ramlanjekar/molgpt-finetuned-beta-lactams2

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Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Training parameters used

print("\nConfiguring QLoRA...")

LoRA configuration - high rank and alpha for significant adaptation

lora_config = LoraConfig( r=64, # LoRA rank - higher for more expressiveness lora_alpha=128, # LoRA alpha - higher for stronger adaptation target_modules=[ "c_attn", # Attention layers "c_proj", # Projection layers "c_fc" # Feed-forward layers ], lora_dropout=0.1, bias="none", task_type="CAUSAL_LM" )

Apply LoRA to model

model = get_peft_model(model, lora_config) model.print_trainable_parameters()

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4. TRAINING CONFIGURATION

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print("\nSetting up training...")

Data collator for language modeling

data_collator = DataCollatorForLanguageModeling( tokenizer=tokenizer, mlm=False # Causal LM, not masked LM )

Training arguments

training_args = TrainingArguments( output_dir="./molgpt_finetuned_qlora", num_train_epochs=12, per_device_train_batch_size=16, gradient_accumulation_steps=1, # Adjust if OOM learning_rate=2e-4, # Standard for LoRA weight_decay=0.01, logging_steps=10, save_strategy="epoch", save_total_limit=2, fp16=True, # Mixed precision for T4 GPU optim="adamw_torch", warmup_steps=50, lr_scheduler_type="cosine", report_to="none", # Disable wandb/tensorboard remove_unused_columns=False )

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5. TRAIN MODEL

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print("\nStarting training...") trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_dataset, data_collator=data_collator )

Train

trainer.train() print("\nTraining completed!")

Uses

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Recommendations

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How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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