supervised-fine-tuning

LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs

Present long-context massive language fashions (LLMs) can course of inputs as much as 100,000 tokens, but they battle to generate outputs exceeding even a modest size of two,000 phrases. Managed experiments reveal that the mannequin’s efficient technology size is...

MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning

Owing to its strong efficiency and broad applicability when in comparison with different strategies, LoRA or Low-Rank Adaption is without doubt one of the hottest PEFT or Parameter Environment friendly Nice-Tuning strategies for fine-tuning a big language mannequin. The...

Inside Microsoft’s Phi-3 Mini: A Lightweight AI Model Punching Above Its Weight

Microsoft has not too long ago unveiled its newest light-weight language mannequin known as Phi-3 Mini, kickstarting a trio of compact AI fashions which can be designed to ship state-of-the-art efficiency whereas being sufficiently small to run effectively on...

RAFT – A Fine-Tuning and RAG Approach to Domain-Specific Question Answering

Because the purposes of enormous language fashions broaden into specialised domains, the necessity for environment friendly and efficient adaptation strategies turns into more and more essential. Enter RAFT (Retrieval Augmented High-quality Tuning), a novel strategy that mixes the strengths...

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