train_unsloth_7b.py

from unsloth import FastLlamaModel
import torch
from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
from transformers import TrainingArguments
from datasets import load_from_disk
import math
import wandb
import os


max_seq_length = 2048 # Can change to whatever number <= 4096
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.


revisions = [("250k", "8ee454fe392a0267c3dee21323b5cac233d67441"), 
             ("500k", "12d3eec2d02533226c9cff719d4278967574ffcd"), ("750k", "845b8c6d8499c0e8fea0b8e5480d72e700385820"), ("1000k", "53669200ad7a6a6f1ac6a73e54c9e54c1d834a17")]



#for revision in revisions:
model, tokenizer = FastLlamaModel.from_pretrained(
    model_name = "Finnish-NLP/llama-7b-finnish",
    max_seq_length = max_seq_length,
    dtype = dtype,
    load_in_4bit = load_in_4bit,
    revision='53669200ad7a6a6f1ac6a73e54c9e54c1d834a17'    
)

tokenizer.clean_up_tokenization_spaces=True
tokenizer.add_tokens(["<|alku|>", "<PAD>", "<|ihminen|>", "<|avustaja|>"])
tokenizer.pad_token = "<PAD>"
tokenizer.add_special_tokens({'eos_token': '<|loppu|>'})
tokenizer.add_tokens('\n', special_tokens=True)
tokenizer.add_eos_token=True
model.resize_token_embeddings(new_num_tokens=len(tokenizer))
model.config.eos_token_id = tokenizer.eos_token_id
print(model.config.eos_token_id)
assert tokenizer.pad_token_id != tokenizer.eos_token_id
print(tokenizer.padding_side)
print(tokenizer.add_bos_token)
print(model)



model = FastLlamaModel.get_peft_model(
    model,
    r = 32,
    target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                    "gate_proj", "up_proj", "down_proj"],
    lora_alpha = 32,
    lora_dropout = 0
    bias = "none"
    use_gradient_checkpointing = True,
    modules_to_save = ["lm_head", "embed_tokens"],
    random_state = 3407,
    max_seq_length = max_seq_length,
    use_rslora=True
)


dataset = load_from_disk("deepl_kaannetyt_combined")
dataset = dataset.train_test_split(test_size=0.02)


bs = 2
ga = 4
epochs = 3
train_steps =   math.ceil(len(dataset["train"]) / bs / ga * epochs)
print(train_steps)
eval_steps = math.ceil(train_steps/10)
print(eval_steps)



try:
    wandb.finish()
except Exception as e:
    wandb.init()

response_template = "\n<|avustaja|> Vastauksesi:"
response_template_ids = tokenizer.encode(response_template, add_special_tokens=False)


collator = DataCollatorForCompletionOnlyLM(response_template_ids, tokenizer=tokenizer, mlm=False)

trainer = SFTTrainer(
    model = model,
    train_dataset = dataset["train"],
    eval_dataset = dataset["test"],
    dataset_text_field = "text",
    data_collator=collator,
    max_seq_length = max_seq_length,
    tokenizer=tokenizer,
    args = TrainingArguments(
        per_device_train_batch_size = 2,
        per_device_eval_batch_size = 2,
        gradient_accumulation_steps = 4,
        warmup_steps = 50,
        max_steps = train_steps,
        report_to="wandb",
        eval_steps=eval_steps,
        evaluation_strategy="steps",
        save_strategy='steps',
        learning_rate = 2e-5,
        fp16 = not torch.cuda.is_bf16_supported(),
        bf16 = torch.cuda.is_bf16_supported(),
        logging_steps = 5,
        optim = "adamw_8bit",
        weight_decay = 0.001,
        lr_scheduler_type = "cosine",
        seed = 3407,
        output_dir = f"llama7b-finniish-instruct-v0.1",
    ),
)

wandb.login()

trainer.train()