diff options
| -rw-r--r-- | bot.py | 6 | ||||
| -rw-r--r-- | train.py | 13 |
2 files changed, 9 insertions, 10 deletions
@@ -18,8 +18,8 @@ parser.add_argument('-m', '--model', default='model', args = parser.parse_args() -tokenizer = AutoTokenizer.from_pretrained('gpt2-large') -model = AutoModelForCausalLM.from_pretrained(args.model).to('cuda') +tokenizer = AutoTokenizer.from_pretrained('gpt2-medium') +model = AutoModelForCausalLM.from_pretrained(args.model, low_cpu_mem_usage=True).to('cuda') if args.input is None: @@ -74,7 +74,7 @@ if args.input is None: print(args.input) inputs = tokenizer.encode(args.input, return_tensors='pt').to('cuda') output = tokenizer.decode(model.generate( - inputs, do_sample=True, max_length=150, top_p=0.9)[0]) + inputs, max_length=150, do_sample=True, top_p=0.9)[0]) print(output) @@ -1,7 +1,6 @@ from argparse import ArgumentParser from itertools import chain -from torch import float16 from datasets import load_dataset from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments, default_data_collator @@ -22,7 +21,7 @@ tokenized_dataset = raw_dataset.map(lambda examples: tokenizer(examples['text']) # Generate chunks of block_size -block_size = 256 # tokenizer.model_max_length +block_size = tokenizer.model_max_length # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size. def group_texts(examples): @@ -38,16 +37,16 @@ def group_texts(examples): k: [t[i : i + block_size] for i in range(0, total_length, block_size)] for k, t in concatenated_examples.items() } - result["labels"] = result["input_ids"].copy() + result['labels'] = result['input_ids'].copy() return result lm_dataset = tokenized_dataset.map(group_texts, batched=True) # Create and train the model -model = AutoModelForCausalLM.from_pretrained('gpt2-large', - torch_dtype=float16, low_cpu_mem_usage=True).to('cuda') -trainer = Trainer(model, TrainingArguments(output_dir=args.output, per_device_train_batch_size=1), - default_data_collator, lm_dataset['train']) +model = AutoModelForCausalLM.from_pretrained('gpt2-large', low_cpu_mem_usage=True).to('cuda') +trainer = Trainer(model, TrainingArguments(output_dir=args.output, save_strategy='no', + per_device_train_batch_size=1, gradient_checkpointing=True, optim='adafactor'), + default_data_collator, lm_dataset['train']) trainer.train() trainer.save_model() |