LLMs are generally trained on public data with no specific focus. Fine-tuning is a crucial step that adapts a pre-trained LLM model to a specific task, enhancing the LLM responses significantly. Although text generation is a well-known application of an LLM, the neural network embeddings obtai...
Prompt-Tuning又可以称为Prompt、Prompting、Prompt-based Fine-tuning等。 1.1 解决问题 降低语义差异(Bridge the gap between Pre-training and Fine-tuning) :预训练任务主要以Masked Language Modeling(MLM)为主,而下游任务则重新引入新的训练参数,因此两个阶段的目标通常有较大差异。因此需要解决如何缩小Pre-trainin...
In classification finetuning, we have a specific number of class labels (for example, "spam" and "not spam") that the model can output A classification finetuned model can only predict classes it has seen during training (for example, "spam" or "not spam", whereas an instruction-finetune...
Fine-tuning Large Language Models (LLMs) has revolutionized Natural Language Processing (NLP), offering unprecedented capabilities in tasks like language translation, sentiment analysis, and text generation. This transformative approach leverages pre-trained models like GPT-2, enhancing their performance on...
RL fine-tuning:将对齐过程视为一个强化学习过程,预训练LM作为policy,指令作为其输入,其输出指令的回答,action space是所有词表,state是当前已经生成的tokens序列,奖励reward由第二步的奖励模型给出。InstructGPT使用PPO算法优化,为了避免最终的Aligned LM与初始预训练LM表现差距过大,还加入了两者对同一指令输入得到的输...
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Fine-Tuning for Causal Language Modeling Causal language modeling involves predicting the next word in a sequence based on the preceding context, enabling tasks like text generation. Fine-tuning a model like Falcon-7B for a specific task involves adapting the pretrained model by provi...
同时,你仍然可以通过传统的“fine-tuning model”API定制自己的微调大模型,这种方式主要是通过你上传格式化的“问-答”型的训练数据文件来实现对LLM的“增强”。相对于最新推出的“Assistant API” ,感觉这种方式在工程化的显得不够灵活和直接,不是很“智能”,目前“fine-tuning model”最高也只能支持GPT-3.5系列模...
当然这里前 3 名都闭源模型,后面开源模型,大多数也都是英文的模型。如果 GPU 资源充足(至少 A100*8),这里也可以基于开源模型做中文的预训练,最后再 finetuning 。但我们没有 GPU 资源, 我们可以选择开源的中文模型直接做微调。 具体有哪些中文模型可以选择,可以参考这两个地址 中文语言理解测评基准(CLUE)[2] 和...
来自论文《P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks》,发表于2022年。 摘要:提示微调(Prompt Tuning),该方法仅对冻结的语言模型中的连续提示进行微调,极大地减少了训练时每个任务的存储和内存使用。然而,在NLU(自然语言理解)的背景下,先前的工作表明,对于...