Marxist LLM Assistant

Example Use Cases

• 1

  Remote inference access to a Marxism-specialized LLM via MCP for conversational or Q&A applications.

• 2

  Retrieval-augmented generation (RAG) to combine external documents with the model’s Marxist-theory knowledge when answering queries.

• 3

  Coordinate or launch distributed fine-tuning jobs (LoRA/DeepSpeed) on multi-GPU servers before exposing the model via MCP.

Description

This project offers a pipeline to fine-tune mainstream LLMs (e.g., Llama 3) on Marxist theory using SFT and efficient LoRA parameter tuning, plus scripts for multi-GPU distributed training. It exposes the trained model via an MCP (Model Context Protocol) server so remote clients can call the model over the network. The codebase also supports retrieval-augmented generation (RAG) tool calls and includes utilities for training configuration, DeepSpeed integration, and MCP client/server setup. It is suitable for research, education, or deployment scenarios where a domain-specialized LLM needs remote access and scalable training.