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library_name: peft
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
tags:
- base_model:adapter:meta-llama/Meta-Llama-3.1-8B
- lora
- sft
- transformers
- trl
pipeline_tag: text-generation
model-index:
- name: summarization
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/calmm-hanoi-university-of-science/summarization/runs/7kmcsnsu)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/calmm-hanoi-university-of-science/summarization/runs/7kmcsnsu)
# summarization
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 200
### Framework versions
- PEFT 0.17.1
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.3.0
- Tokenizers 0.22.1 |