MergeKit: Combine LLMs with Ease and Efficiency

MergeKit is an innovative, open-source toolkit that streamlines the process of merging pre-trained large language models (LLMs). Developed by Arcee.ai, MergeKit offers a robust solution for combining the strengths of various models directly in their weight space, circumventing the need for expensive additional training or complex ensembling.

At its core, MergeKit utilizes an out-of-core approach, enabling users to perform sophisticated merges even in resource-constrained environments. This means you can run merges entirely on CPU or accelerate them with as little as 8 GB of VRAM, making advanced LLM operations accessible to a broader range of users and hardware configurations.

Why Model Merging?

Model merging is a transformative technique in the field of artificial intelligence. Unlike traditional ensembling, which requires running multiple models simultaneously, merged models maintain the same inference cost as a single model while often achieving comparable or superior performance. Key benefits include:

• Combining Specialized Models: Integrate multiple task-specific models into a single, versatile super-model.
• Knowledge Transfer: Transfer capabilities between models without access to their original training data.
• Optimal Trade-offs: Fine-tune model behavior to achieve desired performance characteristics.
• Performance Improvement: Enhance model capabilities while keeping inference costs low.
• New Capabilities: Create novel functionalities through creative model combinations.

Key Features of MergeKit

MergeKit is packed with features designed to handle diverse merging scenarios:

• Broad Model Support: Compatible with popular LLM architectures like Llama, Mistral, GPT-NeoX, StableLM, and more.
• Extensive Merge Methods: Supports a wide array of merging algorithms, including Linear, SLERP, Task Arithmetic, TIES, DARE, DELLA, and Arcee Fusion, each with unique strengths for different use cases.
• Resource Efficiency: Flexible GPU or CPU execution with lazy loading of tensors for minimal memory footprint.
• Advanced Techniques: Features like interpolated gradients, piecewise assembly ("Frankenmerging"), Mixture of Experts (MoE) merging, and Evolutionary Merge Methods.
• LoRA Extraction: Extract PEFT-compatible low-rank approximations from fine-tuned models.
• Multi-Stage Merging: The mergekit-multi tool allows chaining complex merge operations.
• Raw PyTorch Model Merging: mergekit-pytorch extends merging capabilities to arbitrary PyTorch models.
• Tokenizer Transplantation: mergekit-tokensurgeon enables aligning vocabularies between models for tasks like speculative decoding.

Getting Started with MergeKit

Installation is straightforward. Begin by cloning the repository and installing the package:

git clone https://github.com/arcee-ai/mergekit.git
cd mergekit
pip install -e .

For detailed usage, the primary entry point is the mergekit-yaml script, which takes a YAML configuration file to define your merge operations. MergeKit also provides integration with Hugging Face Hub for easy model sharing and cloud merging capabilities via the Arcee App.

Cloud Integration and Beyond

MergeKit offers seamless integration with cloud infrastructure, particularly through Arcee's cloud GPUs. This allows users to launch and manage merges in the cloud, simplifying the process and leveraging powerful hardware without local setup. With options to deploy or download your merged models, MergeKit provides an end-to-end solution for advanced LLM experimentation and deployment.

If you're looking to explore the cutting edge of LLM customization and efficiency, MergeKit is an indispensable tool in your AI arsenal. Its robust features and user-friendly design make it a standout open-source project for anyone working with large language models.