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--- |
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base_model: meta-llama/Meta-Llama-3-8B |
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datasets: |
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- generator |
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library_name: peft |
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license: llama3 |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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model-index: |
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- name: NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the generator dataset. |
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It achieves the following results on the evaluation set: |
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Accuracy (Eval dataset and predict) for a sample of 10: 70.00% |
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## Model description |
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Article: https://medium.com/@frankmorales_91352/fine-tuning-meta-llama-3-8b-with-medal-a-refined-approach-for-enhanced-medical-language-b924d226b09d |
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## Training and evaluation data |
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Article: https://medium.com/@frankmorales_91352/fine-tuning-meta-llama-3-8b-with-medal-a-refined-approach-for-enhanced-medical-language-b924d226b09d |
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Fine-Tuning: https://github.com/frank-morales2020/MLxDL/blob/main/FineTuning_LLM_Meta_Llama_3_8B_for_MEDAL_EVALDATA.ipynb |
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Evaluation: https://github.com/frank-morales2020/MLxDL/blob/main/Meta_Llama_3_8B_for_MEDAL_EVALUATOR_evaldata.ipynb |
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## Training procedure |
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from transformers import EarlyStoppingCallback |
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trainer.add_callback(EarlyStoppingCallback(early_stopping_patience=5)) |
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trainer.train() |
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trainer.save_model() |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 1 |
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from transformers import TrainingArguments |
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args = TrainingArguments( |
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output_dir="/content/gdrive/MyDrive/model/NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata", |
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#num_train_epochs=3, # number of training epochs |
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num_train_epochs=1, # number of training epochs for POC |
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per_device_train_batch_size=2, # batch size per device during training |
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gradient_accumulation_steps=8, # number of steps before performing a backward/update pass |
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gradient_checkpointing=True, # use gradient checkpointing to save memory |
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optim="adamw_torch_fused", # use fused adamw optimizer |
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logging_steps=200, # log every 200 steps |
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learning_rate=2e-4, # learning rate, based on QLoRA paper # i used in the first model |
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bf16=True, # use bfloat16 precision |
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tf32=True, # use tf32 precision |
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max_grad_norm=1.0, # max gradient norm based on QLoRA paper |
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warmup_ratio=0.05, # warmup ratio based on QLoRA paper = 0.03 |
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weight_decay=0.01, |
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lr_scheduler_type="cosine", # lr_scheduler_type="cosine" (Cosine Annealing Learning Rate) |
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push_to_hub=True, # push model to hub |
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report_to="tensorboard", # report metrics to tensorboard |
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gradient_checkpointing_kwargs={"use_reentrant": True}, |
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load_best_model_at_end=True, |
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logging_dir="/content/gdrive/MyDrive/model/NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata/logs", |
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evaluation_strategy="steps", # Evaluate at step intervals |
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eval_steps=200, # Evaluate every 50 steps |
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save_strategy="steps", # Save checkpoints at step intervals |
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save_steps=200, # Save every 50 steps (aligned with eval_steps) |
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metric_for_best_model = "loss", |
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] |
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) |
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### Training results |
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### Step Training Loss Validation Loss |
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## 200 2.505300 2.382469 |
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## 3600 2.226800 2.223289 |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
