cd .. #回到上层目录 git clone https://github.com/derrian-distro/LoRA_Easy_Training_Scripts cd LoRA_Easy_Training_Scripts git submodule init #初始化git子模块 git submodule update #升级子模块 cd sd_scripts pip install --upgrade -r requirements.txt #升级文本下的依赖 此时可能会更新: 但是tensorf...
之后安装Lora训练器: cd..#回到上层目录gitclone https://github.com/derrian-distro/LoRA_Easy_Training_ScriptscdLoRA_Easy_Training_Scriptsgitsubmodule init#初始化git子模块gitsubmodule update#升级子模块cdsd_scripts pipinstall--upgrade -r requirements.txt#升级文本下的依赖 此时可能会更新: 但是tensorflow可...
If the training is too aggression that it is easy to overcook, you can lower thelearning rate. This will cause a smaller update to be made to the model. You may need to increase the image repeats or the number of epochs to compensate. Reference LoRA training parameters– An authoritative ...
Training Stage: StageIntroductionPython scriptShell script Stage 1: Continue Pretraining 增量预训练 pretraining.py run_pt.sh Stage 2: Supervised Fine-tuning 有监督微调 supervised_finetuning.py run_sft.sh Stage 3: Reward Modeling 奖励模型建模 reward_modeling.py run_rm.sh Stage 4: Reinforcement Le...
基于ChatGPT Training Pipeline,本项目实现了领域模型--医疗模型的四阶段训练: 第一阶段:PT(Continue PreTraining)增量预训练,在海量领域文档数据上二次预训练GPT模型,以注入领域知识 第二阶段:SFT(Supervised Fine-tuning)有监督微调,构造指令微调数据集,在预训练模型基础上做指令精调,以对齐指令意图 ...
Continue pretraining of the base llama-7b model to create llama-7b-pt: 代码语言:shell AI代码解释 cdscriptsshrun_pt.sh 训练参数说明wiki 如果你的显存不足,可以改小batch_size=1, block_size=512(影响训练的上下文最大长度); 如果你的显存更大,可以改大block_size=2048, 此为llama原始预训练长度,不...
Continue pretraining of the base llama-7b model to create llama-7b-pt: AI检测代码解析 cdscriptsshrun_pt.sh 1. 2. 训练参数说明wiki 如果你的显存不足,可以改小batch_size=1, block_size=512(影响训练的上下文最大长度); 如果你的显存更大,可以改大block_size=2048, 此为llama原始预训练长度,不能...
PEFT is integrated with Transformers for easy model training and inference, Diffusers for conveniently managing different adapters, and Accelerate for distributed training and inference for really big models. [!TIP] Visit thePEFTorganization to read about the PEFT methods implemented in the library and...
Our training script uses `HQQBackend.ATEN_BACKPROP`, so also make sure to build the custom kernels `cd hqq/kernels && python setup_cuda.py install`. * Wei 齐思用户 分享了一个链接 119 阅读 长按识别参与讨论 齐思用户 用于LLM的QLoRA+FSDP训练的GitHub文件没有充分解决GCU间通信的关键作用,这...
prepare_model_for_int8_training, prepare_model_for_kbit_training, set_peft_model_state_dict, ) import transformers from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR from transformers import ( CONFIG_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, ...