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FastChat is a training and serving platform for large language models that provides an integrated toolkit for fine-tuning, hosting, and benchmarking chatbots. It functions as an inference server capable of hosting multiple models and exposing them via a standardized API for chat applications. The platform distinguishes itself through a distributed model controller that manages worker nodes and routes requests across a hardware-agnostic inference layer supporting various accelerators. It includes a dedicated evaluation framework for assessing model quality using automated judges, multi-turn di
Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and
LLaMA-Factory is a comprehensive suite for dataset preparation, model fine-tuning, memory optimization, and standardized API deployment. It provides a unified platform for the supervised and reward-based fine-tuning of large language models and vision-language models. The framework includes a specialized toolkit for training vision-language models and a model serving interface that deploys trained models through high-performance APIs. It utilizes precision tuning and quantization techniques to reduce the hardware requirements and memory footprint of large models. The system covers data pipel
DeepSpeed is a distributed deep learning optimization library and framework designed for the training and inference of massive AI models. It serves as a model parallelism orchestrator and a toolkit for scaling large language models across multiple GPUs and compute nodes. The project distinguishes itself through 3D parallelism orchestration, which combines data, pipeline, and tensor parallelism. It utilizes ZeRO-based memory partitioning to eliminate redundant storage and employs CPU-offload memory management to move weights and optimizer states to system RAM. Additionally, it provides special
The Official Python Client for Lamini's API
The main features of lamini-ai/lamini are: Language Model Development, Model Training, Open Source Models.
Projects with overlapping indexed features include: lm-sys/fastchat — FastChat is a training and serving platform for large language models that provides an integrated toolkit for… nvidia/megatron-lm — Megatron-LM is a distributed transformer training library and large language model training framework designed to… hiyouga/llama-factory — LLaMA-Factory is a comprehensive suite for dataset preparation, model fine-tuning, memory optimization, and… huggingface/transformers — Transformers is a comprehensive library for machine learning that provides a unified interface for training,… microsoft/deepspeed — DeepSpeed is a distributed deep learning optimization library and framework designed for the training and inference of… rucaibox/llmbox — A comprehensive library for implementing LLMs, including a unified training pipeline and comprehensive model evaluation.