enables you to easily configure and run complex AI recipes
Your PyTorch AI Factory, Flash enables you to easily configure and run complex AI recipes for over 15 tasks across 7 data domains. In a nutshell, Flash is the production-grade research framework you always dreamed of but didn't have time to build. All data loading in Flash is performed via a from_* classmethod on a DataModule. Which DataModule to use and which from_* methods are available depends on the task you want to perform. For example, for image segmentation where your data is stored...