¿Cuál es la mejor estrategia para entrenar modelos de lenguaje con DeepSeek?

22 Eyl 09:46

When I set up my first DeepSeek fine‑tune, I broke the workflow into a handful of clear stages. First, I gathered a balanced corpus that mirrors the target domain—mixing raw text, cleaned web scrapes, and a few structured logs—then ran a quick deduplication pass (using MinHash) and filtered out ultra‑short or non‑ASCII lines. Token‑level cleaning (lower‑casing, normalising Unicode, and removing obvious boilerplate) usually gives a solid baseline, but I also kept a small “raw” slice to test how aggressive preprocessing affects downstream performance. For the model itself, I started with DeepSeek‑base‑7B and added a modest adapter layer (LoRA) instead of training all weights; this cuts memory usage dramatically and still lets you capture domain nuances. I monitored perplexity on a held‑out validation set, but I also tracked downstream metrics like BLEU/F1 for the specific downstream task (e.g., QA or summarisation) because raw perplexity can be misleading. Early stopping based on a plateau in those task‑specific scores, combined with dropout (0.1‑0.2) and weight‑decay (1e‑5), kept overfitting in check. Finally, I used mixed‑precision (FP16) and gradient checkpointing to squeeze the training into a single 48 GB GPU, and I scheduled periodic “warm‑up” steps before the main learning‑rate decay to stabilise convergence. Those tricks saved a lot of compute time while still delivering a model that generalises well on my validation set.