Flama 2.0 Architecture Detailed in 83-Page arXiv Paper, Adding First-Class LLM Support

Flama, a Python framework for deploying machine learning models, has published an 83-page paper on arXiv detailing its architecture, particularly the changes in version 2.0 that make LLMs first-class citizens alongside classic models.

Unified Model Packaging

Every model, whether a scikit-learn pipeline, PyTorch network, or LLM, is packaged into a single .flm file containing weights, inference artifacts (tokenizer, config), and metadata. This structure allows for offline inspection and scoring without loading the full model, enabling CI/CD pipelines to gate promotions based on recorded training metrics. The inference engine is decoupled from the model file, allowing the same LLM to run on vLLM or MLX depending on the deployment environment.

Multi-Protocol Serving

A key feature of Flama 2.0 is the ability to serve a single model across multiple API protocols simultaneously. By adding a model to the application, developers can expose endpoints compatible with OpenAI, Anthropic, and Ollama standards, along with a native streaming chat UI. This is achieved through three independent layers: a dialect layer, a canonical transport layer, and a backend layer (such as vLLM on Linux/CUDA or MLX on Apple Silicon).

Framework Features

The paper outlines several additional subsystems:

• Dependency Injection resolved from type hints.
• Schema validation using Pydantic, Marshmallow, or Typesystem with auto-generated OpenAPI docs.
• CRUD APIs generated from SQLAlchemy tables.
• MCP support to turn any app into an MCP server.
• A Rust core for routing, serialization, parsing, crypto, and compression.