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from src import *
# Load configuration
max_seq_length = config.MAX_SEQ_LENGTH
device_map = config.DEVICE_MAP
eos_token = config.EOS_TOKEN
# Load and configure model
model_name = "unsloth/Meta-Llama-3.1-8B"
model, tokenizer = load_model(model_name, max_seq_length, config.DTYPE, config.LOAD_IN_4BIT, device_map)
eos_token = tokenizer.eos_token
model = configure_peft_model(model, target_modules=["q_proj", "down_proj"])
# Prepare dataset
nsamples = 1000
dataset = load_and_prepare_dataset(
"lavita/ChatDoctor-HealthCareMagic-100k",
nsamples,
formatting_prompts_func,
config.ALPACA_PROMPT_TEMPLATE,
eos_token,
)
# Train model
trainer_stats = train_model(
model=model,
tokenizer=tokenizer,
train_dataset=dataset,
dataset_text_field="text",
max_seq_length=max_seq_length,
dataset_num_proc=2,
packing=False,
training_args=config.TRAIN_ARGS,
)
# Save the model
save_model_and_tokenizer(model, tokenizer, "./llama3_medichat")
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