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https://blog.character.ai/character-ai-open-sources-pipeling-sft-a-scalable-framework-for-fine-tuning-moe-llms-like-deepseek-v3/
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title
Character.AI Open Sources pipeling-sft: A Scalable Framework for Fine-Tuning MoE LLMs like DeepSeek V3
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At Character.AI, we’re excited to share an experimental project from our research team with the open-source community: pipeling-sft — a lightweight yet powerful training framework built for full-parameter supervised fine-tuning (SFT) of large-scale LLMs with Mixture-of-Experts (MoE) architectures.This framework was originally developed to explore better ways of fine-tuning DeepSeek V3, but its capabilities generalize to many similar MoE-based OSS LLMs. Now, we’re releasing it publicly to help the community move faster, scale more efficiently, and customize more easily for downstream tasks.Why This MattersFine-tuning massive language models—especially MoE-based ones—is notoriously challenging. Memory limits, parallelization complexity, and unstable training dynamics all pose significant barriers for researchers and engineers alike. pipeling-sft is designed to make this process simpler, faster, and more stable.Here’s how:Multi-Level Parallelism: Combines pipeline parallelism...
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