cg123/mergekit
Mergekit
Mergekit is a toolkit for combining multiple pretrained large language models into a single model. It functions as an architecture assembler and merging system that transfers capabilities between models using weighted algorithms and layer-wise assembly without requiring additional training.
The project provides specialized utilities for extracting low-rank approximations from fine-tuned models to create portable parameter updates. It also includes a framework for converting dense language models into a mixture of experts architecture by constructing gating mechanisms to route inputs to specialized experts.
The toolkit supports low-memory processing through disk-based sharded loading, allowing large-scale weight merging on hardware with limited system memory. Additional capabilities cover vocabulary merging and tokenizer transplantation to ensure consistent embedding lookups, chat template management, and the orchestration of multi-stage merge pipelines.
Features
- Weight-Space Merging Techniques - Combines multiple pretrained model weights using weighted average algorithms to transfer capabilities without training.
- Model Merging - Provides a comprehensive toolkit for combining multiple pre-trained large language models into a single model using weighted algorithms.
- Model Merging - Combines multiple pretrained large language models into a single model to transfer capabilities without additional training.
- Model Merging Strategies - Provides a comprehensive toolkit for combining multiple pretrained large language models using weighted algorithms.
- Low-Memory Processing - Supports large-scale weight merging on hardware with limited RAM via disk-based sharded loading.
- Mixture of Experts - Converts dense models into a mixture-of-experts architecture by adding gating mechanisms for input routing.
- Low-Rank Adaptation - Extracts low-rank approximations from fine-tuned weights to create portable parameter updates using LoRA techniques.
- Model Architecture Assemblers - Acts as an architecture assembler for building hybrid language models from different source checkpoints.
- Hybrid Layer Assembly - Provides a system for designing hybrid language models by combining specific layers from different pretrained sources.
- Layer-Wise Model Assembly - Builds new models by selectively combining specific layers from different sources into custom hybrid architectures.
- Layer Splicing - Allows the creation of hybrid models by selectively splicing transformer layers from different source checkpoints.
- Binary Weight Processing - Implements merging algorithms that operate directly on binary weight files for models lacking standard transformer library formats.
- Merge Pipelines - Executes multi-stage merge pipelines where sequential weight transformations are chained together.
- Low-Memory Merging - Enables large-scale weight merging on hardware with limited RAM through disk-based sharded loading.
- Weight Approximation Extraction - Includes a utility for extracting low-rank approximations from fine-tuned models to create portable parameter updates.
- Model Transformation Pipelines - Orchestrates complex multi-stage pipelines where the output of one merge operation serves as the input for the next.
- Parameter Efficient Fine-Tuning - Creates smaller and more portable weight updates by extracting low-rank approximations from fine-tuned models.
- Tokenizer Vocabulary Merging - Synchronizes tokenizers by mapping indices between models to ensure consistent embedding lookup.
- Low-Memory Model Merging - Facilitates large language model merging on limited hardware using disk-based data chunking.
- Weight Sharding - Implements disk-based sharded loading to enable large-scale model merging on hardware with limited RAM.
- Advanced Model Techniques - Toolkit for merging multiple trained models into a single performant model.
Star history
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