At the heart of MultiMind SDK lies a model-agnostic client system that abstracts away the complexity of working with diverse LLM architectures—be it transformer-based models like LLaMA or non-transformer models like Mamba, Hyena, or RWKV.
🔁 Model Client & Routing
The Model Client System provides a unified interface to:
- Load and interact with any registered model (local or remote)
- Automatically route user queries to the correct model
- Chain or switch models in multi-agent or hybrid workflows
- Serve models via REST APIs, CLI, or integrate with no-code tools like MultiMindLab
It supports dynamic loading of models by config, file, class, or name via the SDK’s internal registry.
🧠 Model-Agnostic LLM Architecture
MultiMind SDK introduces a flexible BaseLLM interface to unify transformers and non-transformers:
- ✅ Transformer Models Easily fine-tune and run models like LLaMA, Mistral, Falcon, OpenChat, GPT-J, etc. using:
- LoRA/QLoRA/PEFT
- Hugging Face + Ollama + custom backends
- Device management (CUDA, MPS, CPU)
- Adapter hot-swapping + streaming support
- ✅ Non-Transformer Models Support for cutting-edge architectures beyond transformers:
- 🧪 Mamba, RWKV, Hyena, S4, SSMs
- 🔁 Custom RNN/GRU/LSTM/MLP
- 🔌 Plug-and-play with the same pipeline
🧰 Advanced Wrappers for Non-Transformer Models
Each non-transformer model is wrapped with production-ready capabilities:
| Feature | Supported |
|---|---|
| GPU/CPU/device mapping | ✅ |
| LoRA/PEFT support | ✅ |
| Batch & async generation | ✅ |
| Streaming/chat streaming | ✅ |
| Persona/history management | ✅ |
| Logging and eval hooks | ✅ |
| YAML-based config loading | ✅ |
| Custom pre/post-processing | ✅ |
This makes fine-tuning and serving non-transformer models as smooth as transformers.
🔄 Model Conversion Made Simple
Check out the examples/model_conversion folder to:
- 🔧 Convert models between different formats (PyTorch, ONNX, GGUF, etc.)
- 🧠 Quantize models for edge deployment
- ⚙️ Adapt checkpoints for LoRA/QLoRA tuning
- 🎯 Use config-driven templates for automated conversion flows
Supports transformers, gguf, peft, pytorch_model.bin, and more.
🧪 Example Suite for All Model Types
Explore examples/non_transformer/ for a wide array of runnable examples:
✅ Classical ML
- Scikit-learn (SVM, CRF, regression, clustering)
- HMM, statistical NLP, etc.
✅ Deep Learning
- PyTorch (Seq2Seq, RNNs, CNN)
- Keras pipelines
✅ State Space Models (SSMs)
- Mamba, RWKV, Hyena, S4
- Experimental and stable examples
✅ NLP & AutoML
- spaCy, NLTK, TextBlob, Gensim
- CatBoost, XGBoost, LightGBM
✅ Chat, Adapters, and Memory
- Streaming chat with memory context
- Adapter hot-swapping and testing
- Multi-model orchestration
Each example showcases the power of model registration, config-based routing, and adapter management inside the MultiMind SDK.
📦 Built for Developers, Researchers & Startups
Whether you’re:
- Fine-tuning a transformer on Hugging Face
- Wrapping a SSM for low-latency inference
- Building a local/private ChatGPT using RWKV or Mamba
- Creating AutoML workflows with classical models
MultiMind SDK lets you do it all — in one unified framework.
🌍 Try It Out
- ⭐ GitHub: github.com/multimindlab/multimind-sdk
- 💙 Support: opencollective.com/multimind-sdk
- 🌐 No-code GUI coming soon via multimind.dev
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