README.md

---
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- generator
library_name: peft
license: llama3
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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](https://huggingface.co/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

Article: https://medium.com/@frankmorales_91352/fine-tuning-meta-llama-3-8b-with-medal-a-refined-approach-for-enhanced-medical-language-b924d226b09d


## Training and evaluation data

Article: https://medium.com/@frankmorales_91352/fine-tuning-meta-llama-3-8b-with-medal-a-refined-approach-for-enhanced-medical-language-b924d226b09d

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