Summary of Implementation: Non-Transformer LLM Support & Advanced Fine-Tuning

1. Model-Agnostic LLM Architecture

• The core LLM interface (BaseLLM) is fully model-agnostic, supporting both transformer and non-transformer models.
• A generic NonTransformerLLM base class and advanced subclasses are provided for easy integration of any model type.

2. Advanced Non-Transformer Model Wrappers

• Production-ready wrappers for Mamba, Hyena, RWKV, SSMs, and custom RNN/MLP models.
• Each wrapper supports:
  • Device and precision management (CPU/GPU, fp16/bf16, device_map)
  • LoRA/PEFT adapter hot-swapping
  • Batch and async batch generation
  • Streaming generation and chat streaming
  • Advanced chat memory/history (with persona/context window)
  • Logging and evaluation hooks
  • Config-driven instantiation (YAML)
  • Custom pre/post-processing hooks

3. Registry and Dynamic Loading

• A unified model registry (model_registry.py) allows dynamic registration and instantiation of any model (transformer or non-transformer) by name, class, or config.

4. Comprehensive Example Suite

• The examples/non_transformer/ folder contains runnable examples for:
  • Classical ML (scikit-learn, CRF, HMM, clustering, regression, etc.)
  • Deep learning (PyTorch RNN/LSTM/GRU/Seq2Seq, Keras CNN)
  • State Space Models (Mamba, S4, Hyena, RWKV, and stubs for advanced SSMs)
  • NLP pipelines (spaCy, NLTK, TextBlob, Gensim)
  • AutoML (CatBoost, LightGBM, XGBoost)
  • Statistical models (ARIMA)
  • Advanced usage (multi-model registration, batch/streaming, adapters, chat memory)
• Each example demonstrates wrapping, registration, and usage with the MultiMindSDK pipeline.

5. Documentation

• The README.md in examples/non_transformer/ provides a detailed table of all examples, usage instructions, and extension tips.
• Code is commented for clarity and extensibility.

6. Extensibility & Research-Readiness

• All advanced features are implemented in a modular way and ported to all major wrappers.

• Extension points and stubs are provided for new research models (e.g., S4ND, Mega-S4, Perceiver, Diffusion, Topological NNs, MoE, etc.).

• Ready for production, research, and agent/RAG workflows.