NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the generator dataset. It achieves the following results on the evaluation set:
Accuracy (Eval dataset and predict) for a sample of 10: 70.00%
Model description
Training and evaluation data
Fine-Tuning: https://github.com/frank-morales2020/MLxDL/blob/main/FineTuning_LLM_Meta_Llama_3_8B_for_MEDAL_EVALDATA.ipynb
Evaluation: https://github.com/frank-morales2020/MLxDL/blob/main/Meta_Llama_3_8B_for_MEDAL_EVALUATOR_evaldata.ipynb
Training procedure
from transformers import EarlyStoppingCallback trainer.add_callback(EarlyStoppingCallback(early_stopping_patience=5))
trainer.train()
trainer.save_model()
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
from transformers import TrainingArguments
args = TrainingArguments(
output_dir="/content/gdrive/MyDrive/model/NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata",
#num_train_epochs=3, # number of training epochs
num_train_epochs=1, # number of training epochs for POC
per_device_train_batch_size=2, # batch size per device during training
gradient_accumulation_steps=8, # number of steps before performing a backward/update pass
gradient_checkpointing=True, # use gradient checkpointing to save memory
optim="adamw_torch_fused", # use fused adamw optimizer
logging_steps=200, # log every 200 steps
learning_rate=2e-4, # learning rate, based on QLoRA paper # i used in the first model
bf16=True, # use bfloat16 precision
tf32=True, # use tf32 precision
max_grad_norm=1.0, # max gradient norm based on QLoRA paper
warmup_ratio=0.05, # warmup ratio based on QLoRA paper = 0.03
weight_decay=0.01,
lr_scheduler_type="cosine", # lr_scheduler_type="cosine" (Cosine Annealing Learning Rate)
push_to_hub=True, # push model to hub
report_to="tensorboard", # report metrics to tensorboard
gradient_checkpointing_kwargs={"use_reentrant": True},
load_best_model_at_end=True,
logging_dir="/content/gdrive/MyDrive/model/NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata/logs",
evaluation_strategy="steps", # Evaluate at step intervals
eval_steps=200, # Evaluate every 50 steps
save_strategy="steps", # Save checkpoints at step intervals
save_steps=200, # Save every 50 steps (aligned with eval_steps)
metric_for_best_model = "loss",
]
)
Training results
Step Training Loss Validation Loss
200 2.505300 2.382469
3600 2.226800 2.223289
Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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