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ChatGLM3 is a comprehensive framework for deploying, fine-tuning, and serving large language models. It functions as a high-performance inference engine designed to support conversational AI, enabling developers to build interactive agents capable of multi-turn dialogue, autonomous code execution, and structured tool invocation. The project distinguishes itself through its focus on hardware-agnostic deployment and resource optimization. It supports distributed model parallelism across multiple graphics cards, paged key-value caching for concurrent request processing, and weight quantization t
Qwen is a comprehensive framework for large language model development, serving, and deployment. It provides a complete ecosystem for transformer-based sequence modeling, offering base models alongside specialized tools for instruction-tuned alignment, fine-tuning, and long-context inference. The project is designed to support both research and production environments, enabling users to train, optimize, and host generative models locally or across distributed hardware. The framework distinguishes itself through its focus on high-performance serving and extensibility. It features a high-perfor
This project is a comprehensive toolkit for adapting large language models to the Chinese language, providing a specialized framework for fine-tuning, inference, and local deployment. It serves as a coordinated suite for language-specific adaptation, including tools for expanding tokenizers and implementing retrieval-augmented generation. The project distinguishes itself through a complete pipeline for model adaptation, featuring multilingual tokenizer expansion and a fine-tuning framework that supports instruction-based supervised training and adapter merging. It also includes a dedicated de
Airllm is a framework designed to execute and fine-tune large language models on consumer-grade hardware. By employing layer-wise model decomposition and memory-efficient loading techniques, the engine enables the operation of massive models that would otherwise exceed available system or video memory. The project distinguishes itself through a suite of optimization strategies that balance memory footprint with performance. It utilizes block-wise weight quantization and asynchronous layer prefetching to reduce resource consumption and hide data transfer latency. Additionally, the framework su
Dive into LLMs is a framework designed for fine-tuning large language models and constructing modular machine learning pipelines. It provides a structured environment for adjusting pre-trained models on custom datasets while optimizing computational efficiency and training time.
The main features of lordog/dive-into-llms are: LLM Fine-Tuning Engines, Large Language Model Fine-Tuning Frameworks, Model Fine-Tuning, Interactive Model Interfaces, Modular Pipeline Orchestrators, Web-Based Model Deployment, Parameter Efficient Fine-Tuning, AI and Machine Learning.
Projects with overlapping indexed features include: zai-org/chatglm3 — ChatGLM3 is a comprehensive framework for deploying, fine-tuning, and serving large language models. It functions as a… qwenlm/qwen — Qwen is a comprehensive framework for large language model development, serving, and deployment. It provides a… ymcui/chinese-llama-alpaca — This project is a comprehensive toolkit for adapting large language models to the Chinese language, providing a… lyogavin/airllm — Airllm is a framework designed to execute and fine-tune large language models on consumer-grade hardware. By employing… microsoft/lora — LoRA is a framework for parameter-efficient fine-tuning of large-scale neural networks. It functions by injecting… datawhalechina/hello-agents — This project provides a comprehensive framework for building, training, and managing autonomous agents. It enables the…