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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: POC-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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# POC-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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- Loss: 2.2356 |
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## Model description |
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Article: https://medium.com/@frankmorales_91352/sfttrainer-a-comprehensive-exploration-of-its-concept-advantages-limitations-history-and-19ab0926e74e |
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## Training and evaluation data |
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Evaluation: https://github.com/frank-morales2020/MLxDL/blob/main/Meta_Llama_3_8B_for_MEDAL_EVALUATOR_evaldata_NEW_POC.ipynb |
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## Training procedure |
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Fine Tuning: https://github.com/frank-morales2020/MLxDL/blob/main/FineTuning_LLM_Meta_Llama_3_8B_for_MEDAL_EVALDATA_PONEW.ipynb |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 3 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 24 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: constant |
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- lr_scheduler_warmup_ratio: 0.03 |
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- lr_scheduler_warmup_steps: 1500 |
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- num_epochs: 0.5 |
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from transformers import TrainingArguments |
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args = TrainingArguments( |
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output_dir="/content/gdrive/MyDrive/model/POC-NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata", |
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num_train_epochs=0.5, # number of training epochs for POC |
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per_device_train_batch_size=3, #4 # batch size per device during training |
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gradient_accumulation_steps=8, #6 # values like 8, 12, or even 16, # 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=100, # log every 100 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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#learning_rate=1e-5, |
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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=0.03, # warmup ratio based on QLoRA paper = 0.03 |
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weight_decay=0.01, |
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lr_scheduler_type="constant", # use constant learning rate scheduler |
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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/POC-NEW-Meta-Llama-3-8B-MEDAL-flash-attention-2-cosine-evaldata/logs", |
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evaluation_strategy="steps", |
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eval_steps=100, |
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save_strategy="steps", |
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save_steps=100, |
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metric_for_best_model = "loss", |
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warmup_steps=1500, |
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) |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 2.4484 | 0.0207 | 100 | 2.3720 | |
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| 2.3535 | 0.0415 | 200 | 2.3370 | |
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| 2.3303 | 0.0622 | 300 | 2.3204 | |
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| 2.3153 | 0.0830 | 400 | 2.3081 | |
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| 2.3041 | 0.1037 | 500 | 2.2982 | |
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| 2.2904 | 0.1245 | 600 | 2.2917 | |
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| 2.2954 | 0.1452 | 700 | 2.2845 | |
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| 2.2795 | 0.1660 | 800 | 2.2790 | |
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| 2.2772 | 0.1867 | 900 | 2.2751 | |
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| 2.2769 | 0.2075 | 1000 | 2.2711 | |
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| 2.2711 | 0.2282 | 1100 | 2.2678 | |
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| 2.2722 | 0.2489 | 1200 | 2.2644 | |
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| 2.269 | 0.2697 | 1300 | 2.2610 | |
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| 2.2651 | 0.2904 | 1400 | 2.2586 | |
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| 2.2625 | 0.3112 | 1500 | 2.2550 | |
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| 2.2579 | 0.3319 | 1600 | 2.2516 | |
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| 2.2532 | 0.3527 | 1700 | 2.2501 | |
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| 2.256 | 0.3734 | 1800 | 2.2471 | |
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| 2.2509 | 0.3942 | 1900 | 2.2450 | |
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| 2.2482 | 0.4149 | 2000 | 2.2433 | |
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| 2.247 | 0.4357 | 2100 | 2.2406 | |
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| 2.2404 | 0.4564 | 2200 | 2.2395 | |
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| 2.2377 | 0.4771 | 2300 | 2.2372 | |
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| 2.2373 | 0.4979 | 2400 | 2.2356 | |
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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 |
