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

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# POC-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:
- Loss: 2.2356

## Model description

Article: https://medium.com/@frankmorales_91352/sfttrainer-a-comprehensive-exploration-of-its-concept-advantages-limitations-history-and-19ab0926e74e

## Training and evaluation data

Evaluation: https://github.com/frank-morales2020/MLxDL/blob/main/Meta_Llama_3_8B_for_MEDAL_EVALUATOR_evaldata_NEW_POC.ipynb

## Training procedure

Fine Tuning: https://github.com/frank-morales2020/MLxDL/blob/main/FineTuning_LLM_Meta_Llama_3_8B_for_MEDAL_EVALDATA_PONEW.ipynb


### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- lr_scheduler_warmup_steps: 1500
- num_epochs: 0.5

from transformers import TrainingArguments

args = TrainingArguments(

    output_dir="/content/gdrive/MyDrive/model/POC-NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata",

    num_train_epochs=0.5,                   # number of training epochs for POC
    per_device_train_batch_size=3,    #4    # batch size per device during training
    gradient_accumulation_steps=8,    #6    # values like 8, 12, or even 16,    # 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=100,                      # log every 100 steps
    learning_rate=2e-4,                     # learning rate, based on QLoRA paper # i used in the first model
    #learning_rate=1e-5,
    bf16=True,                              # use bfloat16 precision
    tf32=True,                              # use tf32 precision
    max_grad_norm=1.0,                      # max gradient norm based on QLoRA paper
    warmup_ratio=0.03,                      # warmup ratio based on QLoRA paper = 0.03

    weight_decay=0.01,
    lr_scheduler_type="constant",           # use constant learning rate scheduler
    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/POC-NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata/logs",

    evaluation_strategy="steps",
    eval_steps=100,
    save_strategy="steps",
    save_steps=100,
    metric_for_best_model = "loss",
    warmup_steps=1500,
 )

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.4484        | 0.0207 | 100  | 2.3720          |
| 2.3535        | 0.0415 | 200  | 2.3370          |
| 2.3303        | 0.0622 | 300  | 2.3204          |
| 2.3153        | 0.0830 | 400  | 2.3081          |
| 2.3041        | 0.1037 | 500  | 2.2982          |
| 2.2904        | 0.1245 | 600  | 2.2917          |
| 2.2954        | 0.1452 | 700  | 2.2845          |
| 2.2795        | 0.1660 | 800  | 2.2790          |
| 2.2772        | 0.1867 | 900  | 2.2751          |
| 2.2769        | 0.2075 | 1000 | 2.2711          |
| 2.2711        | 0.2282 | 1100 | 2.2678          |
| 2.2722        | 0.2489 | 1200 | 2.2644          |
| 2.269         | 0.2697 | 1300 | 2.2610          |
| 2.2651        | 0.2904 | 1400 | 2.2586          |
| 2.2625        | 0.3112 | 1500 | 2.2550          |
| 2.2579        | 0.3319 | 1600 | 2.2516          |
| 2.2532        | 0.3527 | 1700 | 2.2501          |
| 2.256         | 0.3734 | 1800 | 2.2471          |
| 2.2509        | 0.3942 | 1900 | 2.2450          |
| 2.2482        | 0.4149 | 2000 | 2.2433          |
| 2.247         | 0.4357 | 2100 | 2.2406          |
| 2.2404        | 0.4564 | 2200 | 2.2395          |
| 2.2377        | 0.4771 | 2300 | 2.2372          |
| 2.2373        | 0.4979 | 2400 | 2.2356          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1