mirror of
https://github.com/tloen/alpaca-lora.git
synced 2024-10-01 01:05:56 -04:00
98 lines
2.7 KiB
Python
98 lines
2.7 KiB
Python
import os
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# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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import torch
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import torch.nn as nn
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import bitsandbytes as bnb
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from datasets import load_dataset
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import transformers
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from transformers import AutoTokenizer, AutoConfig, LLaMAForCausalLM, LLaMATokenizer
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from peft import prepare_model_for_int8_training, LoraConfig, get_peft_model
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model = LLaMAForCausalLM.from_pretrained(
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"decapoda-research/llama-7b-hf",
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load_in_8bit=True,
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device_map="auto",
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)
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tokenizer = LLaMATokenizer.from_pretrained(
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"decapoda-research/llama-7b-hf", add_eos_token=True
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)
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model = prepare_model_for_int8_training(model)
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config = LoraConfig(
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r=4,
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lora_alpha=16,
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target_modules=["q_proj", "v_proj"],
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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)
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model = get_peft_model(model, config)
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tokenizer.pad_token_id = 0 # unk. we want this to be different from the eos token
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data = load_dataset("json", data_files="alpaca_data.json")
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def generate_prompt(data_point):
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# sorry about the formatting disaster gotta move fast
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if data_point["instruction"]:
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return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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{data_point["instruction"]}
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### Input:
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{data_point["input"]}
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### Response:
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{data_point["output"]}"""
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else:
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return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{data_point["instruction"]}
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### Response:
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{data_point["output"]}"""
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# optimized for RTX 4090. for larger GPUs, increase some of these?
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MICRO_BATCH_SIZE = 4 # this could actually be 5 but i like powers of 2
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BATCH_SIZE = 128
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GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE
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EPOCHS = 3 # from the result
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LEARNING_RATE = 3e-4 # the karpathy constant
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CUTOFF_LEN = 256 # 256 accounts for about 96% of the data
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data = data.shuffle().map(
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lambda data_point: tokenizer(
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generate_prompt(data_point),
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truncation=True,
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max_length=CUTOFF_LEN,
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padding="max_length",
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)
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)
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trainer = transformers.Trainer(
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model=model,
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train_dataset=data["train"],
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args=transformers.TrainingArguments(
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per_device_train_batch_size=MICRO_BATCH_SIZE,
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gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
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warmup_steps=100,
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num_train_epochs=EPOCHS,
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learning_rate=LEARNING_RATE,
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fp16=True,
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logging_steps=1,
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output_dir="lora-alpaca",
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save_total_limit=3,
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),
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data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),
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)
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model.config.use_cache = False
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trainer.train(resume_from_checkpoint=False)
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model.save_pretrained("lora-alpaca")
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