使用 Unsloth 與 🤗 TRL 讓 LLM 微調速度提升 2 倍
Original: Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL
Hugging Face's official blog announced a partnership with the Unsloth team to integrate Unsloth's efficient fine-tuning technology directly…
Hugging Face 宣布旗下 TRL（Transformer Reinforcement Learning）微調工具包正式整合 Unsloth。開發者現在只需修改幾行程式碼，即可在進行監督式微調（SFT）時獲得 2 倍以上的訓練速度提升，並減少高達 60% 的 VRAM 記憶體消耗。此整合支援 Llama-2、Mistral 等主流開源模型，且完全不損害模型精度。
Hugging Face's official blog announced a partnership with the Unsloth team to integrate Unsloth's efficient fine-tuning technology directly into Hugging Face's TRL (Transformer Reinforcement Learning) library. This collaboration aims to address the pain points of high computational costs and memory limitations encountered when fine-tuning large language models (LLMs).
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