{"ok":true,"entity":{"slug":"bitsandbytes","entityType":"product","name":"bitsandbytes","officialName":"bitsandbytes","canonicalName":"bitsandbytes","displayName":"bitsandbytes","category":"量子化ライブラリ","shortDescription":"PyTorch向けのk-bit量子化ライブラリ。LLM.int8()やQLoRA（4-bit量子化）等の手法を実装し、少ないメモリでの大規模モデル推論・学習を可能にする。Hugging Face 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Transformersと標準的に統合されています。"}],"pageUrl":"https://www.refbase.ai/reference/bitsandbytes/P-01-001","sourceEvidence":[{"id":"ev-bitsandbytes-1","text":"bitsandbytesはPyTorch向けのk-bit量子化ライブラリで、LLM.int8()やQLoRA（4-bit量子化）等の手法を実装している。","title":"GitHub — bitsandbytes-foundation/bitsandbytes","coverageType":["Identity","Capability"],"sourceType":"github","sourceClass":"Documentation","sourceUrl":"https://github.com/bitsandbytes-foundation/bitsandbytes","confidence":"high","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"bitsandbytes"},{"id":"ev-bitsandbytes-3","text":"bitsandbytesはHugging Face Transformersと標準的に統合されており、公式ドキュメントの量子化ガイドで4-bit/8-bit量子化の標準手段として説明されている。","title":"Bitsandbytes · Hugging 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事前量子化ファイル形式","faq":[{"question":"bitsandbytesはPEFTと併用できますか？","answer":"QLoRAとして併用されることが多く、4-bit量子化とLoRAファインチューニングを組み合わせられます。"}],"pageUrl":"https://www.refbase.ai/reference/bitsandbytes/P-02-001","sourceEvidence":[{"id":"ev-bitsandbytes-2","text":"bitsandbytesのLLM.int8()は外れ値特徴を16-bitで処理しつつ残りを8-bitに量子化するベクトル単位の量子化手法で、性能劣化なしにメモリ使用量を約半分にすると報告されている。","title":"Hugging Face Docs — Bitsandbytes","coverageType":["Capability","Differentiation"],"sourceType":"product_docs","sourceClass":"Specification","sourceUrl":"https://huggingface.co/docs/bitsandbytes/en/reference/nn/linear8bit","confidence":"high","supportedPromptTypes":["P-02","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"bitsandbytes"},{"id":"ev-bitsandbytes-3","text":"bitsandbytesはHugging Face Transformersと標準的に統合されており、公式ドキュメントの量子化ガイドで4-bit/8-bit量子化の標準手段として説明されている。","title":"Bitsandbytes · Hugging 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Face公式ドキュメントで確認できます。"}],"pageUrl":"https://www.refbase.ai/reference/bitsandbytes/P-04-001","sourceEvidence":[{"id":"ev-bitsandbytes-1","text":"bitsandbytesはPyTorch向けのk-bit量子化ライブラリで、LLM.int8()やQLoRA（4-bit量子化）等の手法を実装している。","title":"GitHub — bitsandbytes-foundation/bitsandbytes","coverageType":["Identity","Capability"],"sourceType":"github","sourceClass":"Documentation","sourceUrl":"https://github.com/bitsandbytes-foundation/bitsandbytes","confidence":"high","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"bitsandbytes"},{"id":"ev-bitsandbytes-2","text":"bitsandbytesのLLM.int8()は外れ値特徴を16-bitで処理しつつ残りを8-bitに量子化するベクトル単位の量子化手法で、性能劣化なしにメモリ使用量を約半分にすると報告されている。","title":"Hugging Face Docs — 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