LLM Full-Stack Infrastructure Open Source: A Complete Solution from SFT Training to RLHF Alignment to Production-Grade Inference Deployment

This article introduces the open-source project LLM-Infrastructure-mvp, which provides an end-to-end large language model infrastructure solution covering the full-link tech stack for supervised fine-tuning (SFT), reward model training, RLHF alignment, high-performance inference services, and production-grade monitoring. It addresses the issues of scattered toolchains and lack of standardized processes for teams, offering a modular and scalable engineering template for teams building their own LLM infrastructure.

Large language model technology is iterating rapidly, but many teams face common challenges: How to connect model training, alignment optimization, and production deployment into a reproducible and scalable engineering system? Scattered toolchains and lack of standardized processes often lead to reinventing the wheel and increase uncertainty in production environments.

The project is positioned as a directly runnable Minimum Viable Product (MVP), using a modular architecture where components can be used independently or combined; the training pipeline implements three-stage alignment (SFT, reward model, RLHF); the inference service uses the vLLM high-performance engine; production-grade infrastructure includes API gateway, model registry, monitoring system, and containerized orchestration.

1. Training pipeline: SFT supports full-parameter/LoRA fine-tuning with configuration management; reward model uses preference learning (pairwise QA samples); RLHF has complete PPO implementation (GAE, value function training, adaptive KL penalty, multi-round updates).
2. Inference service: vLLM engine (PagedAttention improves memory efficiency, continuous batching optimizes throughput), supports OpenAI-compatible API, streaming responses, and INT8/INT4 quantization.
3. Production infrastructure: API gateway (authentication/rate limiting/routing); MLflow model registry (version management/lineage tracking); Prometheus+Grafana monitoring; Docker/K8s deployment (consistent local/production environments).

The project's value lies in integrating scattered tools into a coherent workflow and providing an out-of-the-box solution. Suitable scenarios: enterprise internal LLM platforms (quickly build private services), research teams (standardized experimental environments), technical learning (best practice cases), product prototypes (quickly validate business hypotheses).

Limitations: Currently mainly supports single-node training; distributed training needs improvement; model quantization can be further optimized; multi-modal capabilities to be integrated. Quick Start Path: 1. Environment preparation (Python3.9+, CUDA11.8+, 16GB+ memory); 2. Local deployment (docker-compose.local.yml to verify functions); 3. GPU inference (docker-compose.gpu.yml to start vLLM); 4. Training experiment (run scripts/train_sft.py to observe metrics).