target_modules=["q", "v"], lora_dropout=0.05, bias="none", task_type=TaskType.SEQ_2_SEQ_LM ) # prepare int-8 model for training model = prepare_model_for_int8_training(model) # add LoRA adaptor model = get_peft_model(model, lora_config) model.print_trainable_parameters() # trai...
(model_name, num_labels=num_classes) peft_config = LoraConfig( task_type=TaskType.CAUSAL_LM, target_modules=["query", "key", "value"], inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1 ) model = get_peft_model(model, peft_config) model.print_trainable_parameters() print(...
此函数通过设置必要的配置来初始化 QLoRA 的模型。 10. 设置 PEFT 进行微调 现在让我们定义用于微调基本模型的 LoRA 配置。 from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training config = LoraConfig( r=32, #Rank lora_alpha=32, target_modules=[ 'q_proj', 'k_proj', 'v...
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...
r=other_args.lora_r, lora_alpha=other_args.lora_alpha, 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)
=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_alpha=lora_alpha, lora_dropout=lora_dropout, bias= "none", target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"], task_type="CAUSAL_LM" ) 装载数据集argilla/databricks-dolly-15k-curated-en ...