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# /// script
# dependencies = ["trl>=0.12.0", "peft>=0.7.0", "trackio", "transformers>=4.40.0", "datasets>=2.18.0", "accelerate>=0.28.0"]
# ///
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTTrainer, SFTConfig
import trackio
print("=" * 80)
print("TEST RUN: Biomedical Llama Fine-Tuning (100 examples)")
print("=" * 80)
print("\n[1/4] Loading dataset...")
dataset = load_dataset("panikos/biomedical-llama-training")
# Use first 100 examples for test
train_dataset = dataset["train"].select(range(100))
eval_dataset = dataset["validation"].select(range(20))
print(f" Train: {len(train_dataset)} examples")
print(f" Eval: {len(eval_dataset)} examples")
print("\n[2/4] Configuring LoRA...")
lora_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
print(" LoRA rank: 16, alpha: 32")
print("\n[3/4] Initializing trainer...")
trainer = SFTTrainer(
model="meta-llama/Llama-3.1-8B-Instruct",
train_dataset=train_dataset,
eval_dataset=eval_dataset,
peft_config=lora_config,
args=SFTConfig(
output_dir="llama-biomedical-test",
num_train_epochs=1,
per_device_train_batch_size=1, # REDUCED from 2 to 1
gradient_accumulation_steps=8, # INCREASED from 4 to 8
learning_rate=2e-4,
lr_scheduler_type="cosine",
warmup_ratio=0.1,
logging_steps=5,
eval_strategy="steps",
eval_steps=20,
save_strategy="epoch",
push_to_hub=True,
hub_model_id="panikos/llama-biomedical-test",
hub_private_repo=True,
bf16=True,
gradient_checkpointing=False, # DISABLED for stability
report_to="trackio",
project="biomedical-llama-training",
run_name="test-run-100-examples-v3"
)
)
print("\n[4/4] Starting training...")
print(" Model: meta-llama/Llama-3.1-8B-Instruct")
print(" Method: SFT with LoRA")
print(" Epochs: 1")
print(" Batch size: 1 x 8 = 8 (effective) - optimized for memory")
print(" Gradient checkpointing: DISABLED")
print()
trainer.train()
print("\n" + "=" * 80)
print("Pushing model to Hub...")
print("=" * 80)
trainer.push_to_hub()
print("\n" + "=" * 80)
print("TEST COMPLETE!")
print("=" * 80)
print("\nModel: https://huggingface.co/panikos/llama-biomedical-test")
print("Dashboard: https://panikos-trackio.hf.space/")
print("Ready for full production training!")
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