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---
library_name: transformers
license: other
base_model: instruction-pretrain/finance-Llama3-8B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: pretrain_sft_finance
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. -->
# pretrain_sft_finance
This model is a fine-tuned version of [instruction-pretrain/finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B) on the time_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2738
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.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: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.1092 | 0.0649 | 10 | 4.4912 |
| 2.1524 | 0.1299 | 20 | 1.8664 |
| 0.7796 | 0.1948 | 30 | 0.6781 |
| 0.3127 | 0.2597 | 40 | 0.2871 |
| 0.4223 | 0.3247 | 50 | 0.2762 |
| 0.2854 | 0.3896 | 60 | 0.2877 |
| 0.2908 | 0.4545 | 70 | 0.3328 |
| 0.4468 | 0.5195 | 80 | 0.3878 |
| 0.2962 | 0.5844 | 90 | 0.2747 |
| 0.2759 | 0.6494 | 100 | 0.2835 |
| 0.3065 | 0.7143 | 110 | 0.2901 |
| 0.2882 | 0.7792 | 120 | 0.2735 |
| 0.2945 | 0.8442 | 130 | 0.2920 |
| 0.2805 | 0.9091 | 140 | 0.2734 |
| 0.2696 | 0.9740 | 150 | 0.2738 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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