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RWKV 6 Mixture Of Experts
Overview :
Flock of Finches 37B-A11B v0.1 is the latest member of the RWKV family, representing an experimental model with 1.1 billion active parameters. Despite being trained on only 109 billion tokens, it performs comparably to the recently released Finch 14B model on common benchmark tests. This model employs an efficient sparse mixture of experts (MoE) approach, activating only a portion of parameters for any given token, thereby saving time and reducing computational resource usage during training and inference. Although this architectural choice incurs higher VRAM usage, from our perspective, it is highly beneficial to train and operate a model with greater capacity at a lower cost.
Target Users :
The target audience includes AI researchers, data scientists, and machine learning engineers who need to handle large-scale datasets and seek to improve the efficiency of model training and inference. The Flock of Finches provides a model with a higher total parameter count but greater computational efficiency through MoE technology, making it suitable for professionals requiring large-scale model training and deployment on limited resources.
Use Cases
Researchers utilize the Flock of Finches model for natural language processing tasks such as text classification and sentiment analysis.
Data scientists leverage this model for large-scale language model training and testing on limited hardware resources.
Machine learning engineers integrate Flock of Finches into their projects to enhance model parameter efficiency and computational performance.
Features
- 1.1 billion active parameters with a total of 3.7 billion parameters in the MoE RWKV-6 architecture.
- Saves time and computational resources during training and inference via MoE technology.
- Utilizes hash routing for uniform distribution of tokens to experts, enhancing inference efficiency.
- Combines shared experts with new experts to provide dynamically selected double-width feedforward networks (FFN).
- Trains new experts using a high initial learning rate, gradually reducing to the original model's learning rate as training progresses.
- Supports token-shift application among new experts to improve model efficiency.
- Performs comparably to the Finch 14B model across various industry-standard benchmark tests.
How to Use
1. Visit the Hugging Face platform to download the Flock of Finches model and code.
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2. Set up the necessary hardware environment according to the documentation, ensuring there is sufficient VRAM.
3. Use the Featherless AI platform for rapid testing and comparison of the model.
4. Fine-tune and optimize the model based on project requirements.
5. After training the model, conduct benchmarking with tools like lm-eval-harness.
6. Adjust model parameters and structure based on test results for optimal performance.
7. Deploy the trained model into practical applications such as chatbots and text generation.
8. Continuously monitor model performance and iteratively optimize based on feedback.
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