Currently, I am trying to fine tune the Korean Llama model(13B) on a private dataset through DeepSpeed and Flash Attention 2, TRL SFTTrainer. I am using 2 * A100 80G GPUs for the fine-tuning, however, I could not conduct the fine-tuning.. I can't find out the problem and any solu...
也就是在预处理函数中增加一个上下文管理器,在上下文管理器之前编码输入文本,然后在上下文管理器中处理标签,下面是mT5预处理函数的一个例子(后面直接街上map,就可以处理整个数据集)。 max_input_length=512max_target_length=30defpreprocess_function(examples):model_inputs=tokenizer(examples["review_body"]...
scheduler=accelerator.prepare(model,optimizer,training_dataloader,scheduler)forbatchintraining_dataloader:optimizer.zero_grad()inputs,targets=batchoutputs=model(inputs)loss=loss_function(outputs,targets)accelerator.backward(loss)optimizer.step()scheduler.step()...
accelerate launch --config_file examples/accelerate_configs/multi_gpu.yaml --num_processes=1 \ examples/scripts/sft.py \ --model_name mistralai/Mixtral-8x7B-v0.1 \ --dataset_name trl-lib/ultrachat_200k_chatml \ --batch_size 2 \ --gradient_accumulation_steps 1 \ --learning_rate 2e-4...
预训练语言模型(Pre-trained Language Model,PLM)想必大家应该并不陌生,其旨在使用自监督学习(Self-supervised Learning)或多任务学习(Multi-task Learning)的方法在大规模的文本语料上进行预训练(Pre-training),基于预训练好的模型,对下游的具体任务进行微调(Fine-tuning)。目前市面上知名的以英文为主预训练语言模型有...
你还可以将模型自动量化,以 8 位或 4 位模式加载。以 4 位模式加载模型大约需要 9 GB 的内存,使其适用于多种消费级显卡,包括 Google Colab 上的所有 GPU。以下是以 4 位加载生成 pipeline 的方法: pipeline =pipeline( "text-generation", model=model, ...
We support the usage ofaccelerateto wrap the model for distributed evaluation, supporting multi-gpu and tensor parallelism. WithTask Grouping, all instances from all tasks are grouped and evaluated in parallel, which significantly improves the throughput of the evaluation. After evaluation, all instances...
"train_micro_batch_size_per_gpu": 1, "wall_clock_breakdown": False } dschf = HfDeepSpeedConfig(ds_config) # keep this object alive engine = deepspeed.initialize(model=model, config_params=ds_config, optimizer=None, lr_scheduler=None) text = "Is this review positive or negative? Review:...
I'm running run_clm.py to fine-tune gpt-2 form the huggingface library, following the language_modeling example: !python run_clm.py \ --model_name_or_path gpt2 \ --train_file train.txt \ --validation_file test.txt \ --do_train \ --do_eval \ --output_dir /tmp/test-clm This...
Megatron-BERT(来自 NVIDIA) 伴随论文Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism由 Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro 发布。 Megatron-GPT2(来自 NVIDIA) 伴随论文Megatron-LM: Training Multi-Billion ...