NeuralFn

Use or modify the NeuralFn nfn CLI for train, infer, eval, dataset shortcuts, tokenizer selection, artifact paths, fine-tuning flags, CUDA harness scripts, and CLI tests. Use when the user mentions nfn, cli/, train scripts, pretraining files, cached datasets, tokenizers, or graph/weights artifacts.

Build neural network graphs programmatically using the neuralfn Python package. Use whenever the user asks to write Python code that imports neuralfn, creates neurons, builds graphs, wires edges, trains models, serializes graphs, or works with the NeuralFn graph framework directly in code. Do NOT use for MCP tool calls -- see neuralfn-mcp instead.

Build, edit, train, and inspect NeuralFn neural-network graphs using the neuralfn MCP server tools. Use whenever the user asks to create a neural network, build a model (GPT, MoE, Llama, NanoGPT, etc.), train on a dataset, add or wire neurons, execute a graph, manage datasets, or do anything with NeuralFn. The MCP server name is "neuralfn".

Build, train, and export torch-backed neural network models (GPT, Llama, MoE, Jamba, JEPA, diffusion, etc.) using the NeuralFn Python API. Use whenever the user asks to build a language model, train a transformer, use template presets, configure ModelSpec/BlockSpec, compile a torch graph, export weights, or do autoregressive inference with NeuralFn in Python code. For MCP tool operations, use neuralfn-mcp instead.