---
base_model: meta-llama/Meta-Llama-3.1-8B
datasets:
- llama-duo/synth_coding_dataset_dedup
library_name: peft
license: llama3.1
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
model-index:
- name: llama3.1-8b-coding-gpt4o-100k
  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. -->

# llama3.1-8b-coding-gpt4o-100k

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 the llama-duo/synth_coding_dataset_dedup dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3444

## 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: 4
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.741         | 1.0   | 525  | 1.3567          |
| 0.7109        | 2.0   | 1050 | 1.3158          |
| 0.7026        | 3.0   | 1575 | 1.3116          |
| 0.6682        | 4.0   | 2100 | 1.3090          |
| 0.6825        | 5.0   | 2625 | 1.3126          |
| 0.6429        | 6.0   | 3150 | 1.3228          |
| 0.6334        | 7.0   | 3675 | 1.3276          |
| 0.6257        | 8.0   | 4200 | 1.3404          |
| 0.6314        | 9.0   | 4725 | 1.3410          |
| 0.6205        | 10.0  | 5250 | 1.3444          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1