We’ve fine-tuned the 774M parameter GPT-2 language model using human feedback for various tasks, successfully matching the preferences of the external human labelers, though those preferences did not always match our own. Specifically, for summarization
我猜测题主想问的是“alignment fine-tuning”和“instruction/supervised fine-tuning”的区别，所以后面...
但是如果检查他的 truthfulness，很可能可能相比于基座模型还下降了，因为他们的基座模型和 ChatGPT/GPT-4...
Fine-tuning: CUDA_VISIBLE_DEVICES=0 swift sft \ --model Qwen/Qwen2.5-7B-Instruct \ --dataset AI-ModelScope/alpaca-gpt4-data-en \ --train_type lora \ --output_dir output \ ... RLHF: CUDA_VISIBLE_DEVICES=0 swift rlhf \ --rlhf_type dpo \ --model Qwen/Qwen2.5-7B-Instruct \ -...
Fine-tuning before SFT. Despite recent popularity of SFT, language model fine-tuning has long been a popular approach. For example, GPT [7] is fine-tuned directly on each task on which it is evaluated (see below), and encoder-only language models (e.g., BERT [8]) — due to the fa...
Examples: Train GPT2 to generate positive movie reviews with a BERT sentiment classifier, full RLHF using adapters only, train GPT-j to be less toxic,Stack-Llama example, etc. How PPO works Fine-tuning a language model via PPO consists of roughly three steps: ...
We also briefly discussed the crucial initial step of Supervised Fine-Tuning (SFT). These two techniques are commonly used together to tune modern LLMs like GPT or Llama models. SFT is typically used first to teach pre-trained model the “skills” we care about, for example: Downstream (...
Recently, the approach of using powerful LLMs like Claude 3 or GPT-4 for crafting such datasets has evolved as a resource- and time-effective alternative to human labelling. The “dolly-15k” dataset is a popular general-purpose open-source instruct fine-tuning ...
大模型微调(Fine-Tuning)的常见方法 随着大模型的飞速发展,在短短一年间就有了大幅度的技术迭代更新,从LoRA、QLoRA、AdaLoRa、ZeroQuant、Flash Attention、KTO、PPO、DPO、蒸馏技术到模型增量学习、数据处理、开源模型的理解等,几乎每天都有新的发展。 我们总结了算法工程师需要掌握的大模型微调技能,并制作了大模型...
Large language models (LLMs) often demonstrate inconsistencies with human preferences. Previous research typically gathered human preference data and then aligned the pre-trained models using reinforcement learning or instruction tuning, a.k.a. the finetuning step. In contrast, al...