2 LoRA代码解析 2.1 MergedLinear源码解析 2.2 对Llama 进行LoRA 微调 参考 0 前言 最近因为工作需要,在接触一些大模型微调训练相关的算子实现,因为以往接触inference相关比较多,而对于training相关的技术接触的相对较少,所以本文就以LoRA: Low-Rank Adaptation of Large Language Models为例来学习一下大模型微调的一些...
Part 1: Fine-tune a Llama2-7b model using PEFT We are going to use the recently introduced method in the paper QLoRA: Quantization-aware Low-Rank Adapter Tuning for Language Generation by Tim Dettmers et al. QLoRA is a new technique to reduce the memory ...
例如,bitsandbytes参数决定了我们如何使用int4加载量化模型,而QLoRA参数则定义了Qlora的网络结构。同时,TrainingArguments参数和SFT参数则涉及到训练过程的具体细节,比如训练步数、时长以及何时记录loss函数等等。 model_id= "NousResearch/Llama-2-7b-chat-hf" ### # QLoRA parameters ### # LoRA attention ...
LoraConfig, PeftModel, get_peft_model, get_peft_model_state_dict, 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 ...
训练完成后，使用 alpaca-lora 提供的脚本 generate.py 对模型进行推理。添加重复惩罚参数，可减少模型输出中的重复内容。通过启动 web UI，可直接与自定义训练的 LLaMA-2 进行交互。以下是模型在遵循指令、英文输出及日文输出方面的表现：模型在遵循指令方面表现出色，英文输出流畅，且能够生成日文内容。在...
'lora_r': 8, 'lora_alpha': 16, 'lora_dropout': 0.05, 'lora_query': True, 'lora_key': False, 'lora_value': True, 'lora_projection': False, 'lora_mlp': False, 'lora_head': False, 'warmup_steps': 100} Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1 --- distributed_...
lora_dropout(float,optional):LoRA 微调中的 Dropout 系数。 learning_rate(float,optional):AdamW 优化器的初始学习率。如果设置过大会出现loss值无法收敛或过拟合现象即过度适应训练集而丧失泛化能力,对非训练集中的数据失去原本的计算能力。 num_train_epochs(float,optional):训练轮数,如果loss值没有收敛到理想值...
Fine-tune the recent Llama-2-7b model on a single GPU and turn it into a chatbot I will leverage PEFT library from Hugging Face ecosystem, as well as QLoRA for more memory efficient finetuning. - DavidLanz/Llama2-Fine-Tuning-using-QLora
Int8 quantization –Even with optimizations such as LoRA, models such as Llama 70B are still too big to train. To decrease the memory footprint during training, we can use Int8 quantization during training. Quantization typically reduces the precision of the floating point data ...
支持大模型评估,支持BLEU-4、ROUGE-1/2/L; 支持微调训练后的大模型作为训练模型再训练; 系统监控除gpu,支持cpu、mem和disk监控; [23/09/28] 支持baichuan、llama2、llama、glm2等大模型,支持QLoRA; 支持gpu预览、大模型微调训练、模型合并、部署服务(测试中); ...