load_checkpoint_in_model(unwrapped_model, save_directory, device_map={"": device}) 也可以使用load_checkpoint_and_dispatch() 函数在空模型中加载完整检查点或分片检查点,它还会自动在您可用的设备(GPU、CPU RAM)上分配这些权重。完整的模型分片推理过程见此YouTube视频。 load_checkpoint_and_dispatch函数常...
from peft import LoraConfig,get peft model from modelscope import AutoTokenizer,AutoModel from torch.utils.data import DataLoader,Dataset model_dir = "../../chatglm3-6b" with torch.no_grad(): tokenizer = AutoTokenizer.from_pretrained(model_dir,trust_remote_code=True) model = AutoModel.from...
在上一期的大模型技术实践中,我们介绍了增加式方法、选择式方法和重新参数化式方法三种主流的参数高效微调技术(PEFT)。微调模型可以让模型更适合于我们当前的下游任务,但当模型过大或数据集规模很大时,单个加速器(比如GPU)负载和不同加速器之间的通信是值得关注的问题,这就需要关注并行技术。 并行化是大规模训练中训...
将inputs 从主 GPU 分发到所有 GPU 上 将model 从主 GPU 分发到所有 GPU 上 每个GPU 分别独立进行前向传播,得到 outputs 将每个 GPU 的 outputs 发回主 GPU 在主GPU 上,通过 loss function 计算出 loss,对 loss function 求导,求出损失梯度 计算得到的梯度分发到所有 GPU 上 反向传播计算参数梯度 将所有...
"o_proj"] if '70b' in model_from: model.config.max_position_embeddings = 4096 peft_config = LoraConfig( lora_alpha=alpha, lora_dropout=0.1, r=rank, bias="none", task_type="CAUSAL_LM", target_modules=target_modules ) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_...
I have tried loading the base model (tiiuae/falcon-7b-instruct) without any PEFT adapter that worked. I have loaded the base model using accelerate on 2 GPUs. The falcon-7b-instruct model is in .bin format, not in the safetensors format. Therefore, I tried loading mistralai/Mistral-7B...
model = get_peft_model(model, lora_config) model.print_trainable_parameters() optimizer = Adam(model.parameters(), lr=2e-5, weight_decay=0.001) return model, optimizer def evaluate(model, validloader, accelerator: Accelerator): model.eval() ...
model = get_peft_model(model, peft_config) ... model训练和保存 model_state_dict = lora.lora_state_dict(model) torch.save(path,model_state_dict ) 三、rwkv-lora微调 rwkv的微调主要的重点内容在于数据的整理(整理成模型可训练的格式)、训练环境的搭建、训练代码的修改和最后的模型效果评估,其中至于...
zero3_save_16bit_model: true zero_stage: 3 distributed_type: DEEPSPEED downcast_bf16: 'no' dynamo_config: {} fsdp_config: {} machine_rank: 0 main_training_function: main megatron_lm_config: {} mixed_precision: fp16 num_machines: 2 ...
model-cards.md mteb.md nystromformer.md open_rail.md openvino.md opinion-classification-with-kili.md optimum-inference.md optimum-onnxruntime-training.md paddlepaddle.md peft.md perceiver.md playlist-generator.md porting-fsmt.md pretraining-bert.md pricing-update.md pytorch-ddp-accel...