Search Results for "apache" - Page 2
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Showing 83 open source projects for "apache"
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Automate contact and company data extractionGenerate leads at scale without building or maintaining scrapers. Use 10,000+ ready-made tools that handle authentication, pagination, and anti-bot protection. Pull data from business directories, social profiles, and public sources, then export to your CRM or database via API. Schedule recurring extractions, enrich existing datasets, and integrate with your workflows.
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H2O LLM Studio
Framework and no-code GUI for fine-tuning LLMs
Welcome to H2O LLM Studio, a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). You can also use H2O LLM Studio with the command line interface (CLI) and specify the configuration file that contains all the experiment parameters. To finetune using H2O LLM Studio with CLI, activate the pipenv environment by running make shell. With H2O LLM Studio, training your large language model is easy and intuitive. First, upload your dataset and then start... -
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Chinese-LLaMA-Alpaca-3
Chinese Llama-3 LLMs) developed from Meta Llama 3
Chinese-LLaMA-Alpaca-3 is an open-source project that provides Mandarin-focused large language models based on Meta’s LLaMA-3 architecture, with both foundational and instruction-tuned variants to support high-quality Chinese natural language understanding and generation. It extends the original LLaMA models with expanded Chinese vocabularies and additional pretraining on Chinese corpora to improve semantic encoding and decoding specifically for Chinese text. Alongside the base models, the... -
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LangChain-Chatchat
Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge
LangChain-Chatchat (formerly Langchain-ChatGLM): A local knowledge base question answering application implementation based on large language models such as Langchain and ChatGLM. The knowledge base information of the current project is stored in the database, please initialize the database before running the project officially (we strongly recommend that you back up your knowledge files before performing operations). Relying on the open-source LLM and Embedding models supported by this... -
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Swirl
Swirl queries any number of data sources with APIs
...It's intended for use by developers and data scientists who want to solve multi-silo search problems from enterprise search to new monitoring & alerting solutions that push information to users continuously. Built on the Python/Django/RabbitMQ stack, SWIRL includes connectors to Apache Solr, ChatGPT, Elastic, OpenSearch | PostgreSQL, Google BigQuery plus generic HTTP/GET/JSON with configurations for premium services. -
Run applications fast and securely in a fully managed environmentRun frontend and backend services, batch jobs, deploy websites and applications, and queue processing workloads without the need to manage infrastructure.
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PEFT
State-of-the-art Parameter-Efficient Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs. Recent State-of-the-Art PEFT techniques achieve performance comparable to that of full... -
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SentenceTransformers
Multilingual sentence & image embeddings with BERT
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for semantic textual similar, semantic search, or paraphrase... -
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MiniMind
Train a 26M-parameter GPT from scratch in just 2h
minimind is a framework that enables users to train a 26-million-parameter GPT (Generative Pre-trained Transformer) model from scratch in approximately two hours. It provides a streamlined process for data preparation, model training, and evaluation, making it accessible for individuals and organizations to develop their own language models without extensive computational resources. -
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MegaParse
File Parser optimised for LLM Ingestion with no loss
MegaParse is a file parser optimized for Large Language Model (LLM) ingestion, ensuring no loss of information. It efficiently parses various document formats, such as PDFs, DOCX, and PPTX, converting them into formats ideal for processing by LLMs. This tool is essential for applications that require accurate and comprehensive data extraction from diverse document types. -
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NVIDIA NeMo
Toolkit for conversational AI
NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI... -
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LLM Action
Technical principles related to large models
LLM-Action is a knowledge/tutorial/repository that shares principles, techniques, and real-world experience related to large language models (LLMs), focusing on LLM engineering, deployment, optimization, inference, compression, and tooling. It organizes content in domains like training, inference, compression, alignment, evaluation, pipelines, and applications. Sections covering infrastructure, engineering, and deployment. Repository templates, sample code, and resource links. Articles/code... -
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GLM-V
GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning
GLM-V is an open-source vision-language model (VLM) series from ZhipuAI that extends the GLM foundation models into multimodal reasoning and perception. The repository provides both GLM-4.5V and GLM-4.1V models, designed to advance beyond basic perception toward higher-level reasoning, long-context understanding, and agent-based applications. GLM-4.5V builds on the flagship GLM-4.5-Air foundation (106B parameters, 12B active), achieving state-of-the-art results on 42 benchmarks across image,... -
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Qwen2.5-Omni
Capable of understanding text, audio, vision, video
Qwen2.5-Omni is an end-to-end multimodal flagship model in the Qwen series by Alibaba Cloud, designed to process multiple modalities (text, images, audio, video) and generate responses both as text and natural speech in streaming real-time. It supports “Thinker-Talker” architecture, and introduces innovations for aligning modalities over time (for example synchronizing video/audio), robust speech generation, and low-VRAM/quantized versions to make usage more accessible. It holds... -
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Qwen-VL
Chat & pretrained large vision language model
Qwen-VL is Alibaba Cloud’s vision-language large model family, designed to integrate visual and linguistic modalities. It accepts image inputs (with optional bounding boxes) and text, and produces text (and sometimes bounding boxes) as output. The model variants (VL-Plus, VL-Max, etc.) have been upgraded for better visual reasoning, text recognition from images, fine-grained understanding, and support for high image resolutions / extreme aspect ratios. Qwen-VL supports multilingual inputs... -
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Gemma
Gemma open-weight LLM library, from Google DeepMind
Gemma, developed by Google DeepMind, is a family of open-weights large language models (LLMs) built upon the research and technology behind Gemini. This repository provides the official implementation of the Gemma PyPI package, a JAX-based library that enables users to load, interact with, and fine-tune Gemma models. The framework supports both text and multi-modal input, allowing natural language conversations that incorporate visual content such as images. It includes APIs for... -
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Xorbits Inference
Replace OpenAI GPT with another LLM in your app
Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop. Xorbits Inference(Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. With Xorbits... -
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Guardrails
Adding guardrails to large language models
Guardrails is a Python package that lets a user add structure, type and quality guarantees to the outputs of large language models (LLMs). At the heart of Guardrails is the rail spec. rail is intended to be a language-agnostic, human-readable format for specifying structure and type information, validators and corrective actions over LLM outputs. We create a RAIL spec to describe the expected structure and types of the LLM output, the quality criteria for the output to be considered valid,... -
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GLM-4
GLM-4 series: Open Multilingual Multimodal Chat LMs
GLM-4 is a family of open models from ZhipuAI that spans base, chat, and reasoning variants at both 32B and 9B scales, with long-context support and practical local-deployment options. The GLM-4-32B-0414 models are trained on ~15T high-quality data (including substantial synthetic reasoning data), then post-trained with preference alignment, rejection sampling, and reinforcement learning to improve instruction following, coding, function calling, and agent-style behaviors. The... -
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Qwen3-Omni
Qwen3-omni is a natively end-to-end, omni-modal LLM
Qwen3-Omni is a natively end-to-end multilingual omni-modal foundation model that processes text, images, audio, and video and delivers real-time streaming responses in text and natural speech. It uses a Thinker-Talker architecture with a Mixture-of-Experts (MoE) design, early text-first pretraining, and mixed multimodal training to support strong performance across all modalities without sacrificing text or image quality. The model supports 119 text languages, 19 speech input languages, and... -
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CogVLM
A state-of-the-art open visual language model
CogVLM is an open-source visual–language model suite—and its GUI-oriented sibling CogAgent—aimed at image understanding, grounding, and multi-turn dialogue, with optional agent actions on real UI screenshots. The flagship CogVLM-17B combines ~10B visual parameters with ~7B language parameters and supports 490×490 inputs; CogAgent-18B extends this to 1120×1120 and adds plan/next-action outputs plus grounded operation coordinates for GUI tasks. The repo provides multiple ways to run models... -
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NeMo Curator
Scalable data pre processing and curation toolkit for LLMs
NeMo Curator is a Python library specifically designed for fast and scalable dataset preparation and curation for large language model (LLM) use-cases such as foundation model pretraining, domain-adaptive pretraining (DAPT), supervised fine-tuning (SFT) and paramter-efficient fine-tuning (PEFT). It greatly accelerates data curation by leveraging GPUs with Dask and RAPIDS, resulting in significant time savings. The library provides a customizable and modular interface, simplifying pipeline... -
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Mosec
A high-performance ML model serving framework, offers dynamic batching
Mosec is a high-performance and flexible model-serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. -
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Automatic batch size selection, distributed training (DDP, DeepSpeed),... -
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BertViz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism. The head view visualizes attention for one or more attention heads in the same layer. It is based on the... -
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GLM-4-Voice
GLM-4-Voice | End-to-End Chinese-English Conversational Model
GLM-4-Voice is an open-source speech-enabled model from ZhipuAI, extending the GLM-4 family into the audio domain. It integrates advanced voice recognition and generation with the multimodal reasoning capabilities of GLM-4, enabling smooth natural interaction via spoken input and output. The model supports real-time speech-to-text transcription, spoken dialogue understanding, and text-to-speech synthesis, making it suitable for conversational AI, virtual assistants, and accessibility... -
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BISHENG
BISHENG is an open LLM devops platform for next generation apps
BISHENG is an open LLM application DevOps platform, focusing on enterprise scenarios. It has been used by a large number of industry-leading organizations and Fortune 500 companies. "Bi Sheng" was the inventor of movable type printing, which played a vital role in promoting the transmission of human knowledge. We hope that BISHENG can also provide strong support for the widespread implementation of intelligent applications. Everyone is welcome to participate.
