---
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- meng-lab/Llama-3.1-8B-Instruct-xsum
model-index:
- name: Llama-3.1-8B-Instruct-sft-5e-3-epoch-100-xsum
  results: []
---

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[<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/uva-llm/huggingface/runs/0cft2k89)
# Llama-3.1-8B-Instruct-sft-5e-3-epoch-100-xsum

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 the meng-lab/Llama-3.1-8B-Instruct-xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 6.7117
- Loss Layer 4 Head: 1.7377
- Loss Layer 8 Head: 1.4957
- Loss Layer 12 Head: 1.4384
- Loss Layer 16 Head: 0.9421
- Loss Layer 20 Head: 0.5804
- Loss Layer 24 Head: 0.3724
- Loss Layer 28 Head: 0.1958

## 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: 0.005
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- 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: 100

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Loss Layer 4 Head | Loss Layer 8 Head | Loss Layer 12 Head | Loss Layer 16 Head | Loss Layer 20 Head | Loss Layer 24 Head | Loss Layer 28 Head |
|:-------------:|:-------:|:----:|:---------------:|:-----------------:|:-----------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|
| 9.417         | 9.5522  | 200  | 10.6034         | 2.1800            | 2.1484            | 1.8370             | 1.5560             | 0.8850             | 0.7908             | 1.1904             |
| 7.0666        | 19.1045 | 400  | 8.3242          | 2.0259            | 1.8363            | 1.7901             | 1.0876             | 0.8469             | 0.4822             | 0.2917             |
| 6.5999        | 28.6567 | 600  | 7.8689          | 1.9122            | 1.7362            | 1.7044             | 1.0472             | 0.6722             | 0.4620             | 0.3698             |
| 5.8586        | 38.2090 | 800  | 7.5812          | 2.0916            | 1.5734            | 1.6211             | 1.0056             | 0.6192             | 0.4660             | 0.2400             |
| 5.4725        | 47.7612 | 1000 | 7.0153          | 1.8457            | 1.5162            | 1.4691             | 0.9794             | 0.6236             | 0.3980             | 0.2260             |
| 5.3026        | 57.3134 | 1200 | 7.0204          | 1.9164            | 1.5058            | 1.5172             | 0.9522             | 0.5897             | 0.3804             | 0.2035             |
| 4.9989        | 66.8657 | 1400 | 6.7446          | 1.7458            | 1.5005            | 1.4430             | 0.9468             | 0.5843             | 0.3757             | 0.1990             |
| 4.9163        | 76.4179 | 1600 | 6.7228          | 1.7406            | 1.4972            | 1.4401             | 0.9436             | 0.5816             | 0.3734             | 0.1968             |
| 4.9194        | 85.9701 | 1800 | 6.7132          | 1.7381            | 1.4960            | 1.4385             | 0.9424             | 0.5807             | 0.3726             | 0.1959             |
| 4.9063        | 95.5224 | 2000 | 6.7117          | 1.7377            | 1.4957            | 1.4384             | 0.9421             | 0.5804             | 0.3724             | 0.1958             |


### Framework versions

- Transformers 4.43.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1