现在,我们可以使用 peft 为LoRA int-8 训练作准备了。 from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, TaskType # Define LoRA Config lora_config = LoraConfig( r=16, lora_alpha=32, target_modules=["q", "v"], lora_dropout=0.05, bias="none", task_type=Tas...
lora_r = 8 # Alpha parameter for LoRA scaling lora_alpha = 16 # Dropout probability for LoRA layers lora_dropout = 0.1 lora_config = LoraConfig( r=lora_r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, bias= "none", target_modules=["q_proj", "o_proj", "k_proj", "v_proj"...
target_modules=["query","value"],lora_dropout=0.1,bias="none",modules_to_save=["classifier"],)lora_model=get_peft_model(model,config)print_trainable_parameters(lora_model)"trainable params: 667493 || all params: 86466149 || trainable%: 0.77"...
Mistral 7B 分类器的 LoRA 设置 对Mistral 7B 模型而言,我们需要指定target_modules(我们将其指定为注意力模块的查询向量映射层和值向量映射层): frompeftimportget_peft_model, LoraConfig, TaskType mistral_peft_config = LoraConfig( task_type=TaskType.SEQ_CLS, r=2, lora_alpha=16, lora_dropout=0.1...
target_modules=other_args.lora_target_modules, lora_dropout=other_args.lora_dropout, bias="none", task_type="SEQ_2_SEQ_LM", ) model = get_peft_model(model, lora_config) model.base_model.model.encoder.enable_input_require_grads()
=0.3, orth_reg_weight=0.2, # lora_alpha=32, # lora_dropout=0.05, bias="none", task_type=TaskType.CAUSAL_LM, target_modules=["query_key_value"], inference_mode=False, r=lora_r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, ) lora_model = get_peft_model(glm_model, lora_...
lora_register_forward_hook ... ['word_embeddings', 'input_layernorm'] lora_target_modules ... [] loss_scale ... None loss_scale_window ... 1000 lr ... None lr_decay_iters ...
lora_config = loraconfig( target_modules=["q_proj","k_proj"], modules_to_save=["lm_head"], ) model.add_adapter(lora_config) 训练推理优化 几个方面的加速 基于deepspeed的加速 1 2 3 4 5 6 git clone -b v2.0.8https://github.com/dao-ailab/flash-attention cdflash-attention && pip ...
from peft import LoraConfig, get_peft_model config = LoraConfig( r=8, lora_alpha=16, target_modules=["q_proj", "k_proj", "v_proj", "o_proj"], lora_dropout=0.05, bias="none", task_type="CAUSAL_LM" ) model = get_peft_model(model, config) data = pd.read_csv("my_csv.csv...
LoRA 是一种改进的微调方法,它不是微调构成预训练大型语言模型权重矩阵的所有权重,而是微调近似于这个较大矩阵的两个较小矩阵。这些矩阵构成了 LoRA 适配器。然后,将此微调的适配器加载到预训练模型中并用于推理。 在针对特定任务或用例进行 LoRA 微调后,结果是原始 LLM 保持不变,并且出现了一个相当小的“LoRA 适...