The 3FS repository (standing likely for “Feature 3F System” or similar) is focused on providing a feature extraction and transformation framework tailored to deep and large models, especially in token-based systems. Its primary aim is to support efficient and scalable feature transformation pipelines—especially for inference environments—by batching, caching, and integrating feature-based modules like segmenters, sparse retrievers, and scorers seamlessly. The repo includes APIs to define components (e.g. seg, ret, scor) that wrap or interface with external or internal modules, as well as logic to schedule and compose these feature transforms. By handling caching and batching at a system level, 3FS helps reduce overhead when many features or modules must be evaluated per input (e.g. in an LLM agent pipeline). The repository includes example integration with models like DeepSeek-V2 / V3, showing how 3FS can be plugged into pipelines for operations like plugin processing.
Features
- Modular components for feature extraction (segmenter, retriever, scorer)
- Caching and batching infrastructure for efficient feature evaluation
- Composition support to chain multiple transforms in a pipeline
- Integration examples targeting DeepSeek models (V2 / V3)
- Designed for low-latency inference environments, reducing overhead
- Extensible APIs to add custom feature modules without rewriting orchestration