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LianjiaTech/BELLE

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BELLE

BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction tuning framework. It provides a pipeline for training, evaluating, and deploying models optimized for natural language understanding and dialogue tasks in the Chinese language.

The project is distinguished by its integrated approach to model refinement, combining the curation of multi-million entry instruction datasets with a distributed training pipeline. This pipeline supports both full fine-tuning and low-rank adaptation to optimize conversational performance.

The system includes a comprehensive evaluation suite that utilizes categorized test benchmarks and automated scoring prompts to assess output quality. For deployment, it provides a quantized runtime that enables these models to run locally and offline on both desktop and mobile devices.

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Features

  • Chinese Conversational LLMs - Implements a specialized conversational large language model optimized for natural language understanding in Chinese.
  • Automated Output Evaluation - Uses secondary large language models to score outputs based on predefined rubrics and test datasets.
  • Training Pipelines - Implements a full pipeline for creating and fine-tuning large language models optimized for the Chinese language.
  • Instruction - Provides a system for gathering and curating multi-million entry Chinese language instruction datasets.
  • Distributed Gradient Synchronization - Implements mechanisms to coordinate weight updates across multiple GPU nodes for large-scale parallel training.
  • Instruction Tuning - Utilizes curated prompt-response pairs to refine base model behavior for improved conversational alignment.
  • Instruction Tuning Frameworks - Offers an integrated system for curating datasets, fine-tuning, and evaluating models for conversational performance.
  • LoRA Training - Employs Low-Rank Adaptation to reduce memory requirements by updating only a small subset of weight matrices.
  • Training Pipelines - Provides an end-to-end workflow combining full fine-tuning and low-rank adaptation to adapt models to instruction sets.
  • Distributed Fine-Tuning - Implements distributed training systems to support both full fine-tuning and low-rank adaptation.
  • Model Evaluation Suites - Provides a comprehensive suite of categorized test benchmarks and scoring prompts to assess model outputs.
  • Weight Quantization - Converts high-precision floating point weights into lower-bit integers to decrease model size and memory usage.
  • LLM Evaluation - Provides tools for measuring output quality and accuracy using standardized test sets and automated scoring.
  • Local Chat Applications - Provides a cross-platform application for deploying quantized models on mobile and desktop for offline interaction.
  • Offline Deployments - Enables the execution of quantized models on local devices for private interaction without internet access.
  • Model Inference Wrappers - Ships a shared interface that allows the model engine to run on both mobile and desktop operating systems.
  • Quantized Inference Runtimes - Provides a cross-platform runtime environment designed to execute compressed and quantized language models.
  • Data Expansion - Explores the impact of instruction data scaling on model performance.
  • Foundation Models - Instruction-tuned language models based on LLaMA and Alpaca architectures.
  • Generative Language Models - Open-source instruction-tuned model for Chinese language applications.
  • Instruction Tuning Datasets - Large-scale Chinese instruction-tuning datasets for model training.
  • Language Models - A project focused on developing Chinese-language conversational models.
  • Open Source Models - Provides Chinese-optimized conversational models.
  • Specialized Domain Models - Instruction-tuned model for general and vertical tasks.
  • Text LLM Models - Instruction-tuned models based on BLOOM and LLaMA architectures.
  • Instruction Datasets - Self-instruct generated dataset for diverse task training.
8,273 stars·761 forks·HTML·Apache-2.0·22 views

Star history

Star history chart for lianjiatech/belleStar history chart for lianjiatech/belle

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.

Frequently asked questions

What does lianjiatech/belle do?

BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction tuning framework. It provides a pipeline for training, evaluating, and deploying models optimized for natural language understanding and dialogue tasks in the Chinese language.

What are the main features of lianjiatech/belle?

The main features of lianjiatech/belle are: Chinese Conversational LLMs, Automated Output Evaluation, Training Pipelines, Instruction, Distributed Gradient Synchronization, Instruction Tuning, Instruction Tuning Frameworks, LoRA Training.

Which projects share features with lianjiatech/belle?

Projects with overlapping indexed features include: openlmlab/moss — MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for… instruction-tuning-with-gpt-4/gpt-4-llm — This project is an instruction tuning framework and synthetic data generator that uses high-capacity teacher models to… nlpxucan/wizardlm — WizardLM is a large language model and instruction-tuning framework designed to execute sophisticated coding,… yizhongw/self-instruct — Self-instruct is a framework for generating synthetic instruction datasets and fine-tuning large language models to… databrickslabs/dolly — Dolly is an instruction-tuned large language model designed to follow complex natural language directions. It operates… lm-sys/fastchat — FastChat is a training and serving platform for large language models that provides an integrated toolkit for…

Projects sharing features with BELLE

These projects share indexed features with BELLE. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • openlmlab/mossOpenLMLab avatar

    OpenLMLab/MOSS

    12,140View on GitHub↗

    MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for deploying large language models capable of multi-turn dialogue, general-purpose response generation, and following complex instructions. The system functions as a tool-augmented framework that extends model knowledge through external plugins and tool-call loops. This allows the model to execute tasks via search engines and calculators to augment responses with external data. The project covers model training through supervised conversational fine-tuning and optimizes deployment

    Python
    View on GitHub↗12,140
  • instruction-tuning-with-gpt-4/gpt-4-llmInstruction-Tuning-with-GPT-4 avatar

    Instruction-Tuning-with-GPT-4/GPT-4-LLM

    4,335View on GitHub↗

    This project is an instruction tuning framework and synthetic data generator that uses high-capacity teacher models to produce instruction-following pairs for training smaller student models. It provides datasets and tools for supervised instruction tuning and reinforcement learning from human feedback. The framework specializes in cross-lingual tuning, offering high-quality instruction-following examples in English and Chinese to improve model generalization across different scripts. It includes a reward modeling tool for creating preference datasets and comparative ratings used to train rew

    HTMLalpacachatgptgpt-4
    View on GitHub↗4,335
  • nlpxucan/wizardlmnlpxucan avatar

    nlpxucan/WizardLM

    9,486View on GitHub↗

    WizardLM is a large language model and instruction-tuning framework designed to execute sophisticated coding, mathematical, and conversational tasks. It functions as an AI system for mathematical reasoning and code generation, as well as a synthetic dataset generator used to train other language models. The project is distinguished by its evolutionary instruction tuning, which uses a method to rewrite simple instructions into complex tasks. This process expands training dataset difficulty and produces a high volume of open-domain tasks across various difficulty levels. The system covers capa

    Python
    View on GitHub↗9,486
  • yizhongw/self-instructyizhongw avatar

    yizhongw/self-instruct

    4,602View on GitHub↗

    Self-instruct is a framework for generating synthetic instruction datasets and fine-tuning large language models to improve their instruction-following capabilities. It provides a pipeline for aligning pretrained models with human intentions through a supervised fine-tuning workflow. The system utilizes a synthetic data generator that uses a seed set of tasks to prompt a model to create new instructional data. It includes an instruction dataset curator to remove redundant or low-quality entries, maintaining dataset diversity through a filtered task pool. The framework covers the full alignme

    Python
    View on GitHub↗4,602
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