接下来,我们就可以使用,Transformers Trainer,Accelerate 或者是 PyTorch training loop,来训练自己的模型,模型训练完成后,使用 save_pretrained 函数将模型保存到目录中。 model.save_pretrained("output_dir") 模型保存之后,保存了2个文件,adapter_model.bin的大小在几M到几十M之间,这个跟我们训练的参数量有关 adap...
lora_model.save_pretrained(output_dir,state_dict=deloreanized_sd, max_shard_size="4GB") 八、加载合并后的模型进行推理 original_model_dir = "models/chatglm-6b/" # 原始模型路径,主要是加载对应的tokenizer,把相关文件复制到和新模型一个目录也行 lora_model_dir = "output_dir_lora_merge" # 上一...
loss, predictions=self.model(input_ids, attention_mask, labels)#获取所有gpu上输出的数据avg_loss_multi_gpu = reduce_value(loss, average=True) gather_preds= [torch.zeros_like(predictions, dtype=predictions.dtype)for_inrange(Config.world_size)] gather_labels= [torch.zeros_like(labels, dtype=labe...
model.save_pretrained(output_merged_dir, safe_serialization=True) # save tokenizer for easy inference tokenizer = AutoTokenizer.from_pretrained(qlora_path) tokenizer.save_pretrained(output_merged_dir) Right on that first line, I get this error: RuntimeError: Error(s) in loading state_dict for ...
The "forgetting" also happens if after finetuning I save the model with torch.save(model.state_dict(), model_save_path) and load it like so: model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m") _ = model.load_state_dict(torch.load(model_save_path)) It's weird. ...
model.save_pretrained("output_dir") # model.push_to_hub("my_awesome_peft_model") also works 这只会保存经过训练的增量 PEFT 权重。例如，您可以在此处的 twitter_complaints raft 数据集上找到使用 LoRA 调整的 bigscience/T0_3B : smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM。
--model_name_or_path "PyTorch-NPU/open_llama_7b" \ --data_path alpaca_data.json \ --bf16 True \ --output_dir ./test/output \ --max_steps 2000 \ --per_device_train_batch_size 1 \ --evaluation_strategy "no" \ --save_strategy "steps" \ ...
答：", return_tensors='pt') inputs = inputs.to('cuda:0') output = model.generate...
--model_name_or_path "PyTorch-NPU/open_llama_7b" \ --data_path alpaca_data.json \ --bf16 True \ --output_dir ./test/output \ --max_steps 2000 \ --per_device_train_batch_size 1 \ --evaluation_strategy "no" \ --save_strategy "steps" \ ...
1 中 # 预训练模型存放的位置 pretrained_model_name_or_path = '/root/personal_assistant/model/...