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---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-3B
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
model-index:
- name: meta-llama/Llama-3.2-3B
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. -->
# meta-llama/Llama-3.2-3B
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6898
## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use adamw_torch 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: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7972 | 0.0275 | 200 | 0.8336 |
| 0.7579 | 0.0551 | 400 | 0.7996 |
| 0.8037 | 0.0826 | 600 | 0.7918 |
| 0.7333 | 0.1101 | 800 | 0.7879 |
| 0.7871 | 0.1376 | 1000 | 0.7818 |
| 0.8135 | 0.1652 | 1200 | 0.7736 |
| 0.7612 | 0.1927 | 1400 | 0.7699 |
| 0.7421 | 0.2202 | 1600 | 0.7643 |
| 0.7451 | 0.2478 | 1800 | 0.7595 |
| 0.7388 | 0.2753 | 2000 | 0.7556 |
| 0.7707 | 0.3028 | 2200 | 0.7523 |
| 0.7063 | 0.3303 | 2400 | 0.7481 |
| 0.8091 | 0.3579 | 2600 | 0.7440 |
| 0.764 | 0.3854 | 2800 | 0.7407 |
| 0.714 | 0.4129 | 3000 | 0.7370 |
| 0.6745 | 0.4405 | 3200 | 0.7339 |
| 0.6771 | 0.4680 | 3400 | 0.7295 |
| 0.7419 | 0.4955 | 3600 | 0.7257 |
| 0.71 | 0.5230 | 3800 | 0.7223 |
| 0.6362 | 0.5506 | 4000 | 0.7189 |
| 0.7616 | 0.5781 | 4200 | 0.7159 |
| 0.676 | 0.6056 | 4400 | 0.7126 |
| 0.6732 | 0.6332 | 4600 | 0.7094 |
| 0.7017 | 0.6607 | 4800 | 0.7067 |
| 0.6796 | 0.6882 | 5000 | 0.7038 |
| 0.7065 | 0.7157 | 5200 | 0.7012 |
| 0.6318 | 0.7433 | 5400 | 0.6987 |
| 0.639 | 0.7708 | 5600 | 0.6965 |
| 0.7078 | 0.7983 | 5800 | 0.6949 |
| 0.7029 | 0.8258 | 6000 | 0.6933 |
| 0.6977 | 0.8534 | 6200 | 0.6921 |
| 0.6803 | 0.8809 | 6400 | 0.6911 |
| 0.703 | 0.9084 | 6600 | 0.6905 |
| 0.6819 | 0.9360 | 6800 | 0.6901 |
| 0.6327 | 0.9635 | 7000 | 0.6899 |
| 0.6685 | 0.9910 | 7200 | 0.6899 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.21.0
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