Model Card for Model ID
model = FastLanguageModel.get_peft_model(
model,
r = 32, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 32,
lora_dropout = 0.1, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 3407,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
)
chat_template = """
Input:
Generate a sentence that includes a word at the CEFR level {INPUT}. The sentence should be meaningful, contextually appropriate, and clearly demonstrate the usage of the word at the specified CEFR level. Ensure that the sentence is suitable for learners at this level, providing clear context for the word meaning.
Make the output format to be like this
The word is: sleep and it is a verb. Example sentence: I sleep early.
The word is: reciprocate and it is a verb. Example sentence: He reciprocated her kindness warmly.
Response:
{OUTPUT}
Input:
Generate a sentence that includes a word at the CEFR level {INPUT}. The sentence should be meaningful, contextually appropriate, and clearly demonstrate the usage of the word at the specified CEFR level. Ensure that the sentence is suitable for learners at this level, providing clear context for the word meaning.
Make the output format to be like this
The word is: sleep and it is a verb. Example sentence: I sleep early.
The word is: reciprocate and it is a verb. Example sentence: He reciprocated her kindness warmly.
Response:
{OUTPUT}
"""
from trl import SFTTrainer
from transformers import TrainingArguments
from unsloth import is_bfloat16_supported
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
train_dataset = dataset,
dataset_text_field = "text",
max_seq_length = max_seq_length,
dataset_num_proc = 2,
packing = False, # Can make training 5x faster for short sequences.
args = TrainingArguments(
per_device_train_batch_size = 1,
gradient_accumulation_steps = 4,
warmup_steps = 5,
# max_steps = 120,
num_train_epochs = 1, # Set this instead of max_steps for full training runs
learning_rate = 2e-4,
fp16 = not is_bfloat16_supported(),
bf16 = is_bfloat16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407,
output_dir = "outputs",
),
)
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