tensorflow
tensorflow/tensorflow
A machine learning framework for building, training and deploying models.
ToolAI.io · Канал GitHub
Отслеживайте активные AI-репозитории с открытым исходным кодом по темам LLM, агентов, MCP, RAG и программирования, с количеством звёзд и проверенными сведениями о проектах.
Данные публичного репозитория
tensorflow/tensorflow
A machine learning framework for building, training and deploying models.
pytorch/pytorch
A deep learning framework with tensor computation, automatic differentiation and GPU acceleration.
keras-team/keras
A high-level API for building and training deep learning models.
scikit-learn/scikit-learn
A Python machine learning library for classification, regression, clustering and model evaluation.
jingyaogong/minimind
A lightweight implementation for exploring small language model pretraining and fine-tuning.
hiyouga/LlamaFactory
A unified fine-tuning toolkit for large language and vision-language models.
deepspeedai/deepspeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
kaito-project/aikit
🏗️ Fine-tune, build, and deploy open-source LLMs easily!
flyteorg/flyte
Flyte is an open-source orchestration framework for reliably building, scaling, and deploying machine learning pipelines, models, and agents using pure Python.
run-llama/llama_index
LlamaIndex is an open-source Python data framework for building LLM applications with private data, offering data connectors, indexing structures, and advanced retrieval interfaces. It is complemented by LlamaParse, an enterprise platform for agentic OCR, parsing, extraction, and indexing.
langfuse/langfuse
An open-source LLM engineering platform for tracing, evaluating, debugging, and improving AI applications, with prompt management, datasets, metrics, a playground, APIs, and managed or self-hosted deployment options.
oumi-ai/oumi
Oumi is a Python-based platform for preparing data, training and fine-tuning open-weight foundation models, evaluating results, running inference, and deploying models. It provides configuration recipes and a consistent CLI for local, cluster, and cloud workflows.
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