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---
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-3.1-8B-Instruct-EI1-2ep-sft
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. -->
# Llama-3.1-8B-Instruct-EI1-2ep-sft
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co./meta-llama/Llama-3.1-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3970
## 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: 6e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0562 | 100 | 0.5980 |
| No log | 0.1124 | 200 | 0.5609 |
| No log | 0.1685 | 300 | 0.5369 |
| No log | 0.2247 | 400 | 0.5156 |
| 0.5582 | 0.2809 | 500 | 0.4955 |
| 0.5582 | 0.3371 | 600 | 0.4795 |
| 0.5582 | 0.3933 | 700 | 0.4655 |
| 0.5582 | 0.4494 | 800 | 0.4522 |
| 0.5582 | 0.5056 | 900 | 0.4433 |
| 0.448 | 0.5618 | 1000 | 0.4355 |
| 0.448 | 0.6180 | 1100 | 0.4295 |
| 0.448 | 0.6742 | 1200 | 0.4252 |
| 0.448 | 0.7303 | 1300 | 0.4200 |
| 0.448 | 0.7865 | 1400 | 0.4159 |
| 0.4123 | 0.8427 | 1500 | 0.4124 |
| 0.4123 | 0.8989 | 1600 | 0.4098 |
| 0.4123 | 0.9551 | 1700 | 0.4075 |
| 0.4123 | 1.0112 | 1800 | 0.4086 |
| 0.4123 | 1.0674 | 1900 | 0.4075 |
| 0.3815 | 1.1236 | 2000 | 0.4069 |
| 0.3815 | 1.1798 | 2100 | 0.4054 |
| 0.3815 | 1.2360 | 2200 | 0.4043 |
| 0.3815 | 1.2921 | 2300 | 0.4029 |
| 0.3815 | 1.3483 | 2400 | 0.4022 |
| 0.3532 | 1.4045 | 2500 | 0.4012 |
| 0.3532 | 1.4607 | 2600 | 0.4002 |
| 0.3532 | 1.5169 | 2700 | 0.3996 |
| 0.3532 | 1.5730 | 2800 | 0.3986 |
| 0.3532 | 1.6292 | 2900 | 0.3982 |
| 0.35 | 1.6854 | 3000 | 0.3978 |
| 0.35 | 1.7416 | 3100 | 0.3975 |
| 0.35 | 1.7978 | 3200 | 0.3971 |
| 0.35 | 1.8539 | 3300 | 0.3971 |
| 0.35 | 1.9101 | 3400 | 0.3970 |
| 0.3468 | 1.9663 | 3500 | 0.3970 |
### Framework versions
- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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