LLM(大型语言模型)微调(Fine-tuning)是指在特定任务上调整或优化预训练的大型语言模型的过程。通过微调,模型能够更好地适应和处理特定类型的数据或解决特定的问题。这一过程通常包括以下几个步骤: 选择模型:…
2.5 Iterative Fine-tuning 两种方法对比 文章主要使用了两种 finetuning 的方法说明,并给出了两种方法的对比分析: PPO:标准 RLHF 算法,与 OpenAI 在 InstructGPT 中方法相似。 Rejection Sampling finetuning:作者从模型中采样 K 个输出并使用之前介绍的奖励函数选择最佳的候选结果,这与 Bai 等人(2022b)的方法相一...
Fine-tuning larger LLMs, such as the Llama 2 70B, demands increased computational power, VRAM, and time. In our assessments with configurations of 4 and 8 Intel® Data Center GPU Max Series cards on a single server, we observed notable efficiency gains. Specifically, a single ...
In the rapidly evolving field of Generative AI (GenAI), fine-tuning large language models (LLMs) like LLama2 presents unique challenges due to the computational and memory demands of the workload. However, the newly enabledLow-Rank Adaptations (LoRA)on Gaudi2 accelerators present a p...
作为LLaMA的延续和升级,Llama2的训练数据扩充了40%,达到2万亿token,并且可处理的上下文增倍,达到4096个token。整体finetuning过程使用了1百万人工标记数据。开源的基座模型包括7B、13B、70B3个版本,并提供了对话增强版本的Llama chat和代码增强版本的Code Llama,供开发者和研究人员使用。
【Llama-2微调:为特定应用定制模型的全面案例研究。分析了Llama-2模型在非结构化文本的功能性表示(ViGGO)、SQL生成(SQL-create-context)、小学数学问题回答(GSM8k)三种真实用例下的应用,表明Fine-Tuning在各方面都显著提高了准确性。在某些任务中(例如SQL生成或功能性表示),Fine-Tuning后的小型Llama-2模型的性能甚至...
Reward Modeling PPO training DPO training full-parameter fine-tuning all weights. partial-parameter freeze some weights and change some weights, set layers.trainable=True or False to let them to be trainable or not. LoRA QLoRA command parameter ...
fine-tuning the Llama-2 base models. In Functional representation and SQL gen tasks with fine-tuning we can achieve better performance than GPT-4 while on some other task like math reasoning, fine-tuned models, while improving over the base models, are still not able to reach G...
AWS customers sometimes choose to fine-tune Llama 2 models using customers’ own data to achieve better performance for downstream tasks. However, due to Llama 2 model’s large number of parameters, full fine-tuning could be prohibitively expensive and time consumin...
原文链接:https://ragntune.com/blog/gpt3.5-vs-llama2-finetuning 本文由OneFlow编译发布,转载请联系授权。 作者| Sam L'Huillier 译者 | 杨婷、宛子琳 责编| 夏萌 出品| OneFlow 本文中,我将分享在SQL任务和函数表示任务中,对GPT-3.5与LLaMA 2的微调进行基准测试的实验。总体而言: ...