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This step‑by‑step tutorial shows how to set up CUDA 12.4, install required packages, prepare a JSON dataset and custom reward, troubleshoot out‑of‑memory errors, and launch DeepSeek R1 training on an 8‑GPU A100 cluster using Accelerate, Deepspeed zero‑3 and vLLM configurations.
DeepSeek R1 builds on the DeepSeek V3 base model using a multi‑stage reinforcement learning pipeline—including GRPO optimization, rule‑based reward modeling, supervised fine‑tuning, language‑consistency rewards, rejection sampling, and distillation—to produce a high‑performing, aligned LLM capable of accurate reasoning.
This tutorial walks through fine‑tuning Shanghai AI Lab’s open‑source InternLM models with XTuner, explaining chat‑format conventions, loading and inference (including multimodal InternLM‑XComposer), dataset preparation, configuration sections, DeepSpeed acceleration, and memory‑efficient QLoRA details for 7‑B‑parameter chat models.
This article examines the main GPU/TPU cluster networking options—including NVLink, InfiniBand, RoCE Ethernet Fabric, and DDC full‑schedule networks—explaining their latency, loss‑less transmission, congestion control, cost, scalability, and suitability for large‑scale LLM training workloads.