Spaces:
Running
Running
File size: 1,723 Bytes
09e908d a6cd710 09e908d a6cd710 09e908d d609d8c 09e908d 846211a 09e908d 846211a 09e908d 846211a 09e908d 846211a 09e908d 846211a 09e908d 846211a 09e908d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 |
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from peft import LoraConfig, get_peft_model
from trl import SFTTrainer
from datasets import load_dataset
import torch
# Load model and tokenizer
model_name = "HuggingFaceTB/SmolLM3-3B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
)
# Prepare PEFT config for efficient fine-tuning
peft_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
model = get_peft_model(model, peft_config)
# Load dataset (example: assume 'financial_data.jsonl' with {'text': 'query ||| response'} format)
dataset = load_dataset("json", data_files="financial_data.jsonl", split="train")
# Training arguments
training_args = TrainingArguments(
output_dir="./finetuned_smollm3",
num_train_epochs=3,
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
learning_rate=2e-4,
fp16=True if torch.cuda.is_available() else False,
save_steps=500,
logging_steps=100,
optim="paged_adamw_8bit",
weight_decay=0.01,
warmup_steps=100,
)
# Trainer
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=dataset,
peft_config=peft_config,
dataset_text_field="text", # Adjust based on your dataset
tokenizer=tokenizer,
max_seq_length=512,
)
trainer.train()
# Save fine-tuned model
trainer.model.save_pretrained("./finetuned_smollm3")
tokenizer.save_pretrained("./finetuned_smollm3") |