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Lordog/dive-into-llms

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40,974 stars·4,986 forks·Jupyter Notebook·29 views

Dive Into Llms

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 project distinguishes itself by offering an interactive web interface that allows for the deployment and publication of trained models directly to a browser. This enables users to test and interact with model results through a standardized web-based environment.

The platform supports the creation of flexible workflows by separating data processing, model architecture, and evaluation into independent stages. These capabilities are delivered through a collection of Jupyter Notebooks that facilitate the development and maintenance of specialized artificial intelligence solutions.

Features

  • LLM Fine-Tuning Engines - Provides a specialized engine optimized for the efficient fine-tuning of large language models on custom datasets.
  • Large Language Model Fine-Tuning Frameworks - Provides a framework for adapting pre-trained large language models to specific tasks or datasets.
  • Model Fine-Tuning - Provides procedures for adapting pre-trained models to specific datasets to improve performance.
  • Interactive Model Interfaces - Provides a browser-based platform for deploying and testing trained machine learning models.
  • Modular Pipeline Orchestrators - Structures machine learning workflows by separating data processing, training, and evaluation into independent, modular components.
  • Web-Based Model Deployment - Publishes trained machine learning models to web interfaces for direct user testing and interaction.
  • Parameter Efficient Fine-Tuning - Adapts large pre-trained models by updating only a small subset of weights to reduce memory and computational overhead.
  • AI and Machine Learning - Programming practice tutorials for large language models.
  • Books and Guides - Deep dive into large language model architectures.
  • Learning Resources - Comprehensive guide to understanding LLMs.
  • Neural Network Layers - Structures neural networks into discrete, interchangeable blocks for easier customization and optimization.

Star history

Star history chart for lordog/dive-into-llmsStar history chart for lordog/dive-into-llms

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Dive Into Llms

These projects share indexed features with Dive Into Llms. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • zai-org/chatglm3zai-org avatar

    zai-org/ChatGLM3

    13,764View on GitHub↗

    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

    Python
    View on GitHub↗13,764
  • qwenlm/qwenQwenLM avatar

    QwenLM/Qwen

    21,294View on GitHub↗

    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

    Pythonchineseflash-attentionlarge-language-models
    View on GitHub↗21,294
  • ymcui/chinese-llama-alpacaymcui avatar

    ymcui/Chinese-LLaMA-Alpaca

    18,944View on GitHub↗

    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

    Pythonalpacaalpaca-2large-language-models
    View on GitHub↗18,944
  • lyogavin/airllmlyogavin avatar

    lyogavin/airllm

    11,508View on GitHub↗

    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

    Jupyter Notebookchinese-llmchinese-nlpfinetune
    View on GitHub↗11,508
Compare all 30 related projects→

Frequently asked questions

What does lordog/dive-into-llms do?

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.

What are the main features of lordog/dive-into-llms?

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.

Which projects share features with lordog/dive-into-llms?

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…