我们根据 Fine Tune FLAN-T5 准备了一个 run_seq2seq_deepspeed.py 训练脚本，它支持我们配置 deepspeed 和其他超参数，包括 google/flan-t5-xxl 的模型 ID。run_seq2seq_deepspeed.py 链接:https://github.com/philschmid/deep-learning-pytorch-huggingface/blob/main/training/scripts/run_seq2seq_deepspeed.py...
--deepspeed configs/ds_flan_t5_z3_config_bf16.json huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either: - Avoid using `tokenizers` before the fork if possible...
--deepspeed configs/ds_flan_t5_z3_config_bf16.json huggingface/tokenizers: The currentprocessjust got forked,afterparallelism has already been used. Disabling parallelismtoavoid deadlocks... Todisable thiswarning, you can either: - Avoid using `tokenizers` before the forkifpossible - Explicitly s...
--model_id$model_id--dataset_path$save_dataset_path--epochs 3 --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --generation_max_length$max_target_length--lr 1e-4 --deepspeed configs/ds_flan_t5_z3_config_bf16.json huggingface/tokenizers: The current process just got f...
https:///google/flan-t5-base XL (30 亿参数) 模型: https:///google/flan-t5-xl XXL (110 亿参数) 模型: https:///google/flan-t5-xxl 这意味着我们将学习如何利用模型并行、多 GPU 以及 DeepSpeed ZeRO 来微调 FLAN-T5 XL 和 XXL。
Base (250M 参数) 模型:https://hf.co/google/flan-t5-base XL (30 亿参数) 模型:https://hf.co/google/flan-t5-xl XXL (110 亿参数) 模型:https://hf.co/google/flan-t5-xxl 这意味着我们将学习如何利用模型并行、多 GPU 以及 DeepSpeed ZeRO 来微调 FLAN-T5 XL 和 XXL。
我们将使用philschmid/flan-t5-xxl-sharded-fp16,这是google/flan-t5-xxl的一个分片版本。分片将帮助我们在加载模型时不会耗尽内存。 fromtransformersimportAutoModelForSeq2SeqLM# huggingface hub模型IDmodel_id="philschmid/flan-t5-xxl-sharded-fp16"# 从hub加载模型model=AutoModelForSeq2SeqLM.from_pretrained...
An NVIDIA A100 GPU is being used for this experimentation, and thegoogle/flan-t5-basemodel will strike a balance between computational efficiency and performance compatibility. Model and Tokenizer initialization The following three instructions are required to create the model. ...
Here is a minimal reproducing script using the vocabulary path provided in the t5_1_1_base.gin that is used for all of the Flan T5 (according to github). >>> import seqio >>> vocabulary = seqio.SentencePieceVocabulary("gs://t5-data/vocabs/cc_all.32000.100extra/sentencepiece.model") >...
fromsagemakerimportimage_uris,model_urisfromsagemaker.modelimportModelfromsagemaker.predictorimportPredictorfromsagemaker.sessionimportSession aws_role=Session().get_caller_identity_arn()model_id,model_version="huggingface-text2text-flan-t5-xxl","*"endpoint_name=f"jumpstart-example-{model...