LLaMA-2-7B用lora的方式训练的显存占用如下,大约在16G. (batch_size=1, max_length=2048) 推理训练后的模型 开源代码: https://github.com/modelscope/modelscope/blob/master/examples/pytorch/llm/llm_infer.py # ### Setting up experimental environment. from _common import * @dataclass class Arguments...
接下来,设置lora所需的PEFT parameters peft_params = LoraConfig( lora_alpha=16, lora_dropout=0.1, r=64, bias="none", task_type="CAUSAL_LM", ) 最后是整体的train设置 training_params = TrainingArguments( output_dir="./results", num_train_epochs=1, per_device_train_batch_size=4, gradient_...
以llama7B模型为例,hidden_size为4096,也就是每个K、V有4096个数据,假设半精度浮点数数据float16,一个Transformer Block中就有409622=16KB的单序列KV缓存空间,而llama2一共32个Transformer Block,所以单序列整个模型需要16*32=512KB的缓存空间,那多序列呢?如果此时句子长度为1024,那就得512MB的缓存空间了。而现在...
2. 将模型转换为Hugging Face支持的格式 复制 pip install git+https://github.com/huggingface/transformerscd transformerspython convert_llama_weights_to_hf.py \ --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir models_hf/7B 1. 现在,我们得到了一个Hugging Face模型,可以利...
# Set training parameters training_arguments = TrainingArguments( output_dir=output_dir, num_train_epochs=num_train_epochs, per_device_train_batch_size=per_device_train_batch_size, gradient_accumulation_steps=gradient_accumulation_steps, optim=optim, ...
2. 将模型转换为Hugging Face支持的格式 pip install git+https://github.com/huggingface/transformerscd transformerspython convert_llama_weights_to_hf.py \ --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir models_hf/7B ...
setting global batch size to 1 WARNING: Setting args.overlap_p2p_comm to False since non-interleaved schedule does not support overlapping p2p communication using torch.float16forparameters ... --- arguments --- accumulate_allreduce_grads_in_fp32 ... False adam_beta1 ......
比较用LoRA微调Roberta、Llama2和Mistral的过程及表现 引言自然语言处理 (NLP) 领域的进展日新月异，你方唱罢我登场。因此，在实际场景中，针对特定的任务，我们经常需要对不同的语言模型进行比较，以寻找最适合的模型。本文主要比较 3 个模型: RoBERTa、Mistral-7B 及 Llama-2-7B。我们用它们来解决一个常见问题 ...
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters...
The device parameters have been replaced with npu in the function below: torch.logspace, torch.randint, torch.hann_window, torch.rand, torch.full_like, torch.ones_like, torch.rand_like, torch.randperm, torch.arange, torch.frombuffer, torch.normal, torch._empty_per_channel_affine_quantized, to...